From 830b614bf5cfa0a0bc09fa275e967e5f328edbdc Mon Sep 17 00:00:00 2001 From: Vincent Van Schaik <193274586+VincentSchaik@users.noreply.github.com> Date: Sun, 2 Nov 2025 13:35:34 -0500 Subject: [PATCH 01/16] Delete 02_activities/assignments/assignment_1.ipynb --- 02_activities/assignments/assignment_1.ipynb | 1021 ------------------ 1 file changed, 1021 deletions(-) delete mode 100644 02_activities/assignments/assignment_1.ipynb diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb deleted file mode 100644 index 526e7a558..000000000 --- a/02_activities/assignments/assignment_1.ipynb +++ /dev/null @@ -1,1021 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "927ae8f4", - "metadata": { - "id": "927ae8f4" - }, - "source": [ - "# Assignment 1 - Building a Vision Model with Keras\n", - "\n", - "In this assignment, you will build a simple vision model using Keras. The goal is to classify images from the Fashion MNIST dataset, which contains images of clothing items.\n", - "\n", - "You will:\n", - "1. Load and inspect the Fashion MNIST dataset.\n", - "2. Run a simple baseline model to establish a performance benchmark.\n", - "3. Build and evaluate a simple CNN model, choosing appropriate loss and metrics.\n", - "4. Design and run controlled experiments on one hyperparameter (e.g., number of filters, kernel size, etc.) and one regularization technique (e.g., dropout, L2 regularization).\n", - "5. Analyze the results and visualize the model's performance.\n", - "\n", - "# 1. Loading and Inspecting the Dataset\n", - "\n", - "Fashion MNIST is a dataset of grayscale images of clothing items, with 10 classes. Each image is 28x28 pixels, like the MNIST dataset of handwritten digits. Keras provides a convenient way to load this dataset.\n", - "\n", - "In this section, you should:\n", - "\n", - "- [ ] Inspect the shapes of the training and test sets to confirm their size and structure.\n", - "- [ ] Convert the labels to one-hot encoded format if necessary. (There is a utility function in Keras for this.)\n", - "- [ ] Visualize a few images from the dataset to understand what the data looks like." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "420c7178", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "420c7178", - "outputId": "b272c7ef-0411-486f-fcd5-30bebe71eee0" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n", - "\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n", - "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n", - "\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n", - "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n", - "\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1us/step\n", - "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n", - "\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n" - ] - } - ], - "source": [ - "from tensorflow.keras.datasets import fashion_mnist\n", - "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n", - "\n", - "# Normalize the pixel values to be between 0 and 1\n", - "X_train = X_train.astype('float32') / 255.0\n", - "X_test = X_test.astype('float32') / 255.0\n", - "\n", - "# Classes in the Fashion MNIST dataset\n", - "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "a6c89fe7", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 374 - }, - "id": "a6c89fe7", - "outputId": "9887e91d-36ff-43e1-cae3-005d868d736c" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "X_train shape: (60000, 28, 28)\n", - "y_train shape: (60000,)\n", - "X_test shape: (10000, 28, 28)\n", - "y_test shape: (10000,)\n", - "y_train_one_hot shape: (60000, 10)\n", - "y_test_one_hot shape: (10000, 10)\n", - "\n", - "First 5 one-hot encoded training labels:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n", - " [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n", - " [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])" - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "First 5 one-hot encoded test labels:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n", - " [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.]])" - ] - }, - "metadata": {} - } - ], - "source": [ - "import numpy as np\n", - "# Inspect the shapes of the datasets\n", - "print(\"X_train shape:\", X_train.shape)\n", - "print(\"y_train shape:\", y_train.shape)\n", - "print(\"X_test shape:\", X_test.shape)\n", - "print(\"y_test shape:\", y_test.shape)\n", - "\n", - "# One-hot encoding on a given array of class labels.\n", - "def one_hot(n_classes, y):\n", - " return np.eye(n_classes)[y]\n", - "\n", - "num_classes = len(class_names)\n", - "\n", - "y_train_one_hot = one_hot(num_classes, y=y_train)\n", - "y_test_one_hot = one_hot(num_classes, y=y_test)\n", - "\n", - "print(\"y_train_one_hot shape:\", y_train_one_hot.shape)\n", - "print(\"y_test_one_hot shape:\", y_test_one_hot.shape)\n", - "\n", - "# Print a few examples of the one-hot encoded labels\n", - "print(\"\\nFirst 5 one-hot encoded training labels:\")\n", - "display(y_train_one_hot[:5])\n", - "print(\"\\nFirst 5 one-hot encoded test labels:\")\n", - "display(y_test_one_hot[:5])" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 607 - }, - "id": "da802b1d", - "outputId": "25c82318-7632-476d-9346-f7b2286109ba" - }, - "source": [ - "# Selecting 9 random indices\n", - "random_indices = np.random.choice(len(X_train), 9, replace=False)\n", - "\n", - "# Creating a 3x3 grid plot\n", - "fig, axes = plt.subplots(3, 3, figsize=(6, 6))\n", - "\n", - "for i, ax in enumerate(axes.flat):\n", - " ax.imshow(X_train[random_indices[i]], cmap='gray')\n", - " ax.set_title(f\"Label: {class_names[y_train[random_indices[i]]]}\")\n", - "\n", - " # Removing axis labels\n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "id": "da802b1d", - "execution_count": 11, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "13e100db", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 488 - }, - "id": "13e100db", - "outputId": "62c8ca06-c223-466d-f5ba-5d16ca183cba" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAGzCAYAAAAPLj87AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAOwtJREFUeJzt3Xl4VdW9//HPSSATJMEQMmFIQBBEJi9IRAFBIhEtFsWK2luQWmw1tEKuE1WIiNcoVsutN5XaFrEKalXQOhSrSLBWkMpQ5bmCEkHCkDCZBAIZSNbvD36cesxA9k7C2Zv9fj3Pfh6yz/qetc7mwDfftYflM8YYAQAAxwoJ9gAAAEDTSNYAADgcyRoAAIcjWQMA4HAkawAAHI5kDQCAw5GsAQBwOJI1AAAOR7IGAMDhSNZwhR07dsjn8+lXv/pVq71nQUGBfD6fCgoKWu09W0N6erq+973vnbKdz+fTAw880Gr9+nw+TZ8+vdXeD0DrIVmjzSxevFg+n0+ffPJJsIfS5q6//nr5fD7dc889wR6K6+zZs0cPPPCANm3aFOyhAI5FsgZaqLy8XG+88YbS09P1wgsviMftW7Nnzx7NnTuXZA00gWQNtNCrr76q2tpaLVq0SEVFRfrggw+CPSQAZxiSNYKqurpac+bM0eDBgxUbG6sOHTpoxIgRWrVqVaMxv/71r5WWlqbIyEhdeuml2rx5c702W7Zs0XXXXae4uDhFRERoyJAh+stf/nLK8Rw9elRbtmzRgQMHmv0ZlixZossvv1yjR4/WeeedpyVLltRrc/KUwD/+8Q/l5OSoS5cu6tChg6655hrt37//lH08++yzateune66664m2+3evVs//vGPlZiYqPDwcJ1//vlatGhRsz/Lyc/Tu3dvRUREaPDgwQ3+8rFx40aNGzdOMTEx6tixo8aMGaO1a9fWa/fVV1/pBz/4geLi4hQVFaWLLrpIb731lv/1goICXXjhhZKkqVOnyufzyefzafHixZbGDJzxDNBGnnnmGSPJ/POf/2y0zf79+01ycrLJyckxTz31lJk/f77p3bu3ad++vdm4caO/3fbt240k079/f5Oenm4effRRM3fuXBMXF2e6dOliiouL/W03b95sYmNjTd++fc2jjz5q/vd//9eMHDnS+Hw+s2zZMn+7VatWGUlm1apV9fbl5uY26zPu3r3bhISEmOeee84YY8yDDz5ozjrrLFNVVdXgsbjgggvMZZddZp588knzX//1XyY0NNRcf/31AW3T0tLMVVdd5f/5d7/7nfH5fOa+++4LaPfdcRYXF5uzzz7bpKammgcffNA89dRT5uqrrzaSzK9//etTfhZJpl+/fiY+Pt48+OCD5tFHHzVpaWkmMjLSfPbZZ/52mzdvNh06dDDJyclm3rx55pFHHjHdu3c34eHhZu3atQHjSUxMNNHR0ea+++4zTzzxhBk4cKAJCQnx/z0UFxebBx980Egyt956q3nuuefMc889ZwoLC085XsBLSNZoM81J1sePH6+X2L755huTmJhofvzjH/v3nUzWkZGRZteuXf79H3/8sZFkZs6c6d83ZswY079/f1NZWenfV1dXZy6++GLTq1cv/77WSNa/+tWvTGRkpCkvLzfGGPPFF18YSWb58uUNHovMzExTV1fn3z9z5kwTGhpqSktL/fu+naz/53/+x/h8PjNv3rx6fX93nLfccotJTk42Bw4cCGh3ww03mNjYWHP06NEmP4skI8l88skn/n1ff/21iYiIMNdcc41/34QJE0xYWFhAQt2zZ4+Jjo42I0eO9O+bMWOGkWT+/ve/+/cdPnzYdO/e3aSnp5va2lpjjDH//Oc/jSTzzDPPNDk+wMuYBkdQhYaGKiwsTJJUV1enQ4cO6fjx4xoyZIg2bNhQr/2ECRPUtWtX/89Dhw5VRkaG3n77bUnSoUOH9P777+v666/X4cOHdeDAAR04cEAHDx5UVlaWvvzyS+3evbvR8YwaNUrGmGbfErVkyRJdddVVio6OliT16tVLgwcPbnAqXJJuvfVW+Xw+/88jRoxQbW2tvv7663pt58+frzvuuEOPPvqo7r///ibHYYzRq6++qvHjx8sY4//cBw4cUFZWlsrKyho8nt81bNgwDR482P9zt27d9P3vf1/vvPOOamtrVVtbq7/97W+aMGGCevTo4W+XnJysm266SR9++KHKy8slSW+//baGDh2q4cOH+9t17NhRt956q3bs2KH/+7//O+V4AJzQLtgDAJ599lk9/vjj2rJli2pqavz7u3fvXq9tr1696u0799xz9ec//1mStG3bNhljNHv2bM2ePbvB/vbt2xeQ8O36/PPPtXHjRk2ePFnbtm3z7x81apTy8/NVXl6umJiYgJhu3boF/HzWWWdJkr755puA/atXr9Zbb72le+6555TnqSVp//79Ki0t1dNPP62nn366wTb79u075fs0dnyPHj3qP7d+9OhR9e7du1678847T3V1dSoqKtL555+vr7/+WhkZGQ22k6Svv/5a/fr1O+WYAJCsEWTPP/+8br75Zk2YMEF33XWXEhISFBoaqry8PBUWFlp+v7q6OknSnXfeqaysrAbb9OzZs0VjPun555+XJM2cOVMzZ86s9/qrr76qqVOnBuwLDQ1t8L3Md273Ov/881VaWqrnnntOP/3pTxv8xeXbTn7u//zP/9SUKVMabDNgwIAm3wOAc5GsEVSvvPKKevTooWXLlgVMD+fm5jbY/ssvv6y374svvlB6erok+adm27dvr8zMzNYf8P9njNHSpUs1evRo3X777fVenzdvnpYsWVIvWTdXfHy8XnnlFQ0fPlxjxozRhx9+qJSUlEbbd+nSRdHR0aqtrW3R527s+EZFRalLly6SpKioKG3durVeuy1btigkJESpqamSpLS0tEbbnXxdUsDfO4CGcc4aQXWy0vx2Zfnxxx9rzZo1DbZ/7bXXAs45r1u3Th9//LHGjRsnSUpISNCoUaP0u9/9Tnv37q0Xf6rbpJp769Y//vEP7dixQ1OnTtV1111Xb5s0aZJWrVqlPXv2NPk+TTn77LP13nvv6dixY7r88st18ODBRtuGhoZq4sSJevXVVxu8la05t4dJ0po1awLObRcVFen111/X2LFjFRoaqtDQUI0dO1avv/66duzY4W9XUlKipUuXavjw4f6p/yuvvFLr1q0L+LusqKjQ008/rfT0dPXt21eS1KFDB0lSaWlps8YIeBGVNdrcokWLtGLFinr777jjDn3ve9/TsmXLdM011+iqq67S9u3btXDhQvXt21dHjhypF9OzZ08NHz5ct912m6qqqrRgwQJ17txZd999t79Nfn6+hg8frv79+2vatGnq0aOHSkpKtGbNGu3atUv/+te/Gh3runXrNHr0aOXm5jZ5kdmSJUsUGhqqq666qsHXr776at1333168cUXlZOT08TRaVrPnj31t7/9TaNGjVJWVpbef//9eufBT3rkkUe0atUqZWRkaNq0aerbt68OHTqkDRs26L333tOhQ4dO2V+/fv2UlZWlX/ziFwoPD9dvf/tbSdLcuXP9bR566CG9++67Gj58uG6//Xa1a9dOv/vd71RVVaX58+f7291777164YUXNG7cOP3iF79QXFycnn32WW3fvl2vvvqqQkJO1ArnnHOOOnXqpIULFyo6OlodOnRQRkbGKaf+AU8J4pXoOMOdvF2psa2oqMjU1dWZhx9+2KSlpZnw8HBzwQUXmDfffNNMmTLFpKWl+d/r5K1bjz32mHn88cdNamqqCQ8PNyNGjDD/+te/6vVdWFhoJk+ebJKSkkz79u1N165dzfe+9z3zyiuv+NvYvXWrurradO7c2YwYMaLJz9+9e3dzwQUXBByL797G1tAYvnuftTEnblE7eWvUyVuwGhpnSUmJyc7ONqmpqaZ9+/YmKSnJjBkzxjz99NNNjvXk+2VnZ5vnn3/e9OrVy//38e2xnbRhwwaTlZVlOnbsaKKioszo0aPNRx99VK9dYWGhue6660ynTp1MRESEGTp0qHnzzTfrtXv99ddN3759Tbt27biNC2iAzxgeZAwAgJNxzhoAAIcjWQMA4HAkawAAHI5kDQCABR988IHGjx+vlJQU+Xw+vfbaa6eMKSgo0H/8x38oPDxcPXv2tLyyHMkaAAALKioqNHDgQOXn5zer/fbt23XVVVdp9OjR2rRpk2bMmKGf/OQneuedd5rdJ1eDAwBgk8/n0/LlyzVhwoRG29xzzz166623Ah5YdMMNN6i0tLTBZ1A0xHEPRamrq9OePXsUHR3NYwgBwIWMMTp8+LBSUlL8D79pC5WVlaqurm7x+xhj6uWb8PBwhYeHt/i9pRNPBvzuY4CzsrI0Y8aMZr+H45L1nj17/M8WBgC4V1FRkc4+++w2ee/Kykp1795dxcXFLX6vjh071nti4qmeYmhFcXGxEhMTA/YlJiaqvLxcx44dU2Rk5Cnfw3HJ+uS6wAAAd2vL/8+rq6tVXFysnTt3NvoI3uYoLy9Xt27dVFRUFPA+rVVVt5Y2S9b5+fl67LHHVFxcrIEDB+rJJ5/U0KFDTxnH1DcAnBlOx//nMTExLUrWrf0+DUlKSlJJSUnAvpKSEsXExDSrqpba6Grwl156STk5OcrNzdWGDRs0cOBAZWVlad++fW3RHQDAo4wxLd7a2rBhw7Ry5cqAfe+++66GDRvW/DdpiweODx061GRnZ/t/rq2tNSkpKSYvL++UsWVlZU0u/sDGxsbG5o6trKysLVJMQK44dOiQOX78uO3t0KFDlsd6+PBhs3HjRrNx40YjyTzxxBNm48aN5uuvvzbGGHPvvfeaH/3oR/72X331lYmKijJ33XWX+fzzz01+fr4JDQ01K1asaHafrZ6sq6qqTGhoqFm+fHnA/smTJ5urr766XvvKykpTVlbm34qKioL+BWNjY2Nja/l2OpL1wYMHTU1Nje3t4MGDlsd6crW8725TpkwxxhgzZcoUc+mll9aLGTRokAkLCzM9evSwvLJcq5+zPnDggGpraxu88m3Lli312ufl5QWslQsAgJONGjWqyenzhp5ONmrUKG3cuNF2n0F/gtmsWbNUVlbm34qKioI9JACASxgXnLNuDa1eWcfHxys0NLTBK9+SkpLqtW/NG88BAN7S0oTrlmTd6pV1WFiYBg8eHHDlW11dnVauXGntyjcAACCpje6zzsnJ0ZQpUzRkyBANHTpUCxYsUEVFhaZOndoW3QEAPMorlXWbJOtJkyZp//79mjNnjoqLizVo0CCtWLGi3kVnAAC0hFeSteNW3SovL1dsbGywhwEAaKGysrI2eyrYyVxx8klgLXmfxMTENh1ra3Dcs8EBAGgur1TWJGsAgGt5JVkH/T5rAADQNCprAIBreaWyJlkDAFyLZA0AgMN5JVlzzhoAAIejsgYAuJZXKmuSNQDAtbySrJkGBwDA4aisAQCu5ZXKmmQNAHAtryRrpsEBAHA4KmsAgGt5pbImWQMAXM0tCbclmAYHAMDhqKwBAK7FNDgAAA5HsgYAwOG8kqw5Zw0AgMNRWQMAXMsrlTXJGgDgWl5J1kyDAwDgcFTWAADX8kplTbIGALiWV5I10+AAADgclTXwLT6fz3LM6frNPDo62nLM8OHDbfX117/+1VacVXaOd2hoqOWY48ePW45xOjvHzi4nV59eqaxJ1gAA1/JKsmYaHAAAh6OyBgC4llcqa5I1AMC1SNYAADicV5I156wBAHA4KmsAgGt5pbImWQMAXMsryZppcAAAHI7KGgDgWl6prEnWAADX8kqyZhocAACHo7IGviUkxPrvr7W1tZZjevbsaTnmJz/5ieWYY8eOWY6RpIqKCssxlZWVlmPWrVtnOeZ0LsphZ7EMO98hO/2czuNgdfEUY4zq6uraaDT1+/JCZU2yBgC4mlsSbkswDQ4AgMNRWQMAXItpcAAAHI5kDQCAw3klWXPOGgAAh6OyBgC4llcqa5I1AMC1vJKsmQYHAMDhqKwBAK7llcqaZA0AcC2vJGumwQEAcDgqa+BbrC5YINlbyOOyyy6zHJOZmWk5ZteuXZZjJCk8PNxyTFRUlOWYyy+/3HLMH/7wB8sxJSUllmMke1WXne+DHR07drQVZ2eBjaNHj9rq63TwSmVNsgYAuJZXkjXT4AAAOFyrJ+sHHnhAPp8vYOvTp09rdwMAgL+ybsnmBm0yDX7++efrvffe+3cn7ZhtBwC0PqbBW6Bdu3ZKSkryb/Hx8W3RDQDA44JVWefn5ys9PV0RERHKyMjQunXrmmy/YMEC9e7dW5GRkUpNTdXMmTNVWVnZ7P7aJFl/+eWXSklJUY8ePfTDH/5QO3fubLRtVVWVysvLAzYAAJzqpZdeUk5OjnJzc7VhwwYNHDhQWVlZ2rdvX4Ptly5dqnvvvVe5ubn6/PPP9cc//lEvvfSSfvnLXza7z1ZP1hkZGVq8eLFWrFihp556Stu3b9eIESN0+PDhBtvn5eUpNjbWv6Wmprb2kAAAZ6hgVNZPPPGEpk2bpqlTp6pv375auHChoqKitGjRogbbf/TRR7rkkkt00003KT09XWPHjtWNN954ymr821o9WY8bN04/+MEPNGDAAGVlZentt99WaWmp/vznPzfYftasWSorK/NvRUVFrT0kAMAZqrWS9XdneKuqqhrsr7q6WuvXrw947kFISIgyMzO1Zs2aBmMuvvhirV+/3p+cv/rqK7399tu68sorm/052/zKr06dOuncc8/Vtm3bGnw9PDzc1gMYAABoLd+d1c3NzdUDDzxQr92BAwdUW1urxMTEgP2JiYnasmVLg+9900036cCBAxo+fLiMMTp+/Lh+9rOfBXca/LuOHDmiwsJCJScnt3VXAACPaa3KuqioKGCWd9asWa02xoKCAj388MP67W9/qw0bNmjZsmV66623NG/evGa/R6tX1nfeeafGjx+vtLQ07dmzR7m5uQoNDdWNN97Y2l0BADyutW7diomJUUxMzCnbx8fHKzQ0tN4jbEtKSpSUlNRgzOzZs/WjH/1IP/nJTyRJ/fv3V0VFhW699Vbdd999Cgk5dd3c6pX1rl27dOONN6p37966/vrr1blzZ61du1ZdunRp7a4AADitwsLCNHjwYK1cudK/r66uTitXrtSwYcMajDl69Gi9hHxyHYLm/qLR6pX1iy++2NpvCZw21dXVp6WfCy+80HJMenq65Rg7C5NIatZv+t/1zjvvWI654IILLMfMnz/fcswnn3xiOUaSPvvsM8sxn3/+ueWYoUOHWo6x8x2STlyZbFVjF041xhhz2m7DDcZDUXJycjRlyhQNGTJEQ4cO1YIFC1RRUaGpU6dKkiZPnqyuXbsqLy9PkjR+/Hg98cQTuuCCC5SRkaFt27Zp9uzZGj9+fLP/jfJoMQCAq53up5BNmjRJ+/fv15w5c1RcXKxBgwZpxYoV/ovOdu7cGfAL7/333y+fz6f7779fu3fvVpcuXTR+/Hj993//d7P7JFkDAGDR9OnTNX369AZfKygoCPi5Xbt2ys3NVW5uru3+SNYAANfyyrPBSdYAANciWQMA4HBeSdZt/lAUAADQMlTWAADX8kplTbIGALiWV5I10+AAADgclTUAwLW8UlmTrAEAruWVZM00OAAADkdljTOSz+ezFWfnt+zLL7/ccsyQIUMsxxw+fNhyTIcOHSzHSNK55557WmL++c9/Wo7Ztm2b5ZiOHTtajpHU6CpKTbn22mstx9TU1FiOsXPsJPmXabSiqqrKUvvjx4/r73//u+V+7PBKZU2yBgC4lleSNdPgAAA4HJU1AMC1vFJZk6wBAK5FsgYAwOG8kqw5Zw0AgMNRWQMAXMsrlTXJGgDgWl5J1kyDAwDgcFTWAADX8kplTbIGALiWV5I10+AAADgclTUAwLW8UlmTrHFa2V0Ny8nmzZtnOSY5ObkNRlJfVFSUrbjjx49bjqmurrYcM3z4cMsxdlYsq6ursxwjSRs2bLAcY2dVMDvHOzs723KMJPXo0cNyzHXXXWerr9PFLQm3JZgGBwDA4aisAQCuxTQ4AAAOR7IGAMDhvJKsOWcNAIDDUVkDAFzLK5U1yRoA4FpeSdZMgwMA4HBU1gAA1/JKZU2yBgC4lleSNdPgAAA4HJU1AMC1vFJZk6xxWrnlH4YV33zzjeUYOwt5HDt2zHJMeHi45RhJatfO+n8NHTt2tBxTWVlpOSYyMtJyjN2FPEaMGGE55uKLL7YcExJifZIzISHBcowkrVixwlacU3klWTMNDgCAw1FZAwBcyyuVNckaAOBaJGsAABzOK8mac9YAADgclTUAwLW8UlmTrAEAruWVZM00OAAADkdlDQBwLa9U1iRrAIBreSVZMw0OAIDDUVkDAFzLK5U1yRpooaioKMsxdhZusBNz9OhRyzGSVFZWZjnm4MGDlmPS09Mtx9j5z9Xn81mOkewdczvfh9raWssxdhcnSU1NtRXnVF5J1kyDAwDgcFTWAABXc0t13BKWK+sPPvhA48ePV0pKinw+n1577bWA140xmjNnjpKTkxUZGanMzEx9+eWXrTVeAAD8Tk6Dt2RzA8vJuqKiQgMHDlR+fn6Dr8+fP1+/+c1vtHDhQn388cfq0KGDsrKybC0yDwBAU7ySrC1Pg48bN07jxo1r8DVjjBYsWKD7779f3//+9yVJf/rTn5SYmKjXXntNN9xwQ8tGCwCAB7XqBWbbt29XcXGxMjMz/ftiY2OVkZGhNWvWNBhTVVWl8vLygA0AgObwSmXdqsm6uLhYkpSYmBiwPzEx0f/ad+Xl5Sk2Nta/nWm3FQAA2g7J+jSZNWuWysrK/FtRUVGwhwQAgKO06q1bSUlJkqSSkhIlJyf795eUlGjQoEENxoSHhys8PLw1hwEA8AgeimJD9+7dlZSUpJUrV/r3lZeX6+OPP9awYcNasysAADwzDW65sj5y5Ii2bdvm/3n79u3atGmT4uLi1K1bN82YMUMPPfSQevXqpe7du2v27NlKSUnRhAkTWnPcAAB4huVk/cknn2j06NH+n3NyciRJU6ZM0eLFi3X33XeroqJCt956q0pLSzV8+HCtWLFCERERrTdqAADknWlwy8l61KhRTX44n8+nBx98UA8++GCLBoYzk50FFewspmBnYQRJ6tixo+WYlJQUyzFVVVWnJcbu9SDV1dWWY+wsGtKpUyfLMXYWDLGzuIYkhYWFWY45fPiw5ZjY2FjLMZ9++qnlGMned3zIkCGW2tfW1mrjxo2W+7EjWMk6Pz9fjz32mIqLizVw4EA9+eSTGjp0aKPtS0tLdd9992nZsmU6dOiQ0tLStGDBAl155ZXN6o9ngwMAXCsYyfqll15STk6OFi5cqIyMDC1YsEBZWVnaunWrEhIS6rWvrq7W5ZdfroSEBL3yyivq2rWrvv76a0u/rJKsAQCw4IknntC0adM0depUSdLChQv11ltvadGiRbr33nvrtV+0aJEOHTqkjz76SO3bt5dkfXnYoN9nDQCAXa11Nfh3n6TZ2Gmn6upqrV+/PuBJnSEhIcrMzGz0SZ1/+ctfNGzYMGVnZysxMVH9+vXTww8/bOl0HckaAOBarZWsU1NTA56mmZeX12B/Bw4cUG1traUndX711Vd65ZVXVFtbq7fffluzZ8/W448/roceeqjZn5NpcACA5xUVFSkmJsb/c2s+rKuurk4JCQl6+umnFRoaqsGDB2v37t167LHHlJub26z3IFkDAFyrtS4wi4mJCUjWjYmPj1doaKhKSkoC9peUlPif4vldycnJat++vUJDQ/37zjvvPBUXF6u6urpZdx0wDQ4AcK3T/QSzsLAwDR48OOBJnXV1dVq5cmWjT+q85JJLtG3bNtXV1fn3ffHFF0pOTm727YEkawAALMjJydHvf/97Pfvss/r888912223qaKiwn91+OTJkzVr1ix/+9tuu02HDh3SHXfcoS+++EJvvfWWHn74YWVnZze7T6bBAQCuFYz7rCdNmqT9+/drzpw5Ki4u1qBBg7RixQr/RWc7d+4MeJhTamqq3nnnHc2cOVMDBgxQ165ddccdd+iee+5pdp8kawCAawXrCWbTp0/X9OnTG3ytoKCg3r5hw4Zp7dq1tvqSmAYHAMDxqKwBAK7FQh4AADgcyRpoA3b+YXz73sTmsrvq1qRJkyzHNHZvZVP2799vOSYyMtJyzLdvFbGiQ4cOlmNSU1Mtx9hZ3cvOwypqamosx0hSu3bW/4u08/fUuXNnyzH5+fmWYyRp0KBBlmPsHIfTyS0JtyU4Zw0AgMM5+9clAACawDQ4AAAO55VkzTQ4AAAOR2UNAHAtr1TWJGsAgGt5JVkzDQ4AgMNRWQMAXMsrlTXJGgDgWl5J1kyDAwDgcFTWAADX8kplTbIGALgWyRpoA3YWBLCz2INdmzdvthxTVVVlOaZ9+/aWY07ngiYJCQmWYyorKy3HHDx40HKMnWMXERFhOUayt6DJN998Yzlm165dlmNuuukmyzGS9Nhjj1mOWbt2ra2+TgevJGvOWQMA4HBU1gAA1/JKZU2yBgC4lleSNdPgAAA4HJU1AMC1vFJZk6wBAK7llWTNNDgAAA5HZQ0AcC2vVNYkawCAa3klWTMNDgCAw1FZAwBcyyuVNckaAOBaJGsP8Pl8tuLsLKgQEmL9jIOd8dXU1FiOqaursxxj1/Hjx09bX3a8/fbblmMqKiosxxw7dsxyTFhYmOUYu/8R7d+/33KMnX8XdhbYsPMdt+t0/Xuyc+wGDBhgOUaSysrKbMU5mVsSbktwzhoAAIfzdGUNAHA3psEBAHA4ryRrpsEBAHA4KmsAgGt5pbImWQMAXMsryZppcAAAHI7KGgDgWl6prEnWAADX8kqyZhocAACHo7IGALiWVyprkjUAwLVI1i5j50H4tbW1tvpy+mIUTjZy5EjLMRMnTrQcc8kll1iOkaSjR49ajjl48KDlGDuLcrRrZ/2fq93vuJ3jYOffYHh4uOUYO4t/2P0P2c5xsMPO9+HIkSO2+rr22mstx7zxxhu2+jodvJKsOWcNAIDDnTGVNQDAe6isG/HBBx9o/PjxSklJkc/n02uvvRbw+s033yyfzxewXXHFFa01XgAA/E4m65ZsbmA5WVdUVGjgwIHKz89vtM0VV1yhvXv3+rcXXnihRYMEAMDLLE+Djxs3TuPGjWuyTXh4uJKSkmwPCgCA5mAavAUKCgqUkJCg3r1767bbbmvyatmqqiqVl5cHbAAANAfT4DZdccUV+tOf/qSVK1fq0Ucf1erVqzVu3LhGbyHJy8tTbGysf0tNTW3tIQEA4GqtfjX4DTfc4P9z//79NWDAAJ1zzjkqKCjQmDFj6rWfNWuWcnJy/D+Xl5eTsAEAzcI0eCvp0aOH4uPjtW3btgZfDw8PV0xMTMAGAEBzMA3eSnbt2qWDBw8qOTm5rbsCAOCMZHka/MiRIwFV8vbt27Vp0ybFxcUpLi5Oc+fO1cSJE5WUlKTCwkLdfffd6tmzp7Kyslp14AAAeGUa3HKy/uSTTzR69Gj/zyfPN0+ZMkVPPfWUPv30Uz377LMqLS1VSkqKxo4dq3nz5tl6BjAAAE0hWTdi1KhRTX64d955p0UDssvuggWnS1xcnOWYlJQUyzG9evU6Lf1I9hYEOPfccy3HVFVVWY4JCbF3hsfOwg2dO3e2HLNnzx7LMZWVlZZj7CwQIUkJCQmWY6qrqy3HREVFWY756KOPLMd07NjRcoxkb+GZuro6yzFlZWWWY2pqaizHSNJFF11kK87J3JJwW4KFPAAAcDgW8gAAuBbT4AAAOJxXkjXT4AAAOByVNQDAtbxSWZOsAQCu5ZVkzTQ4AAAOR7IGALhWsJ4Nnp+fr/T0dEVERCgjI0Pr1q1rVtyLL74on8+nCRMmWOqPZA0AcK1gJOuXXnpJOTk5ys3N1YYNGzRw4EBlZWVp3759Tcbt2LFDd955p0aMGGG5T5I1AMDzysvLA7amnpz4xBNPaNq0aZo6dar69u2rhQsXKioqSosWLWo0pra2Vj/84Q81d+5c9ejRw/L4SNYAANdqrco6NTVVsbGx/i0vL6/B/qqrq7V+/XplZmb694WEhCgzM1Nr1qxpdJwPPvigEhISdMstt9j6nFwNDgBwrda6GryoqEgxMTH+/Y0tPnXgwAHV1tYqMTExYH9iYqK2bNnSYMyHH36oP/7xj9q0aZPtcZKsAQCu1VrJOiYmJiBZt5bDhw/rRz/6kX7/+98rPj7e9vucMcnazkoy8+bNs9VXly5dLMd06tTJcoydlcRCQ0Mtx5SWllqOkaTjx49bjjl8+LDlGDurOfl8PssxknTs2DHLMXZWgbr++ustx3zyySeWY6Kjoy3HSPZWOktPT7fVl1X9+/e3HGP3OBQVFVmOsbNyW2RkpOUYuyuJpaWl2YrDCfHx8QoNDVVJSUnA/pKSEiUlJdVrX1hYqB07dmj8+PH+fSdXZmvXrp22bt2qc84555T9cs4aAOBap/tq8LCwMA0ePFgrV67076urq9PKlSs1bNiweu379Omjzz77TJs2bfJvV199tUaPHq1NmzYpNTW1Wf2eMZU1AMB7gvEEs5ycHE2ZMkVDhgzR0KFDtWDBAlVUVGjq1KmSpMmTJ6tr167Ky8tTRESE+vXrFxB/cqb1u/ubQrIGAMCCSZMmaf/+/ZozZ46Ki4s1aNAgrVixwn/R2c6dOxUS0roT1yRrAIBrBevZ4NOnT9f06dMbfK2goKDJ2MWLF1vuj2QNAHAtFvIAAACOQGUNAHAtr1TWJGsAgGt5JVkzDQ4AgMNRWQMAXMsrlTXJGgDgWiRrAABcwC0JtyUcm6xDQkIsLcbwm9/8xnIfycnJlmMkewts2ImxsyCAHWFhYbbi7HwmOwtl2BEbG2srzs4iB4888ojlGDvH4bbbbrMcs2fPHssxklRZWWk55tvPSm6ur776ynJMr169LMd07tzZcoxkbxGZ9u3bW46x87SrmpoayzGStH//fltxCC7HJmsAAE6FaXAAABzOK8maW7cAAHA4KmsAgGt5pbImWQMAXMsryZppcAAAHI7KGgDgWl6prEnWAADX8kqyZhocAACHo7IGALiWVyprkjUAwLVI1gAAOBzJOshuvPFGSwtM2FmAobCw0HKMJHXs2PG0xMTFxVmOscPOwgOSvcUyioqKLMfYWYwiKirKcowklZSUWI559tlnLcdMmDDBcswbb7xhOSY9Pd1yjGTv+zp48GDLMaNHj7YcY2fRCzsLckhSeHi45Ri7C+NYZWchHcnev/fU1FRL7evq6rR7927L/aBxjk3WAACcCpU1AAAO55Vkza1bAAA4HJU1AMC1vFJZk6wBAK7llWTNNDgAAA5HZQ0AcC2vVNYkawCAa3klWTMNDgCAw1FZAwBcyyuVNckaAOBaJGsAAByOZB1k+/fvt/TAeTsLRERHR1uOkaSqqirLMXbGZ2cxBTuLCMTExFiOkaRDhw5Zjvn6668tx9g5DseOHbMcI0mVlZWWY44fP245Zvny5ZZjPvvsM8sxdhfysLOIjJ3FMkpLSy3H1NTUWI6x83cknViQwio7C2XY6cfn81mOkez9H3Huuedaan/8+HEW8mhljk3WAAA0h1uq45YgWQMAXMsr0+CWbt3Ky8vThRdeqOjoaCUkJGjChAnaunVrQJvKykplZ2erc+fO6tixoyZOnGhrjWAAAHCCpWS9evVqZWdna+3atXr33XdVU1OjsWPHqqKiwt9m5syZeuONN/Tyyy9r9erV2rNnj6699tpWHzgAACcr65ZsbmBpGnzFihUBPy9evFgJCQlav369Ro4cqbKyMv3xj3/U0qVLddlll0mSnnnmGZ133nlau3atLrrootYbOQDA85gGb4aysjJJ/75ydP369aqpqVFmZqa/TZ8+fdStWzetWbOmwfeoqqpSeXl5wAYAAP7NdrKuq6vTjBkzdMkll6hfv36SpOLiYoWFhalTp04BbRMTE1VcXNzg++Tl5Sk2Nta/paam2h0SAMBjvDINbjtZZ2dna/PmzXrxxRdbNIBZs2aprKzMv9m5HxkA4E1eSda2bt2aPn263nzzTX3wwQc6++yz/fuTkpJUXV2t0tLSgOq6pKRESUlJDb5XeHi4wsPD7QwDAABPsFRZG2M0ffp0LV++XO+//766d+8e8PrgwYPVvn17rVy50r9v69at2rlzp4YNG9Y6IwYA4P+jsm5Adna2li5dqtdff13R0dH+89CxsbGKjIxUbGysbrnlFuXk5CguLk4xMTH6+c9/rmHDhnElOACg1XnlanBLyfqpp56SJI0aNSpg/zPPPKObb75ZkvTrX/9aISEhmjhxoqqqqpSVlaXf/va3rTJYAAC+jWTdgOZ8qIiICOXn5ys/P9/2oCRp7969Cg0NbXZ7Owd8165dlmMkqUOHDpZj4uPjLcfYWeTgwIEDlmP2799vOUaS2rWzfsmDnesT7CyMEBERYTlGsre4S0iI9es07fw9nXfeeZZjvv3AIivsXOj5zTffWI6x832wc+zsLP4h2VsAxE5fkZGRlmMauw7oVE7ecmvFoEGDLLWvqqrS6tWrLfeDxvFscACAa1FZAwDgcF5J1i16ghkAAGh7VNYAANfySmVNsgYAuJZXkjXT4AAAOByVNQDAtbxSWZOsAQCu5ZVkzTQ4AAAOR2UNAHAtr1TWJGsAgGuRrAEAcDivJGvOWQMA4HCOraw/++wzS+2XLVtmuY8f//jHlmMkac+ePZZjvvrqK8sxlZWVlmM6duxoOcbOqlaSvZWCwsLCLMdYWX3tpKqqKssxklRbW2s5xs5v5kePHrUcs3fvXssxdqsGO8fBzipsp+s7Xl1dbTlGsrfynZ0YOyt12VkRTJK6d+9uOaakpMRSe7vH2y63VMct4dhkDQDAqTANDgAAGpSfn6/09HRFREQoIyND69ata7Tt73//e40YMUJnnXWWzjrrLGVmZjbZviEkawCAa52srFuyWfXSSy8pJydHubm52rBhgwYOHKisrCzt27evwfYFBQW68cYbtWrVKq1Zs0apqakaO3asdu/e3ew+SdYAANdqrWRdXl4esDV13csTTzyhadOmaerUqerbt68WLlyoqKgoLVq0qMH2S5Ys0e23365BgwapT58++sMf/qC6ujqtXLmy2Z+TZA0A8LzU1FTFxsb6t7y8vAbbVVdXa/369crMzPTvCwkJUWZmptasWdOsvo4ePaqamhrFxcU1e3xcYAYAcK3WusCsqKhIMTEx/v3h4eENtj9w4IBqa2uVmJgYsD8xMVFbtmxpVp/33HOPUlJSAhL+qZCsAQCu1VrJOiYmJiBZt5VHHnlEL774ogoKChQREdHsOJI1AADNFB8fr9DQ0Hr3npeUlCgpKanJ2F/96ld65JFH9N5772nAgAGW+uWcNQDAtU731eBhYWEaPHhwwMVhJy8WGzZsWKNx8+fP17x587RixQoNGTLE8ueksgYAuFYwHoqSk5OjKVOmaMiQIRo6dKgWLFigiooKTZ06VZI0efJkde3a1X+R2qOPPqo5c+Zo6dKlSk9PV3FxsaQTT+Nr7hP5SNYAANcKRrKeNGmS9u/frzlz5qi4uFiDBg3SihUr/Bed7dy5UyEh/564fuqpp1RdXa3rrrsu4H1yc3P1wAMPNKtPkjUAABZNnz5d06dPb/C1goKCgJ937NjR4v7OmGTd2D1xTdm0aZOtvu68807LMenp6ZZjDhw4YDnGziICFRUVlmMkewts2FnIw84CEXbGJkk+n89yjJ3fzO0snmInxs7xttuXnWNnh51+rC5EcZKdRUOs3Dt7Ul1dneWYU13M1JhPP/3Ucszzzz9vq6/TwSvPBj9jkjUAwHu8kqy5GhwAAIejsgYAuJZXKmuSNQDAtbySrJkGBwDA4aisAQCu5ZXKmmQNAHAtryRrpsEBAHA4KmsAgGt5pbImWQMAXItkDQCAw3klWXPOGgAAh3NsZe3z+Sw9sN/Og/D/+te/Wo6xGzd69GjLMXYWJ0lLS7McExsbazlGUsAScM1lZ4ENOwt51NbWWo6xa9++fZZj7Pw2v3v3bssxVVVVlmMk6ciRI5Zj7C6eYpWdY1dTU2Orr6NHj1qOsfPv4t1337Uc8/nnn1uOkaSPPvrIVpyTuaU6bgnHJmsAAE6FaXAAAOAIVNYAANfySmVNsgYAuJZXkjXT4AAAOByVNQDAtbxSWZOsAQCu5ZVkzTQ4AAAOR2UNAHAtr1TWJGsAgGuRrAEAcDivJGvOWQMA4HCOraxb+tuS06xatcpyzEUXXdQGI6mvT58+tuLi4+Mtx5SWllqOOfvssy3H7Nixw3KMZG/Bh8LCQlt9AWg5r1TWjk3WAACcileSNdPgAAA4nKVknZeXpwsvvFDR0dFKSEjQhAkTtHXr1oA2o0aN8q9FfXL72c9+1qqDBgBA+ndl3ZLNDSwl69WrVys7O1tr167Vu+++q5qaGo0dO1YVFRUB7aZNm6a9e/f6t/nz57fqoAEAkLyTrC2ds16xYkXAz4sXL1ZCQoLWr1+vkSNH+vdHRUUpKSmpdUYIAIDHteicdVlZmSQpLi4uYP+SJUsUHx+vfv36adasWTp69Gij71FVVaXy8vKADQCA5qCyPoW6ujrNmDFDl1xyifr16+fff9NNNyktLU0pKSn69NNPdc8992jr1q1atmxZg++Tl5enuXPn2h0GAMDDvHI1uO1knZ2drc2bN+vDDz8M2H/rrbf6/9y/f38lJydrzJgxKiws1DnnnFPvfWbNmqWcnBz/z+Xl5UpNTbU7LAAAzji2kvX06dP15ptv6oMPPjjlAysyMjIkSdu2bWswWYeHhys8PNzOMAAAHkdl3QBjjH7+859r+fLlKigoUPfu3U8Zs2nTJklScnKyrQECANAYknUDsrOztXTpUr3++uuKjo5WcXGxJCk2NlaRkZEqLCzU0qVLdeWVV6pz58769NNPNXPmTI0cOVIDBgxokw8AAPAuknUDnnrqKUknHnzybc8884xuvvlmhYWF6b333tOCBQtUUVGh1NRUTZw4Uffff3+rDRgAAK+xPA3elNTUVK1evbpFAwIAwAq3VMctwUIe0JYtW4I9hCZt3rw52EMA4FAtTdRuSfQs5AEAgMNRWQMAXMsrlTXJGgDgWl5J1kyDAwDgcFTWAADX8kplTbIGALiWV5I10+AAADgclTUAwLW8UlmTrAEArkWyBgDA4bySrDlnDQCAw1FZAwBcyyuVNckaAOBaXknWTIMDAOBwVNYAANfySmVNsgYAuJZXkjXT4AAAOByVNQDAtbxSWZOsAQCu5ZVkzTQ4AAAOR2UNAHAtKmsAABzOGNPizY78/Hylp6crIiJCGRkZWrduXZPtX375ZfXp00cRERHq37+/3n77bUv9kawBAK4VjGT90ksvKScnR7m5udqwYYMGDhyorKws7du3r8H2H330kW688Ubdcsst2rhxoyZMmKAJEyZo8+bNlj6oo5SVlRlJbGxsbGwu38rKyk5LrvD5fLY3O2MdOnSoyc7O9v9cW1trUlJSTF5eXoPtr7/+enPVVVcF7MvIyDA//elPm92n4ypr45LzBwCApp2u/89NK1TV5eXlAVtVVVWDfVVXV2v9+vXKzMz07wsJCVFmZqbWrFnTYMyaNWsC2ktSVlZWo+0b4rhkffjw4WAPAQDQCtry//OwsDAlJSW1ynt17NhRqampio2N9W95eXkNtj1w4IBqa2uVmJgYsD8xMVHFxcUNxhQXF1tq3xDHXQ2ekpKioqIiRUdHy+fzBbxWXl6u1NRUFRUVKSYmJkgjDD6OwwkchxM4DidwHE5wwnEwxujw4cNKSUlpsz4iIiK0fft2VVdXt/i9jDH18k14eHiL37c1OS5Zh4SE6Oyzz26yTUxMjKf/MZ7EcTiB43ACx+EEjsMJwT4OsbGxbd5HRESEIiIi2ryfb4uPj1doaKhKSkoC9peUlDRa6SclJVlq3xDHTYMDAOBUYWFhGjx4sFauXOnfV1dXp5UrV2rYsGENxgwbNiygvSS9++67jbZviOMqawAAnCwnJ0dTpkzRkCFDNHToUC1YsEAVFRWaOnWqJGny5Mnq2rWr/7z3HXfcoUsvvVSPP/64rrrqKr344ov65JNP9PTTTze7T1cl6/DwcOXm5jruXMLpxnE4geNwAsfhBI7DCRyHtjdp0iTt379fc+bMUXFxsQYNGqQVK1b4LyLbuXOnQkL+PXF98cUXa+nSpbr//vv1y1/+Ur169dJrr72mfv36NbtPn+FeKQAAHI1z1gAAOBzJGgAAhyNZAwDgcCRrAAAcjmQNAIDDuSZZW1079Ez0wAMPyOfzBWx9+vQJ9rDa3AcffKDx48crJSVFPp9Pr732WsDrxhjNmTNHycnJioyMVGZmpr788svgDLYNneo43HzzzfW+H1dccUVwBttG8vLydOGFFyo6OloJCQmaMGGCtm7dGtCmsrJS2dnZ6ty5szp27KiJEyfWe3qU2zXnOIwaNare9+FnP/tZkEaMlnJFsra6duiZ7Pzzz9fevXv924cffhjsIbW5iooKDRw4UPn5+Q2+Pn/+fP3mN7/RwoUL9fHHH6tDhw7KyspSZWXlaR5p2zrVcZCkK664IuD78cILL5zGEba91atXKzs7W2vXrtW7776rmpoajR07VhUVFf42M2fO1BtvvKGXX35Zq1ev1p49e3TttdcGcdStrznHQZKmTZsW8H2YP39+kEaMFmv2YppBZHXt0DNVbm6uGThwYLCHEVSSzPLly/0/19XVmaSkJPPYY4/595WWlprw8HDzwgsvBGGEp8d3j4MxxkyZMsV8//vfD8p4gmXfvn1Gklm9erUx5sTfffv27c3LL7/sb/P5558bSWbNmjXBGmab++5xMMaYSy+91Nxxxx3BGxRaleMraztrh57JvvzyS6WkpKhHjx764Q9/qJ07dwZ7SEG1fft2FRcXB3w/YmNjlZGR4cnvR0FBgRISEtS7d2/ddtttOnjwYLCH1KbKysokSXFxcZKk9evXq6amJuD70KdPH3Xr1u2M/j589zictGTJEsXHx6tfv36aNWuWjh49GozhoRU4/nGjTa0dumXLliCNKjgyMjK0ePFi9e7dW3v37tXcuXM1YsQIbd68WdHR0cEeXlCcXA+2pWvFngmuuOIKXXvtterevbsKCwv1y1/+UuPGjdOaNWsUGhoa7OG1urq6Os2YMUOXXHKJ/7GNxcXFCgsLU6dOnQLansnfh4aOgyTddNNNSktLU0pKij799FPdc8892rp1q5YtWxbE0cIuxydr/Nu4ceP8fx4wYIAyMjKUlpamP//5z7rllluCODI4wQ033OD/c//+/TVgwACdc845Kigo0JgxY4I4sraRnZ2tzZs3e+K6jaY0dhxuvfVW/5/79++v5ORkjRkzRoWFhTrnnHNO9zDRQo6fBrezdqhXdOrUSeeee662bdsW7KEEzcnvAN+P+nr06KH4+Pgz8vsxffp0vfnmm1q1apXOPvts//6kpCRVV1ertLQ0oP2Z+n1o7Dg0JCMjQ5LOyO+DFzg+WdtZO9Qrjhw5osLCQiUnJwd7KEHTvXt3JSUlBXw/ysvL9fHHH3v++7Fr1y4dPHjwjPp+GGM0ffp0LV++XO+//766d+8e8PrgwYPVvn37gO/D1q1btXPnzjPq+3Cq49CQTZs2SdIZ9X3wEldMg59q7VCvuPPOOzV+/HilpaVpz549ys3NVWhoqG688cZgD61NHTlyJKAa2L59uzZt2qS4uDh169ZNM2bM0EMPPaRevXqpe/fumj17tlJSUjRhwoTgDboNNHUc4uLiNHfuXE2cOFFJSUkqLCzU3XffrZ49eyorKyuIo25d2dnZWrp0qV5//XVFR0f7z0PHxsYqMjJSsbGxuuWWW5STk6O4uDjFxMTo5z//uYYNG6aLLrooyKNvPac6DoWFhVq6dKmuvPJKde7cWZ9++qlmzpypkSNHasCAAUEePWwJ9uXozfXkk0+abt26mbCwMDN06FCzdu3aYA/ptJs0aZJJTk42YWFhpmvXrmbSpElm27ZtwR5Wm1u1apWRVG+bMmWKMebE7VuzZ882iYmJJjw83IwZM8Zs3bo1uINuA00dh6NHj5qxY8eaLl26mPbt25u0tDQzbdo0U1xcHOxht6qGPr8k88wzz/jbHDt2zNx+++3mrLPOMlFRUeaaa64xe/fuDd6g28CpjsPOnTvNyJEjTVxcnAkPDzc9e/Y0d911lykrKwvuwGEb61kDAOBwjj9nDQCA15GsAQBwOJI1AAAOR7IGAMDhSNYAADgcyRoAAIcjWQMA4HAkawAAHI5kDQCAw5GsAQBwOJI1AAAO9/8AIREcwXP0qi8AAAAASUVORK5CYII=\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Original label for the first image: 9\n", - "One-hot encoded label for the first image: [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Display the first image and its label\n", - "plt.figure()\n", - "plt.imshow(X_train[0], cmap='gray')\n", - "plt.title(f\"Label: {class_names[y_train[0]]}\")\n", - "plt.colorbar()\n", - "plt.grid(False)\n", - "plt.show()\n", - "\n", - "print(\"Original label for the first image:\", y_train[0])\n", - "print(\"One-hot encoded label for the first image:\", y_train_one_hot[0])" - ] - }, - { - "cell_type": "markdown", - "id": "989f7dd0", - "metadata": { - "id": "989f7dd0" - }, - "source": [ - "Question: Does the data look as expected? How is the quality of the images? Are there any issues with the dataset that you notice?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ac04ffed" - }, - "source": [ - "Answer: The labels appear to match the images. The images are of very low resolution but are still discernible. It is difficult to distinguish between certain items, such as a Pullover and a Shirt, or a Coat and a Pullover, based on the random images observed." - ], - "id": "ac04ffed" - }, - { - "cell_type": "markdown", - "id": "c9e8ad60", - "metadata": { - "id": "c9e8ad60" - }, - "source": [ - "# 2. Baseline Model\n", - "\n", - "In this section, you will create a linear regression model as a baseline. This model will not use any convolutional layers, but it will help you understand the performance of a simple model on this dataset.\n", - "You should:\n", - "- [ ] Create a simple linear regression model using Keras.\n", - "- [ ] Compile the model with an appropriate loss function and optimizer.\n", - "- [ ] Train the model on the training set and evaluate it on the test set.\n", - "\n", - "A linear regression model can be created using the `Sequential` API in Keras. Using a single `Dense` layer with no activation function is equivalent to a simple linear regression model. Make sure that the number of units in the output layer matches the number of classes in the dataset.\n", - "\n", - "Note that for this step, we will need to use `Flatten` to convert the 2D images into 1D vectors before passing them to the model. Put a `Flatten()` layer as the first layer in your model so that the 2D image data can be flattened into 1D vectors." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "8563a7aa", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 901 - }, - "id": "8563a7aa", - "outputId": "4995131f-cae1-4d21-92ac-2083591bc0de" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(**kwargs)\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.7357 - loss: 0.7990 - val_accuracy: 0.8367 - val_loss: 0.4759\n", - "Epoch 2/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - accuracy: 0.8375 - loss: 0.4803 - val_accuracy: 0.8417 - val_loss: 0.4465\n", - "Epoch 3/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8492 - loss: 0.4427 - val_accuracy: 0.8488 - val_loss: 0.4397\n", - "Epoch 4/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8554 - loss: 0.4256 - val_accuracy: 0.8552 - val_loss: 0.4207\n", - "Epoch 5/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8552 - loss: 0.4180 - val_accuracy: 0.8493 - val_loss: 0.4250\n", - "Epoch 6/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8597 - loss: 0.4074 - val_accuracy: 0.8477 - val_loss: 0.4377\n", - "Epoch 7/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8626 - loss: 0.4011 - val_accuracy: 0.8563 - val_loss: 0.4086\n", - "Epoch 8/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8611 - loss: 0.3970 - val_accuracy: 0.8577 - val_loss: 0.4080\n", - "Epoch 9/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8619 - loss: 0.3992 - val_accuracy: 0.8537 - val_loss: 0.4144\n", - "Epoch 10/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8636 - loss: 0.3920 - val_accuracy: 0.8580 - val_loss: 0.4098\n", - "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8480 - loss: 0.4376\n", - "Test accuracy: 0.8443999886512756\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "from keras.models import Sequential\n", - "from keras.layers import Dense, Flatten\n", - "from tensorflow.keras.optimizers import SGD\n", - "\n", - "\n", - "# Create a simple linear regression model\n", - "model = Sequential()\n", - "\n", - "# You can use `model.add()` to add layers to the model\n", - "model.add(Flatten(input_shape=X_train.shape[1:]))\n", - "model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "# Compile the model using `model.compile()`\n", - "# Adam optimizer\n", - "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "# Stochastic Gradient Descent with a learning rate of 0.01\n", - "# model.compile(optimizer=SGD(learning_rate=0.01), loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "\n", - "# Train the model with `model.fit()`\n", - "history = model.fit(X_train, y_train_one_hot, epochs=10, batch_size=32, validation_split=0.1)\n", - "\n", - "# Evaluate the model with `model.evaluate()`\n", - "test_loss, test_acc = model.evaluate(X_test, y_test_one_hot)\n", - "print('Test accuracy:', test_acc)\n", - "\n", - "plt.plot(history.history['loss'], label='train')\n", - "plt.plot(history.history['val_loss'], label='validation')\n", - "plt.ylim(0, 2)\n", - "plt.legend(loc='best')\n", - "plt.title('Loss');\n" - ] - }, - { - "cell_type": "markdown", - "id": "9a07e9f7", - "metadata": { - "id": "9a07e9f7" - }, - "source": [ - "Reflection: What is the performance of the baseline model? How does it compare to what you expected? Why do you think the performance is at this level?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "93db732f" - }, - "source": [ - "Results: The accuracy on the training data increased with each epoch, as did the accuracy on the validation data. The loss also decreased gradually. Overall, this is a good baseline performance for a simple linear model on this dataset." - ], - "id": "93db732f" - }, - { - "cell_type": "markdown", - "id": "fa107b59", - "metadata": { - "id": "fa107b59" - }, - "source": [ - "# 3. Building and Evaluating a Simple CNN Model\n", - "\n", - "In this section, you will build a simple Convolutional Neural Network (CNN) model using Keras. A convolutional neural network is a type of deep learning model that is particularly effective for image classification tasks. Unlike the basic neural networks we have built in the labs, CNNs can accept images as input without needing to flatten them into vectors.\n", - "\n", - "You should:\n", - "- [ ] Build a simple CNN model with at least one convolutional layer (to learn spatial hierarchies in images) and one fully connected layer (to make predictions).\n", - "- [ ] Compile the model with an appropriate loss function and metrics for a multi-class classification problem.\n", - "- [ ] Train the model on the training set and evaluate it on the test set.\n", - "\n", - "Convolutional layers are designed to accept inputs with three dimensions: height, width and channels (e.g., RGB for color images). For grayscale images like those in Fashion MNIST, the input shape will be (28, 28, 1).\n", - "\n", - "When you progress from the convolutional layers to the fully connected layers, you will need to flatten the output of the convolutional layers. This can be done using the `Flatten` layer in Keras, which doesn't require any parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3513cf3d", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 901 - }, - "id": "3513cf3d", - "outputId": "f0ff2227-398f-4849-acae-286612bee3d7" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 16ms/step - accuracy: 0.8175 - loss: 0.5277 - val_accuracy: 0.8858 - val_loss: 0.3170\n", - "Epoch 2/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 15ms/step - accuracy: 0.8991 - loss: 0.2844 - val_accuracy: 0.8853 - val_loss: 0.3172\n", - "Epoch 3/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 15ms/step - accuracy: 0.9125 - loss: 0.2410 - val_accuracy: 0.8883 - val_loss: 0.3075\n", - "Epoch 4/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 17ms/step - accuracy: 0.9201 - loss: 0.2156 - val_accuracy: 0.9010 - val_loss: 0.2815\n", - "Epoch 5/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 17ms/step - accuracy: 0.9317 - loss: 0.1925 - val_accuracy: 0.9003 - val_loss: 0.2917\n", - "Epoch 6/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 16ms/step - accuracy: 0.9387 - loss: 0.1659 - val_accuracy: 0.9008 - val_loss: 0.2978\n", - "Epoch 7/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 16ms/step - accuracy: 0.9446 - loss: 0.1531 - val_accuracy: 0.9010 - val_loss: 0.3045\n", - "Epoch 8/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 15ms/step - accuracy: 0.9507 - loss: 0.1388 - val_accuracy: 0.9047 - val_loss: 0.3088\n", - "Epoch 9/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 17ms/step - accuracy: 0.9559 - loss: 0.1260 - val_accuracy: 0.9033 - val_loss: 0.3164\n", - "Epoch 10/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 15ms/step - accuracy: 0.9590 - loss: 0.1180 - val_accuracy: 0.8997 - val_loss: 0.3409\n", - "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8995 - loss: 0.3454\n", - "Test accuracy: 0.8992999792098999\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "from keras.layers import Conv2D\n", - "\n", - "# Reshape the data to include the channel dimension\n", - "X_train = X_train.reshape(-1, 28, 28, 1)\n", - "X_test = X_test.reshape(-1, 28, 28, 1)\n", - "\n", - "# Create a simple CNN model\n", - "model = Sequential()\n", - "\n", - "# Adding a convolutional layer with 32 filters of size 3x3.\n", - "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", - "\n", - "# Adding the Flatten Layer to flatten the 2D output of the convolutional layer into a 1D vector.\n", - "model.add(Flatten())\n", - "\n", - "# Adding the Dense Layer for a fully connected layer.\n", - "model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "# Train the model and save history\n", - "# Compiling the model\n", - "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "# Training the model\n", - "history = model.fit(X_train, y_train_one_hot, epochs=10, batch_size=32, validation_split=0.1)\n", - "\n", - "# Evaluating the model\n", - "test_loss, test_acc = model.evaluate(X_test, y_test_one_hot)\n", - "print('Test accuracy:', test_acc)\n", - "\n", - "# Plotting loss\n", - "plt.plot(history.history['loss'], label='train')\n", - "plt.plot(history.history['val_loss'], label='validation')\n", - "plt.ylim(0, 2)\n", - "plt.legend(loc='best')\n", - "plt.title('Loss');\n" - ] - }, - { - "cell_type": "markdown", - "id": "fabe379c", - "metadata": { - "id": "fabe379c" - }, - "source": [ - "Reflection: Did the CNN model perform better than the baseline model? If so, by how much? What do you think contributed to this improvement?" - ] - }, - { - "cell_type": "markdown", - "source": [ - "Results:\n", - "\n", - "- The baseline model achieved a test accuracy of approximately 84.4%.\n", - "- The simple CNN model achieved a test accuracy of approximately 89.9%.\n", - "\n", - "Conclusion:\n", - "- The CNN model performed better than the baseline model on the test set.\n", - "- The higher accuracy of the CNN suggests that the convolutional layer was effective in learning more complex features from the image data leading to better performance." - ], - "metadata": { - "id": "5Ai7KYBymoLB" - }, - "id": "5Ai7KYBymoLB" - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "FozJ9DzLnxAb" - }, - "id": "FozJ9DzLnxAb", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "id": "1a5e2463", - "metadata": { - "id": "1a5e2463" - }, - "source": [ - "# 3. Designing and Running Controlled Experiments\n", - "\n", - "In this section, you will design and run controlled experiments to improve the model's performance. You will focus on one hyperparameter and one regularization technique.\n", - "You should:\n", - "- [ ] Choose one hyperparameter to experiment with (e.g., number of filters, kernel size, number of layers, etc.) and one regularization technique (e.g., dropout, L2 regularization). For your hyperparameter, you should choose at least three different values to test (but there is no upper limit). For your regularization technique, simply test the presence or absence of the technique.\n", - "- [ ] Run experiments by modifying the model architecture or hyperparameters, and evaluate the performance of each model on the test set.\n", - "- [ ] Record the results of your experiments, including the test accuracy and any other relevant metrics.\n", - "- [ ] Visualize the results of your experiments using plots or tables to compare the performance of different models.\n", - "\n", - "The best way to run your experiments is to create a `for` loop that iterates over a range of values for the hyperparameter you are testing. For example, if you are testing different numbers of filters, you can create a loop that runs the model with 32, 64, and 128 filters. Within the loop, you can compile and train the model, then evaluate it on the test set. After each iteration, you can store the results in a list or a dictionary for later analysis.\n", - "\n", - "Note: It's critical that you re-initialize the model (by creating a new instance of the model) before each experiment. If you don't, the model will retain the weights from the previous experiment, which can lead to misleading results." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "99d6f46c", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 662 - }, - "id": "99d6f46c", - "outputId": "32140b50-86bc-4cbf-ef51-3142aeee7334" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Testing with 16 filters...\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Test accuracy with 16 filters: 0.8952\n", - "Testing with 32 filters...\n", - "Test accuracy with 32 filters: 0.8957\n", - "Testing with 64 filters...\n", - "Test accuracy with 64 filters: 0.8983\n", - "\n", - "Results for different numbers of filters:\n", - "Number of filters: 16, Test Accuracy: 0.8952\n", - "Number of filters: 32, Test Accuracy: 0.8957\n", - "Number of filters: 64, Test Accuracy: 0.8983\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "# A. Test Hyperparameters\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Flatten, Conv2D\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Experimenting with number of filters in the convolutional layer\n", - "num_filters_options = [16, 32, 64]\n", - "results_filters = {}\n", - "\n", - "for num_filters in num_filters_options:\n", - " print(f\"Testing with {num_filters} filters...\")\n", - "\n", - " # Initialize the model\n", - " model = Sequential()\n", - " model.add(Conv2D(num_filters, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", - " model.add(Flatten())\n", - " model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - " # Compiling the model\n", - " model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - " # Training the model\n", - " history = model.fit(X_train, y_train_one_hot, epochs=5, batch_size=32, validation_split=0.1, verbose=0)\n", - "\n", - " # Evaluating the model\n", - " test_loss, test_acc = model.evaluate(X_test, y_test_one_hot, verbose=0)\n", - " print(f\"Test accuracy with {num_filters} filters: {test_acc:.4f}\")\n", - "\n", - " # Storing results\n", - " results_filters[num_filters] = test_acc\n", - "\n", - "# Printing summarized results\n", - "print(\"\\nResults for different numbers of filters:\")\n", - "for num_filters, accuracy in results_filters.items():\n", - " print(f\"Number of filters: {num_filters}, Test Accuracy: {accuracy:.4f}\")\n", - "\n", - "# Visualizing results\n", - "plt.figure(figsize=(8, 4))\n", - "plt.bar(range(len(results_filters)), list(results_filters.values()), align='center')\n", - "plt.xticks(range(len(results_filters)), list(results_filters.keys()))\n", - "plt.ylabel('Test Accuracy')\n", - "plt.xlabel('Number of Filters')\n", - "plt.title('CNN Performance with Different Numbers of Filters')\n", - "plt.ylim(0.85, 0.95)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "dc43ac81", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 983 - }, - "id": "dc43ac81", - "outputId": "5c999758-6bd4-42e5-a800-2979d190d5af" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Testing with Dropout (rate=0.25)...\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1/5\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 24ms/step - accuracy: 0.8088 - loss: 0.5421 - val_accuracy: 0.8782 - val_loss: 0.3378\n", - "Epoch 2/5\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 23ms/step - accuracy: 0.8906 - loss: 0.3032 - val_accuracy: 0.8940 - val_loss: 0.2984\n", - "Epoch 3/5\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 23ms/step - accuracy: 0.9062 - loss: 0.2599 - val_accuracy: 0.8950 - val_loss: 0.2952\n", - "Epoch 4/5\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 23ms/step - accuracy: 0.9163 - loss: 0.2345 - val_accuracy: 0.8983 - val_loss: 0.2905\n", - "Epoch 5/5\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 23ms/step - accuracy: 0.9210 - loss: 0.2186 - val_accuracy: 0.8955 - val_loss: 0.2933\n", - "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8999 - loss: 0.3034\n", - "Test accuracy with Dropout: 0.8986\n", - "Testing without Dropout...\n", - "Epoch 1/5\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 15ms/step - accuracy: 0.8091 - loss: 0.5430 - val_accuracy: 0.8828 - val_loss: 0.3312\n", - "Epoch 2/5\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 15ms/step - accuracy: 0.8968 - loss: 0.2932 - val_accuracy: 0.8827 - val_loss: 0.3138\n", - "Epoch 3/5\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 15ms/step - accuracy: 0.9137 - loss: 0.2451 - val_accuracy: 0.8952 - val_loss: 0.2912\n", - "Epoch 4/5\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 15ms/step - accuracy: 0.9211 - loss: 0.2231 - val_accuracy: 0.8975 - val_loss: 0.2942\n", - "Epoch 5/5\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 15ms/step - accuracy: 0.9291 - loss: 0.1973 - val_accuracy: 0.9030 - val_loss: 0.2851\n", - "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9011 - loss: 0.2921\n", - "Test accuracy without Dropout: 0.8985\n", - "\n", - "Results for regularization experiment:\n", - "with Dropout: Test Accuracy: 0.8986\n", - "without Dropout: Test Accuracy: 0.8985\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "# B. Test presence or absence of regularization\n", - "from keras.layers import Dropout\n", - "\n", - "# Experimenting with Dropout regularization\n", - "dropout_rate = 0.25\n", - "results_regularization = {}\n", - "\n", - "# Model with Dropout\n", - "print(f\"Testing with Dropout (rate={dropout_rate})...\")\n", - "model_dropout = Sequential()\n", - "\n", - "# Using 32 filters based on previous experiment or as a starting point\n", - "model_dropout.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", - "model_dropout.add(Flatten())\n", - "\n", - "# Adding Dropout layer\n", - "model_dropout.add(Dropout(dropout_rate))\n", - "\n", - "model_dropout.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "model_dropout.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", - "history_dropout = model_dropout.fit(X_train, y_train_one_hot, epochs=5, batch_size=32, validation_split=0.1)\n", - "test_loss_dropout, test_acc_dropout = model_dropout.evaluate(X_test, y_test_one_hot)\n", - "\n", - "print(f\"Test accuracy with Dropout: {test_acc_dropout:.4f}\")\n", - "results_regularization['with Dropout'] = test_acc_dropout\n", - "\n", - "# Modeling without Dropout (Simple CNN as a comparison)\n", - "print(\"Testing without Dropout...\")\n", - "model_no_dropout = Sequential()\n", - "\n", - "# Same architecture as above\n", - "model_no_dropout.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", - "model_no_dropout.add(Flatten())\n", - "model_no_dropout.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "model_no_dropout.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", - "history_no_dropout = model_no_dropout.fit(X_train, y_train_one_hot, epochs=5, batch_size=32, validation_split=0.1)\n", - "test_loss_no_dropout, test_acc_no_dropout = model_no_dropout.evaluate(X_test, y_test_one_hot)\n", - "print(f\"Test accuracy without Dropout: {test_acc_no_dropout:.4f}\")\n", - "results_regularization['without Dropout'] = test_acc_no_dropout\n", - "\n", - "\n", - "# Printing summarized results\n", - "print(\"\\nResults for regularization experiment:\")\n", - "for regularization_type, accuracy in results_regularization.items():\n", - " print(f\"{regularization_type}: Test Accuracy: {accuracy:.4f}\")\n", - "\n", - "# Visualizing results\n", - "plt.figure(figsize=(6, 4))\n", - "plt.bar(range(len(results_regularization)), list(results_regularization.values()), align='center')\n", - "plt.xticks(range(len(results_regularization)), list(results_regularization.keys()))\n", - "plt.ylabel('Test Accuracy')\n", - "plt.title('CNN Performance with and without Dropout Regularization')\n", - "plt.ylim(0.85, 0.95)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cb426f26", - "metadata": { - "id": "cb426f26" - }, - "source": [ - "Reflection: Report on the performance of the models you tested. Did any of the changes you made improve the model's performance? If so, which ones? What do you think contributed to these improvements? Finally, what combination of hyperparameters and regularization techniques yielded the best performance?\n", - "\n", - "Hyperparameter Experiment (Number of Filters):\n", - "- Increasing the number of filters in the convolutional layer from 16 to 64 generally led to a slight improvement in test accuracy.\n", - "- The model with 64 filters had the highest accuracy among the values tested in this experiment (approximately 0.8983).\n", - "\n", - "\n", - "Regularization Experiment (Dropout):\n", - "- The model with Dropout (rate 0.25) had a test accuracy of approximately 0.8986, while the model without Dropout had a test accuracy of approximately 0.8985.\n", - "- The impact of Dropout on test accuracy appears minimal." - ] - }, - { - "cell_type": "markdown", - "id": "46c43a3d", - "metadata": { - "id": "46c43a3d" - }, - "source": [ - "# 5. Training Final Model and Evaluation\n", - "\n", - "In this section, you will train the final model using the best hyperparameters and regularization techniques you found in the previous section. You should:\n", - "- [ ] Compile the final model with the best hyperparameters and regularization techniques.\n", - "- [ ] Train the final model on the training set and evaluate it on the test set.\n", - "- [ ] Report the final model's performance on the test set, including accuracy and any other relevant metrics." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "31f926d1", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 901 - }, - "id": "31f926d1", - "outputId": "7aa38061-ff43-4fcb-8e35-40bbd2ebcd02" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", - " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 38ms/step - accuracy: 0.8134 - loss: 0.5285 - val_accuracy: 0.8843 - val_loss: 0.3175\n", - "Epoch 2/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 38ms/step - accuracy: 0.8955 - loss: 0.2882 - val_accuracy: 0.8922 - val_loss: 0.2995\n", - "Epoch 3/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 39ms/step - accuracy: 0.9106 - loss: 0.2493 - val_accuracy: 0.8978 - val_loss: 0.2857\n", - "Epoch 4/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 40ms/step - accuracy: 0.9219 - loss: 0.2166 - val_accuracy: 0.8992 - val_loss: 0.2913\n", - "Epoch 5/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 39ms/step - accuracy: 0.9294 - loss: 0.1955 - val_accuracy: 0.9020 - val_loss: 0.2864\n", - "Epoch 6/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 39ms/step - accuracy: 0.9379 - loss: 0.1700 - val_accuracy: 0.9010 - val_loss: 0.3012\n", - "Epoch 7/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 37ms/step - accuracy: 0.9433 - loss: 0.1604 - val_accuracy: 0.9045 - val_loss: 0.2953\n", - "Epoch 8/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 37ms/step - accuracy: 0.9468 - loss: 0.1474 - val_accuracy: 0.9015 - val_loss: 0.3205\n", - "Epoch 9/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 37ms/step - accuracy: 0.9515 - loss: 0.1332 - val_accuracy: 0.8987 - val_loss: 0.3235\n", - "Epoch 10/10\n", - "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 37ms/step - accuracy: 0.9554 - loss: 0.1259 - val_accuracy: 0.9023 - val_loss: 0.3234\n", - "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.8957 - loss: 0.3465\n", - "Final model test accuracy: 0.8995000123977661\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "# The model with 64 filters with Dropout (rate 0.25) had the highest test accuracy\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Flatten, Conv2D, Dropout\n", - "import matplotlib.pyplot as plt\n", - "\n", - "final_model = Sequential()\n", - "final_model.add(Conv2D(64, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", - "final_model.add(Flatten())\n", - "final_model.add(Dropout(0.25))\n", - "final_model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "final_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "# Train the final model\n", - "history_final = final_model.fit(X_train, y_train_one_hot, epochs=10, batch_size=32, validation_split=0.1)\n", - "\n", - "# Evaluate the final model\n", - "test_loss_final, test_acc_final = final_model.evaluate(X_test, y_test_one_hot)\n", - "print('Final model test accuracy:', test_acc_final)\n", - "\n", - "# Plotting loss for the final model\n", - "plt.plot(history_final.history['loss'], label='train')\n", - "plt.plot(history_final.history['val_loss'], label='validation')\n", - "plt.ylim(0, 2)\n", - "plt.legend(loc='best')\n", - "plt.title('Final Model Loss');\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "a01f8ebc", - "metadata": { - "id": "a01f8ebc" - }, - "source": [ - "Reflection: How does the final model's performance compare to the baseline and the CNN model? What do you think contributed to the final model's performance? If you had time, what other experiments would you run to further improve the model's performance?\n", - "\n", - "Results:\n", - "- Baseline Model: 84.4%\n", - "- Simple CNN Model: 89.9%\n", - "- Final Model (with 64 filters and Dropout): 90.0%\n", - "\n", - "The final model shows improvement in test accuracy compared to the simple CNN and a significant improvement over the baseline model.\n", - "\n", - "Contributing factors:\n", - "- 64 filters extracted richer edges and textures improving class separation.\n", - "- Dropout(0.25) provided regularization balancing overfitting and underfitting.\n", - "- Adam optimizer offered effective adaptive learning improving convergence and stability.\n", - "\n", - "Potential improvments (Future experiments):\n", - "- Early stopping: Looking at the plots from the training history I can see that the training loss continues to decrease throughout the 10 epochs but the validation loss starts to level off and even slightly increase after around epoch 7 or 8 which suggests that the model might be starting to overfit the training data. One consideration is stopping the training earlier at around epoch 7 or 8 where the validation loss was at its lowest point could potentially lead to a model that generalizes better to new data and might have resulted in a slightly higher test accuracy.\n", - "\n", - "- Dropout rates: Trying a higher dropout rate instead of 0.25 could potentially help further mitigate overfitting forceing the network to learn more features that are not dependent on any single neuron which can improve generalization to unseen data.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "01db8512", - "metadata": { - "id": "01db8512" - }, - "source": [ - "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n", - "### Submission Parameters:\n", - "* Submission Due Date: `23:59 PM - 26/10/2025`\n", - "* The branch name for your repo should be: `assignment-1`\n", - "* What to submit for this assignment:\n", - " * This Jupyter Notebook (assignment_1.ipynb)\n", - " * The Lab 1 notebook (labs/lab_1.ipynb)\n", - " * The Lab 2 notebook (labs/lab_2.ipynb)\n", - " * The Lab 3 notebook (labs/lab_3.ipynb)\n", - "* What the pull request link should look like for this assignment: `https://github.com//deep_learning/pull/`\n", - "* Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n", - "Checklist:\n", - "- [ ] Created a branch with the correct naming convention.\n", - "- [ ] Ensured that the repository is public.\n", - "- [ ] Reviewed the PR description guidelines and adhered to them.\n", - "- [ ] Verify that the link is accessible in a private browser window.\n", - "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-7-help-ml`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "deep_learning", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - }, - "colab": { - "provenance": [] - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file From 05522253226b0644ad2efefc56d1d74365c075a4 Mon Sep 17 00:00:00 2001 From: Vincent Van Schaik <193274586+VincentSchaik@users.noreply.github.com> Date: Sun, 2 Nov 2025 13:36:33 -0500 Subject: [PATCH 02/16] Revised baseline model. Removed activation function. --- 02_activities/assignments/assignment_1.ipynb | 1010 ++++++++++++++++++ 1 file changed, 1010 insertions(+) create mode 100644 02_activities/assignments/assignment_1.ipynb diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb new file mode 100644 index 000000000..0eb17e1c5 --- /dev/null +++ b/02_activities/assignments/assignment_1.ipynb @@ -0,0 +1,1010 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "927ae8f4", + "metadata": { + "id": "927ae8f4" + }, + "source": [ + "# Assignment 1 - Building a Vision Model with Keras\n", + "\n", + "In this assignment, you will build a simple vision model using Keras. The goal is to classify images from the Fashion MNIST dataset, which contains images of clothing items.\n", + "\n", + "You will:\n", + "1. Load and inspect the Fashion MNIST dataset.\n", + "2. Run a simple baseline model to establish a performance benchmark.\n", + "3. Build and evaluate a simple CNN model, choosing appropriate loss and metrics.\n", + "4. Design and run controlled experiments on one hyperparameter (e.g., number of filters, kernel size, etc.) and one regularization technique (e.g., dropout, L2 regularization).\n", + "5. Analyze the results and visualize the model's performance.\n", + "\n", + "# 1. Loading and Inspecting the Dataset\n", + "\n", + "Fashion MNIST is a dataset of grayscale images of clothing items, with 10 classes. Each image is 28x28 pixels, like the MNIST dataset of handwritten digits. Keras provides a convenient way to load this dataset.\n", + "\n", + "In this section, you should:\n", + "\n", + "- [ ] Inspect the shapes of the training and test sets to confirm their size and structure.\n", + "- [ ] Convert the labels to one-hot encoded format if necessary. (There is a utility function in Keras for this.)\n", + "- [ ] Visualize a few images from the dataset to understand what the data looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "420c7178", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "420c7178", + "outputId": "c856223c-2859-4d8b-a45f-595d5828e7f0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n", + "\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n", + "\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n", + "\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n", + "\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n" + ] + } + ], + "source": [ + "from tensorflow.keras.datasets import fashion_mnist\n", + "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n", + "\n", + "# Normalize the pixel values to be between 0 and 1\n", + "X_train = X_train.astype('float32') / 255.0\n", + "X_test = X_test.astype('float32') / 255.0\n", + "\n", + "# Classes in the Fashion MNIST dataset\n", + "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a6c89fe7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "a6c89fe7", + "outputId": "7650ed4c-bed8-44f7-9641-ec17b0ea4b01" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "X_train shape: (60000, 28, 28)\n", + "y_train shape: (60000,)\n", + "X_test shape: (10000, 28, 28)\n", + "y_test shape: (10000,)\n", + "y_train_one_hot shape: (60000, 10)\n", + "y_test_one_hot shape: (10000, 10)\n", + "\n", + "First 5 one-hot encoded training labels:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "First 5 one-hot encoded test labels:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n", + " [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.]])" + ] + }, + "metadata": {} + } + ], + "source": [ + "import numpy as np\n", + "# Inspect the shapes of the datasets\n", + "print(\"X_train shape:\", X_train.shape)\n", + "print(\"y_train shape:\", y_train.shape)\n", + "print(\"X_test shape:\", X_test.shape)\n", + "print(\"y_test shape:\", y_test.shape)\n", + "\n", + "# One-hot encoding on a given array of class labels.\n", + "def one_hot(n_classes, y):\n", + " return np.eye(n_classes)[y]\n", + "\n", + "num_classes = len(class_names)\n", + "\n", + "y_train_one_hot = one_hot(num_classes, y=y_train)\n", + "y_test_one_hot = one_hot(num_classes, y=y_test)\n", + "\n", + "print(\"y_train_one_hot shape:\", y_train_one_hot.shape)\n", + "print(\"y_test_one_hot shape:\", y_test_one_hot.shape)\n", + "\n", + "# Print a few examples of the one-hot encoded labels\n", + "print(\"\\nFirst 5 one-hot encoded training labels:\")\n", + "display(y_train_one_hot[:5])\n", + "print(\"\\nFirst 5 one-hot encoded test labels:\")\n", + "display(y_test_one_hot[:5])" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 607 + }, + "id": "da802b1d", + "outputId": "062881b5-eee9-47fd-f937-9cf4b6e85d78" + }, + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Selecting 9 random indices\n", + "random_indices = np.random.choice(len(X_train), 9, replace=False)\n", + "\n", + "# Creating a 3x3 grid plot\n", + "fig, axes = plt.subplots(3, 3, figsize=(6, 6))\n", + "\n", + "for i, ax in enumerate(axes.flat):\n", + " ax.imshow(X_train[random_indices[i]], cmap='gray')\n", + " ax.set_title(f\"Label: {class_names[y_train[random_indices[i]]]}\")\n", + "\n", + " # Removing axis labels\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "id": "da802b1d", + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "13e100db", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 488 + }, + "id": "13e100db", + "outputId": "100739b0-4141-4a9e-ce31-4758682c9d1f" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Original label for the first image: 9\n", + "One-hot encoded label for the first image: [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Display the first image and its label\n", + "plt.figure()\n", + "plt.imshow(X_train[0], cmap='gray')\n", + "plt.title(f\"Label: {class_names[y_train[0]]}\")\n", + "plt.colorbar()\n", + "plt.grid(False)\n", + "plt.show()\n", + "\n", + "print(\"Original label for the first image:\", y_train[0])\n", + "print(\"One-hot encoded label for the first image:\", y_train_one_hot[0])" + ] + }, + { + "cell_type": "markdown", + "id": "989f7dd0", + "metadata": { + "id": "989f7dd0" + }, + "source": [ + "Question: Does the data look as expected? How is the quality of the images? Are there any issues with the dataset that you notice?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ac04ffed" + }, + "source": [ + "Answer: The labels appear to match the images. The images are of very low resolution but are still discernible. It is difficult to distinguish between certain items, such as a Pullover and a Shirt, or a Coat and a Pullover, based on the random images observed." + ], + "id": "ac04ffed" + }, + { + "cell_type": "markdown", + "id": "c9e8ad60", + "metadata": { + "id": "c9e8ad60" + }, + "source": [ + "# 2. Baseline Model\n", + "\n", + "In this section, you will create a linear regression model as a baseline. This model will not use any convolutional layers, but it will help you understand the performance of a simple model on this dataset.\n", + "You should:\n", + "- [ ] Create a simple linear regression model using Keras.\n", + "- [ ] Compile the model with an appropriate loss function and optimizer.\n", + "- [ ] Train the model on the training set and evaluate it on the test set.\n", + "\n", + "A linear regression model can be created using the `Sequential` API in Keras. Using a single `Dense` layer with no activation function is equivalent to a simple linear regression model. Make sure that the number of units in the output layer matches the number of classes in the dataset.\n", + "\n", + "Note that for this step, we will need to use `Flatten` to convert the 2D images into 1D vectors before passing them to the model. Put a `Flatten()` layer as the first layer in your model so that the 2D image data can be flattened into 1D vectors." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8563a7aa", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 845 + }, + "id": "8563a7aa", + "outputId": "486a5a9e-5242-4b6e-8cb3-0edfc7aa31f1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.2515 - loss: 9.2925 - val_accuracy: 0.2622 - val_loss: 10.9502\n", + "Epoch 2/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.3003 - loss: 9.1870 - val_accuracy: 0.3043 - val_loss: 8.6636\n", + "Epoch 3/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.3284 - loss: 9.8619 - val_accuracy: 0.3205 - val_loss: 8.9238\n", + "Epoch 4/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.3214 - loss: 8.6299 - val_accuracy: 0.2790 - val_loss: 10.1073\n", + "Epoch 5/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.2990 - loss: 9.2748 - val_accuracy: 0.2875 - val_loss: 7.5371\n", + "Epoch 6/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.2918 - loss: 8.7675 - val_accuracy: 0.2870 - val_loss: 9.5375\n", + "Epoch 7/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.2924 - loss: 9.7496 - val_accuracy: 0.2755 - val_loss: 7.9185\n", + "Epoch 8/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.2847 - loss: 8.5859 - val_accuracy: 0.2605 - val_loss: 7.2141\n", + "Epoch 9/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.2702 - loss: 8.6233 - val_accuracy: 0.2570 - val_loss: 9.7228\n", + "Epoch 10/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.2643 - loss: 9.4007 - val_accuracy: 0.2567 - val_loss: 10.0137\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.2597 - loss: 9.9368\n", + "Test accuracy: 0.25940001010894775\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense, Flatten\n", + "from tensorflow.keras.optimizers import SGD\n", + "\n", + "\n", + "# Create a simple linear regression model\n", + "model = Sequential()\n", + "\n", + "# You can use `model.add()` to add layers to the model\n", + "model.add(Flatten(input_shape=X_train.shape[1:]))\n", + "\n", + "# With an activation function\n", + "# model.add(Dense(num_classes, activation='softmax'))\n", + "\n", + "# No activation function\n", + "model.add(Dense(num_classes))\n", + "\n", + "# Compile the model using `model.compile()`\n", + "# Adam optimizer\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Stochastic Gradient Descent with a learning rate of 0.01\n", + "# model.compile(optimizer=SGD(learning_rate=0.01), loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "\n", + "# Train the model with `model.fit()`\n", + "history = model.fit(X_train, y_train_one_hot, epochs=10, batch_size=32, validation_split=0.1)\n", + "\n", + "# Evaluate the model with `model.evaluate()`\n", + "test_loss, test_acc = model.evaluate(X_test, y_test_one_hot)\n", + "print('Test accuracy:', test_acc)\n", + "\n", + "plt.plot(history.history['loss'], label='train')\n", + "plt.plot(history.history['val_loss'], label='validation')\n", + "plt.ylim(0, 10)\n", + "plt.legend(loc='best')\n", + "plt.title('Loss');\n" + ] + }, + { + "cell_type": "markdown", + "id": "9a07e9f7", + "metadata": { + "id": "9a07e9f7" + }, + "source": [ + "Reflection: What is the performance of the baseline model? How does it compare to what you expected? Why do you think the performance is at this level?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "93db732f" + }, + "source": [ + "Results: The training and validation accuracy and loss show fluctuations across the epochs, rather than a consistent increasing or decreasing trend. The baseline performance is quite low as expected for a simple linear model." + ], + "id": "93db732f" + }, + { + "cell_type": "markdown", + "id": "fa107b59", + "metadata": { + "id": "fa107b59" + }, + "source": [ + "# 3. Building and Evaluating a Simple CNN Model\n", + "\n", + "In this section, you will build a simple Convolutional Neural Network (CNN) model using Keras. A convolutional neural network is a type of deep learning model that is particularly effective for image classification tasks. Unlike the basic neural networks we have built in the labs, CNNs can accept images as input without needing to flatten them into vectors.\n", + "\n", + "You should:\n", + "- [ ] Build a simple CNN model with at least one convolutional layer (to learn spatial hierarchies in images) and one fully connected layer (to make predictions).\n", + "- [ ] Compile the model with an appropriate loss function and metrics for a multi-class classification problem.\n", + "- [ ] Train the model on the training set and evaluate it on the test set.\n", + "\n", + "Convolutional layers are designed to accept inputs with three dimensions: height, width and channels (e.g., RGB for color images). For grayscale images like those in Fashion MNIST, the input shape will be (28, 28, 1).\n", + "\n", + "When you progress from the convolutional layers to the fully connected layers, you will need to flatten the output of the convolutional layers. This can be done using the `Flatten` layer in Keras, which doesn't require any parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3513cf3d", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 901 + }, + "id": "3513cf3d", + "outputId": "f0ff2227-398f-4849-acae-286612bee3d7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 16ms/step - accuracy: 0.8175 - loss: 0.5277 - val_accuracy: 0.8858 - val_loss: 0.3170\n", + "Epoch 2/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 15ms/step - accuracy: 0.8991 - loss: 0.2844 - val_accuracy: 0.8853 - val_loss: 0.3172\n", + "Epoch 3/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 15ms/step - accuracy: 0.9125 - loss: 0.2410 - val_accuracy: 0.8883 - val_loss: 0.3075\n", + "Epoch 4/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 17ms/step - accuracy: 0.9201 - loss: 0.2156 - val_accuracy: 0.9010 - val_loss: 0.2815\n", + "Epoch 5/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 17ms/step - accuracy: 0.9317 - loss: 0.1925 - val_accuracy: 0.9003 - val_loss: 0.2917\n", + "Epoch 6/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 16ms/step - accuracy: 0.9387 - loss: 0.1659 - val_accuracy: 0.9008 - val_loss: 0.2978\n", + "Epoch 7/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 16ms/step - accuracy: 0.9446 - loss: 0.1531 - val_accuracy: 0.9010 - val_loss: 0.3045\n", + "Epoch 8/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 15ms/step - accuracy: 0.9507 - loss: 0.1388 - val_accuracy: 0.9047 - val_loss: 0.3088\n", + "Epoch 9/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 17ms/step - accuracy: 0.9559 - loss: 0.1260 - val_accuracy: 0.9033 - val_loss: 0.3164\n", + "Epoch 10/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 15ms/step - accuracy: 0.9590 - loss: 0.1180 - val_accuracy: 0.8997 - val_loss: 0.3409\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8995 - loss: 0.3454\n", + "Test accuracy: 0.8992999792098999\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "from keras.layers import Conv2D\n", + "\n", + "# Reshape the data to include the channel dimension\n", + "X_train = X_train.reshape(-1, 28, 28, 1)\n", + "X_test = X_test.reshape(-1, 28, 28, 1)\n", + "\n", + "# Create a simple CNN model\n", + "model = Sequential()\n", + "\n", + "# Adding a convolutional layer with 32 filters of size 3x3.\n", + "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + "\n", + "# Adding the Flatten Layer to flatten the 2D output of the convolutional layer into a 1D vector.\n", + "model.add(Flatten())\n", + "\n", + "# Adding the Dense Layer for a fully connected layer.\n", + "model.add(Dense(num_classes, activation='softmax'))\n", + "\n", + "# Train the model and save history\n", + "# Compiling the model\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Training the model\n", + "history = model.fit(X_train, y_train_one_hot, epochs=10, batch_size=32, validation_split=0.1)\n", + "\n", + "# Evaluating the model\n", + "test_loss, test_acc = model.evaluate(X_test, y_test_one_hot)\n", + "print('Test accuracy:', test_acc)\n", + "\n", + "# Plotting loss\n", + "plt.plot(history.history['loss'], label='train')\n", + "plt.plot(history.history['val_loss'], label='validation')\n", + "plt.ylim(0, 2)\n", + "plt.legend(loc='best')\n", + "plt.title('Loss');\n" + ] + }, + { + "cell_type": "markdown", + "id": "fabe379c", + "metadata": { + "id": "fabe379c" + }, + "source": [ + "Reflection: Did the CNN model perform better than the baseline model? If so, by how much? What do you think contributed to this improvement?" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Results:\n", + "\n", + "- The baseline model achieved a test accuracy of approximately 25.9%.\n", + "- The simple CNN model achieved a test accuracy of approximately 89.9%.\n", + "\n", + "Conclusion:\n", + "- The CNN model performed better than the baseline model on the test set.\n", + "- The higher accuracy of the CNN suggests that the convolutional layer was effective in learning more complex features from the image data leading to better performance." + ], + "metadata": { + "id": "5Ai7KYBymoLB" + }, + "id": "5Ai7KYBymoLB" + }, + { + "cell_type": "markdown", + "id": "1a5e2463", + "metadata": { + "id": "1a5e2463" + }, + "source": [ + "# 3. Designing and Running Controlled Experiments\n", + "\n", + "In this section, you will design and run controlled experiments to improve the model's performance. You will focus on one hyperparameter and one regularization technique.\n", + "You should:\n", + "- [ ] Choose one hyperparameter to experiment with (e.g., number of filters, kernel size, number of layers, etc.) and one regularization technique (e.g., dropout, L2 regularization). For your hyperparameter, you should choose at least three different values to test (but there is no upper limit). For your regularization technique, simply test the presence or absence of the technique.\n", + "- [ ] Run experiments by modifying the model architecture or hyperparameters, and evaluate the performance of each model on the test set.\n", + "- [ ] Record the results of your experiments, including the test accuracy and any other relevant metrics.\n", + "- [ ] Visualize the results of your experiments using plots or tables to compare the performance of different models.\n", + "\n", + "The best way to run your experiments is to create a `for` loop that iterates over a range of values for the hyperparameter you are testing. For example, if you are testing different numbers of filters, you can create a loop that runs the model with 32, 64, and 128 filters. Within the loop, you can compile and train the model, then evaluate it on the test set. After each iteration, you can store the results in a list or a dictionary for later analysis.\n", + "\n", + "Note: It's critical that you re-initialize the model (by creating a new instance of the model) before each experiment. If you don't, the model will retain the weights from the previous experiment, which can lead to misleading results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99d6f46c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 662 + }, + "id": "99d6f46c", + "outputId": "32140b50-86bc-4cbf-ef51-3142aeee7334" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing with 16 filters...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test accuracy with 16 filters: 0.8952\n", + "Testing with 32 filters...\n", + "Test accuracy with 32 filters: 0.8957\n", + "Testing with 64 filters...\n", + "Test accuracy with 64 filters: 0.8983\n", + "\n", + "Results for different numbers of filters:\n", + "Number of filters: 16, Test Accuracy: 0.8952\n", + "Number of filters: 32, Test Accuracy: 0.8957\n", + "Number of filters: 64, Test Accuracy: 0.8983\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "# A. Test Hyperparameters\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, Flatten, Conv2D\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Experimenting with number of filters in the convolutional layer\n", + "num_filters_options = [16, 32, 64]\n", + "results_filters = {}\n", + "\n", + "for num_filters in num_filters_options:\n", + " print(f\"Testing with {num_filters} filters...\")\n", + "\n", + " # Initialize the model\n", + " model = Sequential()\n", + " model.add(Conv2D(num_filters, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + " model.add(Flatten())\n", + " model.add(Dense(num_classes, activation='softmax'))\n", + "\n", + " # Compiling the model\n", + " model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + " # Training the model\n", + " history = model.fit(X_train, y_train_one_hot, epochs=5, batch_size=32, validation_split=0.1, verbose=0)\n", + "\n", + " # Evaluating the model\n", + " test_loss, test_acc = model.evaluate(X_test, y_test_one_hot, verbose=0)\n", + " print(f\"Test accuracy with {num_filters} filters: {test_acc:.4f}\")\n", + "\n", + " # Storing results\n", + " results_filters[num_filters] = test_acc\n", + "\n", + "# Printing summarized results\n", + "print(\"\\nResults for different numbers of filters:\")\n", + "for num_filters, accuracy in results_filters.items():\n", + " print(f\"Number of filters: {num_filters}, Test Accuracy: {accuracy:.4f}\")\n", + "\n", + "# Visualizing results\n", + "plt.figure(figsize=(8, 4))\n", + "plt.bar(range(len(results_filters)), list(results_filters.values()), align='center')\n", + "plt.xticks(range(len(results_filters)), list(results_filters.keys()))\n", + "plt.ylabel('Test Accuracy')\n", + "plt.xlabel('Number of Filters')\n", + "plt.title('CNN Performance with Different Numbers of Filters')\n", + "plt.ylim(0.85, 0.95)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc43ac81", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 983 + }, + "id": "dc43ac81", + "outputId": "5c999758-6bd4-42e5-a800-2979d190d5af" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Testing with Dropout (rate=0.25)...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/5\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 24ms/step - accuracy: 0.8088 - loss: 0.5421 - val_accuracy: 0.8782 - val_loss: 0.3378\n", + "Epoch 2/5\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 23ms/step - accuracy: 0.8906 - loss: 0.3032 - val_accuracy: 0.8940 - val_loss: 0.2984\n", + "Epoch 3/5\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 23ms/step - accuracy: 0.9062 - loss: 0.2599 - val_accuracy: 0.8950 - val_loss: 0.2952\n", + "Epoch 4/5\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 23ms/step - accuracy: 0.9163 - loss: 0.2345 - val_accuracy: 0.8983 - val_loss: 0.2905\n", + "Epoch 5/5\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 23ms/step - accuracy: 0.9210 - loss: 0.2186 - val_accuracy: 0.8955 - val_loss: 0.2933\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8999 - loss: 0.3034\n", + "Test accuracy with Dropout: 0.8986\n", + "Testing without Dropout...\n", + "Epoch 1/5\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 15ms/step - accuracy: 0.8091 - loss: 0.5430 - val_accuracy: 0.8828 - val_loss: 0.3312\n", + "Epoch 2/5\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 15ms/step - accuracy: 0.8968 - loss: 0.2932 - val_accuracy: 0.8827 - val_loss: 0.3138\n", + "Epoch 3/5\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 15ms/step - accuracy: 0.9137 - loss: 0.2451 - val_accuracy: 0.8952 - val_loss: 0.2912\n", + "Epoch 4/5\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 15ms/step - accuracy: 0.9211 - loss: 0.2231 - val_accuracy: 0.8975 - val_loss: 0.2942\n", + "Epoch 5/5\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 15ms/step - accuracy: 0.9291 - loss: 0.1973 - val_accuracy: 0.9030 - val_loss: 0.2851\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9011 - loss: 0.2921\n", + "Test accuracy without Dropout: 0.8985\n", + "\n", + "Results for regularization experiment:\n", + "with Dropout: Test Accuracy: 0.8986\n", + "without Dropout: Test Accuracy: 0.8985\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "# B. Test presence or absence of regularization\n", + "from keras.layers import Dropout\n", + "\n", + "# Experimenting with Dropout regularization\n", + "dropout_rate = 0.25\n", + "results_regularization = {}\n", + "\n", + "# Model with Dropout\n", + "print(f\"Testing with Dropout (rate={dropout_rate})...\")\n", + "model_dropout = Sequential()\n", + "\n", + "# Using 32 filters based on previous experiment or as a starting point\n", + "model_dropout.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + "model_dropout.add(Flatten())\n", + "\n", + "# Adding Dropout layer\n", + "model_dropout.add(Dropout(dropout_rate))\n", + "\n", + "model_dropout.add(Dense(num_classes, activation='softmax'))\n", + "\n", + "model_dropout.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "history_dropout = model_dropout.fit(X_train, y_train_one_hot, epochs=5, batch_size=32, validation_split=0.1)\n", + "test_loss_dropout, test_acc_dropout = model_dropout.evaluate(X_test, y_test_one_hot)\n", + "\n", + "print(f\"Test accuracy with Dropout: {test_acc_dropout:.4f}\")\n", + "results_regularization['with Dropout'] = test_acc_dropout\n", + "\n", + "# Modeling without Dropout (Simple CNN as a comparison)\n", + "print(\"Testing without Dropout...\")\n", + "model_no_dropout = Sequential()\n", + "\n", + "# Same architecture as above\n", + "model_no_dropout.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + "model_no_dropout.add(Flatten())\n", + "model_no_dropout.add(Dense(num_classes, activation='softmax'))\n", + "\n", + "model_no_dropout.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "history_no_dropout = model_no_dropout.fit(X_train, y_train_one_hot, epochs=5, batch_size=32, validation_split=0.1)\n", + "test_loss_no_dropout, test_acc_no_dropout = model_no_dropout.evaluate(X_test, y_test_one_hot)\n", + "print(f\"Test accuracy without Dropout: {test_acc_no_dropout:.4f}\")\n", + "results_regularization['without Dropout'] = test_acc_no_dropout\n", + "\n", + "\n", + "# Printing summarized results\n", + "print(\"\\nResults for regularization experiment:\")\n", + "for regularization_type, accuracy in results_regularization.items():\n", + " print(f\"{regularization_type}: Test Accuracy: {accuracy:.4f}\")\n", + "\n", + "# Visualizing results\n", + "plt.figure(figsize=(6, 4))\n", + "plt.bar(range(len(results_regularization)), list(results_regularization.values()), align='center')\n", + "plt.xticks(range(len(results_regularization)), list(results_regularization.keys()))\n", + "plt.ylabel('Test Accuracy')\n", + "plt.title('CNN Performance with and without Dropout Regularization')\n", + "plt.ylim(0.85, 0.95)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cb426f26", + "metadata": { + "id": "cb426f26" + }, + "source": [ + "Reflection: Report on the performance of the models you tested. Did any of the changes you made improve the model's performance? If so, which ones? What do you think contributed to these improvements? Finally, what combination of hyperparameters and regularization techniques yielded the best performance?\n", + "\n", + "Hyperparameter Experiment (Number of Filters):\n", + "- Increasing the number of filters in the convolutional layer from 16 to 64 generally led to a slight improvement in test accuracy.\n", + "- The model with 64 filters had the highest accuracy among the values tested in this experiment (approximately 0.8983).\n", + "\n", + "\n", + "Regularization Experiment (Dropout):\n", + "- The model with Dropout (rate 0.25) had a test accuracy of approximately 0.8986, while the model without Dropout had a test accuracy of approximately 0.8985.\n", + "- The impact of Dropout on test accuracy appears minimal." + ] + }, + { + "cell_type": "markdown", + "id": "46c43a3d", + "metadata": { + "id": "46c43a3d" + }, + "source": [ + "# 5. Training Final Model and Evaluation\n", + "\n", + "In this section, you will train the final model using the best hyperparameters and regularization techniques you found in the previous section. You should:\n", + "- [ ] Compile the final model with the best hyperparameters and regularization techniques.\n", + "- [ ] Train the final model on the training set and evaluate it on the test set.\n", + "- [ ] Report the final model's performance on the test set, including accuracy and any other relevant metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31f926d1", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 901 + }, + "id": "31f926d1", + "outputId": "7aa38061-ff43-4fcb-8e35-40bbd2ebcd02" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 38ms/step - accuracy: 0.8134 - loss: 0.5285 - val_accuracy: 0.8843 - val_loss: 0.3175\n", + "Epoch 2/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 38ms/step - accuracy: 0.8955 - loss: 0.2882 - val_accuracy: 0.8922 - val_loss: 0.2995\n", + "Epoch 3/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 39ms/step - accuracy: 0.9106 - loss: 0.2493 - val_accuracy: 0.8978 - val_loss: 0.2857\n", + "Epoch 4/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 40ms/step - accuracy: 0.9219 - loss: 0.2166 - val_accuracy: 0.8992 - val_loss: 0.2913\n", + "Epoch 5/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 39ms/step - accuracy: 0.9294 - loss: 0.1955 - val_accuracy: 0.9020 - val_loss: 0.2864\n", + "Epoch 6/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 39ms/step - accuracy: 0.9379 - loss: 0.1700 - val_accuracy: 0.9010 - val_loss: 0.3012\n", + "Epoch 7/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 37ms/step - accuracy: 0.9433 - loss: 0.1604 - val_accuracy: 0.9045 - val_loss: 0.2953\n", + "Epoch 8/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 37ms/step - accuracy: 0.9468 - loss: 0.1474 - val_accuracy: 0.9015 - val_loss: 0.3205\n", + "Epoch 9/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 37ms/step - accuracy: 0.9515 - loss: 0.1332 - val_accuracy: 0.8987 - val_loss: 0.3235\n", + "Epoch 10/10\n", + "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 37ms/step - accuracy: 0.9554 - loss: 0.1259 - val_accuracy: 0.9023 - val_loss: 0.3234\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.8957 - loss: 0.3465\n", + "Final model test accuracy: 0.8995000123977661\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "# The model with 64 filters with Dropout (rate 0.25) had the highest test accuracy\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, Flatten, Conv2D, Dropout\n", + "import matplotlib.pyplot as plt\n", + "\n", + "final_model = Sequential()\n", + "final_model.add(Conv2D(64, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + "final_model.add(Flatten())\n", + "final_model.add(Dropout(0.25))\n", + "final_model.add(Dense(num_classes, activation='softmax'))\n", + "\n", + "final_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Train the final model\n", + "history_final = final_model.fit(X_train, y_train_one_hot, epochs=10, batch_size=32, validation_split=0.1)\n", + "\n", + "# Evaluate the final model\n", + "test_loss_final, test_acc_final = final_model.evaluate(X_test, y_test_one_hot)\n", + "print('Final model test accuracy:', test_acc_final)\n", + "\n", + "# Plotting loss for the final model\n", + "plt.plot(history_final.history['loss'], label='train')\n", + "plt.plot(history_final.history['val_loss'], label='validation')\n", + "plt.ylim(0, 2)\n", + "plt.legend(loc='best')\n", + "plt.title('Final Model Loss');\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "a01f8ebc", + "metadata": { + "id": "a01f8ebc" + }, + "source": [ + "Reflection: How does the final model's performance compare to the baseline and the CNN model? What do you think contributed to the final model's performance? If you had time, what other experiments would you run to further improve the model's performance?\n", + "\n", + "Results:\n", + "- Baseline Model: 25.9%\n", + "- Simple CNN Model: 89.9%\n", + "- Final Model (with 64 filters and Dropout): 90.0%\n", + "\n", + "The final model shows improvement in test accuracy compared to the simple CNN and a significant improvement over the baseline model.\n", + "\n", + "Contributing factors:\n", + "- 64 filters extracted richer edges and textures improving class separation.\n", + "- Dropout(0.25) provided regularization balancing overfitting and underfitting.\n", + "- Adam optimizer offered effective adaptive learning improving convergence and stability.\n", + "\n", + "Potential improvments (Future experiments):\n", + "- Early stopping: Looking at the plots from the training history I can see that the training loss continues to decrease throughout the 10 epochs but the validation loss starts to level off and even slightly increase after around epoch 7 or 8 which suggests that the model might be starting to overfit the training data. One consideration is stopping the training earlier at around epoch 7 or 8 where the validation loss was at its lowest point could potentially lead to a model that generalizes better to new data and might have resulted in a slightly higher test accuracy.\n", + "\n", + "- Dropout rates: Trying a higher dropout rate instead of 0.25 could potentially help further mitigate overfitting forceing the network to learn more features that are not dependent on any single neuron which can improve generalization to unseen data.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "01db8512", + "metadata": { + "id": "01db8512" + }, + "source": [ + "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n", + "### Submission Parameters:\n", + "* Submission Due Date: `23:59 PM - 26/10/2025`\n", + "* The branch name for your repo should be: `assignment-1`\n", + "* What to submit for this assignment:\n", + " * This Jupyter Notebook (assignment_1.ipynb)\n", + " * The Lab 1 notebook (labs/lab_1.ipynb)\n", + " * The Lab 2 notebook (labs/lab_2.ipynb)\n", + " * The Lab 3 notebook (labs/lab_3.ipynb)\n", + "* What the pull request link should look like for this assignment: `https://github.com//deep_learning/pull/`\n", + "* Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n", + "Checklist:\n", + "- [ ] Created a branch with the correct naming convention.\n", + "- [ ] Ensured that the repository is public.\n", + "- [ ] Reviewed the PR description guidelines and adhered to them.\n", + "- [ ] Verify that the link is accessible in a private browser window.\n", + "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-7-help-ml`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "deep_learning", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 24c141f689d8f6030758f546aed1cb564cc15654 Mon Sep 17 00:00:00 2001 From: Vincent Van Schaik <193274586+VincentSchaik@users.noreply.github.com> Date: Sun, 2 Nov 2025 13:37:20 -0500 Subject: [PATCH 03/16] Delete 02_activities/assignments/assignment_2.ipynb Error rendering your Notebook. --- 02_activities/assignments/assignment_2.ipynb | 4561 ------------------ 1 file changed, 4561 deletions(-) delete mode 100644 02_activities/assignments/assignment_2.ipynb diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb deleted file mode 100644 index 711d37c7d..000000000 --- a/02_activities/assignments/assignment_2.ipynb +++ /dev/null @@ -1,4561 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "197fbb2d", - "metadata": { - "id": "197fbb2d" - }, - "source": [ - "# Assignment 2 – Zero-Shot Image Classification with Transformers\n", - "\n", - "In this assignment, you will apply a pre-trained vision–language transformer (e.g. CLIP) to perform **zero-shot** classification on the Fashion-MNIST dataset—classifying each image without any task-specific training. You will build on the concepts from Assignment 1 by comparing this “off-the-shelf” approach to the CNN you previously trained.\n", - "\n", - "You will:\n", - "1. **Load** the Fashion-MNIST images using PyTorch instead of Keras.\n", - "2. **Run a zero-shot baseline** with simple text prompts to set a performance reference.\n", - "3. **Engineer improved prompts** and measure the resulting accuracy gains.\n", - "4. **Visualise image embeddings** with UMAP to inspect class separability.\n", - "5. **Conduct one mini-experiment** of your choice.\n", - "6. **Summarise findings** and reflect on strengths and weaknesses of zero-shot transformers versus a trained CNN.\n", - "\n", - "# 1. Loading the Fashion-MNIST Dataset\n", - "\n", - "As in assignment 1, we'll load the Fashion-MNIST dataset, but this time using `torchvision.datasets` to ensure compatibility with the `transformers` library. We will also load our model and processor from the `transformers` library.\n", - "\n", - "The transformers library allows us to use pre-trained models like CLIP, which can perform zero-shot classification by leveraging the text prompts we provide. There are two key objects we will use: the `CLIPModel` for the model itself and the `CLIPProcessor` for preparing our images and text prompts.\n", - "\n", - "Since we are not actually training a model in this assignment, we will set the CLIP model to evaluation mode. If the model is designed to utilize features like dropout or batch normalization, setting it to evaluation mode ensures that these features behave correctly during inference (prediction). Setting the model to evaluaton mode also tells PyTorch that we don't have to compute gradients, which can save memory and speed up inference.\n", - "\n", - "In order to speed up processing, we will also move the model to an \"accelerator\" if available. This is typically a GPU, but modern MacBooks also have an \"Apple Silicon\" accelerator that can be used for inference, called MPS (Metal Performance Shaders). If you are using a MacBook with Apple Silicon, you can use the MPS device for faster processing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "026463ce", - "metadata": { - "id": "026463ce" - }, - "outputs": [], - "source": [ - "# Uncomment and run if required\n", - "#!pip install transformers torchvision torch accelerate" - ] - }, - { - "cell_type": "code", - "source": [ - "# Installing cuml\n", - "!pip install cuml-cu12 --extra-index-url=https://pypi.nvidia.com" - ], - "metadata": { - "id": "UUQ0KHWYxwaj", - "outputId": "2f284dfc-d49f-4c40-a1bf-1e518d6b8e42", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "id": "UUQ0KHWYxwaj", - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://pypi.nvidia.com\n", - "Requirement already satisfied: cuml-cu12 in /usr/local/lib/python3.12/dist-packages (25.6.0)\n", - "Requirement already satisfied: cuda-python<13.0a0,>=12.6.2 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (12.6.2.post1)\n", - "Requirement already satisfied: cudf-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: cupy-cuda12x>=12.0.0 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (13.3.0)\n", - "Requirement already satisfied: cuvs-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.1)\n", - "Requirement already satisfied: dask-cuda==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: dask-cudf-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (1.5.2)\n", - "Requirement already satisfied: libcuml-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: numba<0.62.0a0,>=0.59.1 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (0.60.0)\n", - "Requirement already satisfied: numpy<3.0a0,>=1.23 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (2.0.2)\n", - "Requirement already satisfied: nvidia-cublas-cu12 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (12.6.4.1)\n", - "Requirement already satisfied: nvidia-cufft-cu12 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (11.3.0.4)\n", - "Requirement already satisfied: nvidia-curand-cu12 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (10.3.7.77)\n", - "Requirement already satisfied: nvidia-cusolver-cu12 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (11.7.1.2)\n", - "Requirement already satisfied: nvidia-cusparse-cu12 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (12.5.4.2)\n", - "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.0)\n", - "Requirement already satisfied: pylibraft-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: raft-dask-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: rapids-dask-dependency==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: rmm-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: scikit-learn>=1.5 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (1.6.1)\n", - "Requirement already satisfied: scipy>=1.8.0 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (1.16.3)\n", - "Requirement already satisfied: treelite==4.4.1 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (4.4.1)\n", - "Requirement already satisfied: cachetools in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (5.5.2)\n", - "Requirement already satisfied: fsspec>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (2025.3.0)\n", - "Requirement already satisfied: libcudf-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: numba-cuda<0.12.0a0,>=0.11.0 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (0.11.0)\n", - "Requirement already satisfied: nvidia-cuda-nvcc-cu12 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (12.5.82)\n", - "Requirement already satisfied: nvidia-cuda-nvrtc-cu12 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (12.6.77)\n", - "Requirement already satisfied: nvtx>=0.2.1 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (0.2.13)\n", - "Requirement already satisfied: pandas<2.2.4dev0,>=2.0 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (2.2.2)\n", - "Requirement already satisfied: pyarrow<20.0.0a0,>=14.0.0 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (18.1.0)\n", - "Requirement already satisfied: pylibcudf-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: pynvjitlink-cu12 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (0.7.0)\n", - "Requirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (13.9.4)\n", - "Requirement already satisfied: typing_extensions>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (4.15.0)\n", - "Requirement already satisfied: libcuvs-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuvs-cu12==25.6.*->cuml-cu12) (25.6.1)\n", - "Requirement already satisfied: click>=8.1 in /usr/local/lib/python3.12/dist-packages (from dask-cuda==25.6.*->cuml-cu12) (8.3.0)\n", - "Requirement already satisfied: pynvml<13.0.0a0,>=12.0.0 in /usr/local/lib/python3.12/dist-packages (from dask-cuda==25.6.*->cuml-cu12) (12.0.0)\n", - "Requirement already satisfied: zict>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from dask-cuda==25.6.*->cuml-cu12) (3.0.0)\n", - "Requirement already satisfied: libraft-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from libcuml-cu12==25.6.*->cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: rapids-logger==0.1.* in /usr/local/lib/python3.12/dist-packages (from libcuml-cu12==25.6.*->cuml-cu12) (0.1.19)\n", - "Requirement already satisfied: distributed-ucxx-cu12==0.44.* in /usr/local/lib/python3.12/dist-packages (from raft-dask-cu12==25.6.*->cuml-cu12) (0.44.0)\n", - "Requirement already satisfied: nvidia-nccl-cu12>=2.19 in /usr/local/lib/python3.12/dist-packages (from raft-dask-cu12==25.6.*->cuml-cu12) (2.27.3)\n", - "Requirement already satisfied: ucx-py-cu12==0.44.* in /usr/local/lib/python3.12/dist-packages (from raft-dask-cu12==25.6.*->cuml-cu12) (0.44.0)\n", - "Requirement already satisfied: dask==2025.5.0 in /usr/local/lib/python3.12/dist-packages (from rapids-dask-dependency==25.6.*->cuml-cu12) (2025.5.0)\n", - "Requirement already satisfied: distributed==2025.5.0 in /usr/local/lib/python3.12/dist-packages (from rapids-dask-dependency==25.6.*->cuml-cu12) (2025.5.0)\n", - "Requirement already satisfied: librmm-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from rmm-cu12==25.6.*->cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: cloudpickle>=3.0.0 in /usr/local/lib/python3.12/dist-packages (from dask==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (3.1.1)\n", - "Requirement already satisfied: partd>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from dask==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (1.4.2)\n", - "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.12/dist-packages (from dask==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (6.0.3)\n", - "Requirement already satisfied: toolz>=0.10.0 in /usr/local/lib/python3.12/dist-packages (from dask==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (0.12.1)\n", - "Requirement already satisfied: jinja2>=2.10.3 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (3.1.6)\n", - "Requirement already satisfied: locket>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (1.0.0)\n", - "Requirement already satisfied: msgpack>=1.0.2 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (1.1.2)\n", - "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (5.9.5)\n", - "Requirement already satisfied: sortedcontainers>=2.0.5 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (2.4.0)\n", - "Requirement already satisfied: tblib>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (3.2.0)\n", - "Requirement already satisfied: tornado>=6.2.0 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (6.5.1)\n", - "Requirement already satisfied: urllib3>=1.26.5 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (2.5.0)\n", - "Requirement already satisfied: ucxx-cu12==0.44.* in /usr/local/lib/python3.12/dist-packages (from distributed-ucxx-cu12==0.44.*->raft-dask-cu12==25.6.*->cuml-cu12) (0.44.0)\n", - "Requirement already satisfied: libkvikio-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from libcudf-cu12==25.6.*->cudf-cu12==25.6.*->cuml-cu12) (25.6.0)\n", - "Requirement already satisfied: libucx-cu12<1.19,>=1.15.0 in /usr/local/lib/python3.12/dist-packages (from ucx-py-cu12==0.44.*->raft-dask-cu12==25.6.*->cuml-cu12) (1.18.1)\n", - "Requirement already satisfied: libucxx-cu12==0.44.* in /usr/local/lib/python3.12/dist-packages (from ucxx-cu12==0.44.*->distributed-ucxx-cu12==0.44.*->raft-dask-cu12==25.6.*->cuml-cu12) (0.44.0)\n", - "Requirement already satisfied: fastrlock>=0.5 in /usr/local/lib/python3.12/dist-packages (from cupy-cuda12x>=12.0.0->cuml-cu12) (0.8.3)\n", - "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.12/dist-packages (from numba<0.62.0a0,>=0.59.1->cuml-cu12) (0.43.0)\n", - "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=1.5->cuml-cu12) (3.6.0)\n", - "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.12/dist-packages (from nvidia-cufft-cu12->cuml-cu12) (12.6.85)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<2.2.4dev0,>=2.0->cudf-cu12==25.6.*->cuml-cu12) (2.9.0.post0)\n", - "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<2.2.4dev0,>=2.0->cudf-cu12==25.6.*->cuml-cu12) (2025.2)\n", - "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas<2.2.4dev0,>=2.0->cudf-cu12==25.6.*->cuml-cu12) (2025.2)\n", - "Requirement already satisfied: nvidia-ml-py<13.0.0a0,>=12.0.0 in /usr/local/lib/python3.12/dist-packages (from pynvml<13.0.0a0,>=12.0.0->dask-cuda==25.6.*->cuml-cu12) (12.575.51)\n", - "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->cudf-cu12==25.6.*->cuml-cu12) (4.0.0)\n", - "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->cudf-cu12==25.6.*->cuml-cu12) (2.19.2)\n", - "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2>=2.10.3->distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (3.0.3)\n", - "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->cudf-cu12==25.6.*->cuml-cu12) (0.1.2)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas<2.2.4dev0,>=2.0->cudf-cu12==25.6.*->cuml-cu12) (1.17.0)\n" - ] - } - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b4701b15", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 450, - "referenced_widgets": [ - "3750ae40cd39499ab50991df5946d2c4", - "45f50bbda4034dee9e57c6ea0679ced6", - "b50a1290227a4270915f679dbb5628f1", - "dadfc824823d42df8b240ecf6565ea34", - "e3bfa3e2b1e54a488527ff0ae3273df4", - "85e6a4ec07da45deade08d16cc6eef61", - "f7d9356562064c31acab1b6bb00b7a69", - "b7088f8adb6d4e0db3bf112a11bfb2fe", - "44d8945ab43c456093a1a2c25e020463", - "91236763ae8a481982409ca37aabfdc6", - "ca0b9f0b91544d98bce5968b606f2fbb", - "4d9a3ea5e7054175870ed1827cb11aff", - "4f3c81150aff4f1bb51a108b8d66179a", - "0caba3aa88fb44a4b449f1deff0e77f7", - "e2df6d112d4b47eb845fa7a9f78cf821", - "86de6d4aa4bc4e14a4ec52969739d6ca", - "6f24ebc5ab6f46c2b380070325c893d7", - "b616cab018e646e2b138ad3b746a6616", - "915793ec7e324a4d8cf6d085e0c161c6", - "d1f277973f0f4bd1a013009e6fb296b1", - "11b7bb4b6eef4f6d89f853792342c126", - "477a3264053345ed84e0e1c2446409bf", - "dedc22fcd16d4f6d8d1f58f2a587b8db", - "a45f0dccae9340a185d58ddeb9209039", - "680d40e1dc86411d8da97903d6386d9b", - "a4af94e1834747dd8e523e8b3025608b", - "7c76cd40a8584d4d876c027c972060fd", - "02bb96f01b524da68d893596f26f609b", - "16c9e8b36d6042348a7e91aa85ee9ee2", - "f073119ca5c94362897eda2f2c245e9f", - "a9cef28515db48c8975a809b3d35e174", - "23c727feada1401e902a9c3056267376", - "9d1ab73f90c44d0eb0833ee96b57ed38", - "d6547cdb382c40d8beff462d009bdae0", - "73816aeaaa8847f084e8cad15cd0d957", - "270989562a5c4f1d835b0b5da15aed86", - "534906f008474c2090930d058a261a37", - "2289e8cf8dbf499aa8e778b1aa88dbac", - "a8cbcd4b56144be0bbfe320eb581c9cf", - "21bdb4ecd8274ec9b67f1281d6383529", - "1db6eca3754d4754bdf68a06d49754ed", - "d073e859935849e0800a7d4ede2fb8ec", - "91f734f319f54df3b3a6b396337bbfac", - "4bbbf1e5d5c4490695b42526f4cf5269", - "b94cb23cc9f546c2b1e370ed3fd16e33", - "1f247f8d0b064ad6a8907b882eb21a13", - "7ff2d4d5d76b4bf98d5185063c113ef4", - "c6dcb311005b42e98d12ae53d8139600", - "3e0a0055fdb64ba5864cb9db5071a6ac", - "ba2bfc83dfa44868a4119d55cc0400bd", - "9b7d4215bda64c189f8f7ca90e80072c", - "b158d65ffb754442a11b1906bfa09c38", - "e4c1a8e9d7624d2ba97c5158eebb6bdb", - "64a8b8cb2a6d4feabb26b8b9b1cbfeaf", - "b01c58c8d7f2443e8305834526355a0c", - "d2c1e60c41844a18b82f0cb28880b916", - "be4390e933dc45b8a4d7b1ebea712aa3", - "2558716cf8c8437590a8594e6e5d5e98", - "afd3848573814a008ba6f8f3e906bf6b", - "087cb104dc444f6484923bcdb567b8fa", - "01c9984f7d7e46129f7f26a79dd17755", - "992513aa0e9c4baa96cb5191e045a082", - "b0090c94753d41dabd9ac2dd423e9201", - "41039a29e2e74c95a8af8f6e1b2ffb11", - "d09ba93de344459f99b09f5f5ac37f3e", - "09962b3ebab64b1f8dfafae83a294cc2", - "17fc66ed0df348cf92223bfeae38fbc0", - "240ea684bf644ad39fa1dbb9df4349f1", - "6f8ade98160e4143989bda894b0a170e", - "2ae17bbf2db2418bb0a1552a35485b09", - "1825818985064b028a4be6220958086c", - "f53717f5ffa547ae812e5ac208c2064a", - "c83df788a5ca47a2af0434b9b9696ef8", - "2e823e9f707a4497b52a39f8085167e5", - "36b753ceca714ee09786fd806939af01", - "93a4b3cb88eb41659d0f2b0761d70426", - "b710ea11c6ca49e39b28423b2e7de06d", - "e370bffadf8a444eb0083b3337e19579", - "a2fdd5e9d6964b18b2064f3ead1cb60a", - "84238eff2dfd48d4aebc73a5c057b871", - "062f69c14c2e459ba6dd79cf96e36213", - "00e03185b187495688cfbb21de2aa0d1", - "72c526138af3440d93bac8a5db9f72a5", - "902061e0c6b147ddb9e7c0c28b68b045", - "346e74103d7d4b0abca523841fad013a", - "f0f93bbdbc494d89bcf54751f4615abd", - "c954d22b66e4457cb1c661ed7b7c8ef8", - "f9df48ee9bb7405984197bbf584b88f1", - "6e5548d364044237985c6eea263828ca", - "cfc02aade9e34d96b4c031cd3487888a", - "ac86c3f3f5f04dd08d9044a3ad31e061", - "919a3d32344d4647ae0656b5682cccf2", - "dcec9feb3df64446822c89184dc13f0f", - "58d9ee30d6b04072a71c39b4b1ab0a2a", - "8ce69d8b62894c55b1940b9f2e90ae4a", - "f928225848a84bc7a90a29e48ac7c0e0", - "037f8bbdd54e4cbf9b530b4a09e0b1d5", - "fe462e23b1e449889b1544c8ef540bb0", - "e022787742b340d49474e2659755ce37" - ] - }, - "id": "b4701b15", - "outputId": "fa28abf3-6d42-498c-ace4-ed3f2356bde3" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "config.json: 0.00B [00:00, ?B/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "3750ae40cd39499ab50991df5946d2c4" - } - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "pytorch_model.bin: 0%| | 0.00/605M [00:00" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Display the first batch of images from `test_loader`\n", - "\n", - "def show_batch(loader):\n", - " images, labels = next(iter(loader))\n", - " images = images.cpu() # Move images to CPU for plotting\n", - " # Renormalize to [0, 1] for visualization\n", - " images = (images - images.min()) / (images.max() - images.min())\n", - " _, axes = plt.subplots(1, len(images), figsize=(15, 5))\n", - " for ax, img, label in zip(axes, images, labels):\n", - " ax.imshow(img.permute(1, 2, 0))\n", - " ax.set_title(CLASS_NAMES[label.item()])\n", - " ax.axis('off')\n", - " plt.show()\n", - "\n", - "show_batch(test_loader)" - ] - }, - { - "cell_type": "markdown", - "id": "e1183726", - "metadata": { - "id": "e1183726" - }, - "source": [ - "We’re now ready to run our zero-shot classification baseline!\n", - "\n", - "# Brief Introduction to Zero-Shot Classification\n", - "\n", - "In Assignment 1, we followed the typical machine-learning pipeline: we trained a CNN on the Fashion-MNIST dataset, using labelled examples to update the model’s weights. While effective, that approach requires a curated, task-specific training set—a luxury you don’t always have in practice.\n", - "\n", - "Zero-shot classification flips the script. A large vision–language model (VLM) such as **CLIP** is first pre-trained on hundreds of millions of image–text pairs scraped from the web. Because it learns *joint* visual–textual embeddings, the model can later solve new tasks simply by “measuring” how similar an image is to a **text prompt** that describes each candidate class—without seeing a single task-labelled example.\n", - "\n", - "**How it works** \n", - "1. Feed an image through CLIP’s vision encoder → **image feature**. \n", - "2. Feed a textual prompt (e.g. “a photo of a sandal”) through CLIP’s text encoder → **text feature**. \n", - "3. Compute cosine similarity between the image feature and every class’s text feature. \n", - "4. Pick the class whose prompt is most similar.\n", - "\n", - "For our first attempt, we’ll use the bare class names as prompts, e.g.:\n", - "\n", - "- \"T-shirt/top\"\n", - "- \"Trouser\"\n", - "\n", - "### You should:\n", - "\n", - "- [ ] Build embeddings: use the `get_text_embeddings` helper function to create text embeddings for the class names.\n", - "- [ ] Run inference: use the `get_image_embeddings` helper function to create image embeddings.\n", - "- [ ] Compute cosine similarity: complete and use the `get_cosine_similarity` helper function to compute the cosine similarity between the image and text embeddings.\n", - "- [ ] Make predictions: use the `get_predictions` helper function to get the predicted class for each image in the batch.\n", - "\n", - "Note that for normalized vectors like the ones we are using, cosine similarity is equivalent to the dot product. This means we can use the handy formula `cosine_similarity = vector_a @ vector_b.T` to compute the similarity between the image and text embeddings." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4c3acc29", - "metadata": { - "id": "4c3acc29" - }, - "outputs": [], - "source": [ - "def get_text_embeddings(class_names: list[str]) -> torch.Tensor:\n", - " \"\"\" Get text embeddings for the given class names using CLIP.\n", - " Args:\n", - " class_names (list[str]): List of class names to encode.\n", - " Returns:\n", - " torch.Tensor: Normalized text embeddings for the class names.\n", - " \"\"\"\n", - " tokenized = clip_processor(text=class_names,\n", - " padding=True,\n", - " return_tensors=\"pt\").to(device)\n", - "\n", - " with torch.no_grad():\n", - " text_embeddings = clip_model.get_text_features(**tokenized)\n", - "\n", - " text_feats = text_embeddings / text_embeddings.norm(dim=-1, keepdim=True)\n", - "\n", - " return text_feats\n", - "\n", - "def get_image_embeddings(images: torch.Tensor) -> torch.Tensor:\n", - " \"\"\" Get image embeddings for the given images using CLIP.\n", - " Args:\n", - " images (torch.Tensor): Batch of images to encode.\n", - " Returns:\n", - " torch.Tensor: Normalized image embeddings for the images.\n", - " \"\"\"\n", - " with torch.no_grad():\n", - " image_embeddings = clip_model.get_image_features(pixel_values=images)\n", - "\n", - " image_feats = image_embeddings / image_embeddings.norm(dim=-1, keepdim=True)\n", - "\n", - " return image_feats" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "16653654", - "metadata": { - "id": "16653654" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "def get_cosine_similarity(image_feats: torch.Tensor, text_feats: torch.Tensor) -> np.ndarray:\n", - " \"\"\"\n", - " Compute cosine similarity between image features and text features.\n", - " Args:\n", - " image_feats (torch.Tensor): Image features of shape (N, D).\n", - " text_feats (torch.Tensor): Text features of shape (M, D).\n", - " Returns:\n", - " numpy.ndarray: Cosine similarity matrix of shape (N, M), where N is the number of images and M is the number of text prompts.\n", - " \"\"\"\n", - " image_feats = image_feats.cpu() # Ensure image features are on CPU\n", - " text_feats = text_feats.cpu() # Ensure text features are on CPU\n", - "\n", - " # Compute cosine similarity, which is the dot product of normalized vectors\n", - " return image_feats @ text_feats.T\n", - "\n", - "def get_predictions(similarity: np.ndarray) -> np.ndarray:\n", - " \"\"\"\n", - " Get predictions based on cosine similarity scores.\n", - " Args:\n", - " similarity (numpy.ndarray): Cosine similarity matrix of shape (N, M), where N is the number of images and M is the number of text prompts.\n", - " Returns:\n", - " numpy.ndarray: Predicted class indices for each image, shape (N,).\n", - " \"\"\"\n", - " # Get the index of the maximum similarity for each image\n", - " return np.argmax(similarity, axis=1)\n" - ] - }, - { - "cell_type": "markdown", - "id": "55d9e1de", - "metadata": { - "id": "55d9e1de" - }, - "source": [ - "With these functions complete, you are ready to run the zero-shot classification baseline. Complete the code to follow these steps:\n", - "\n", - "- [ ] Build text embeddings for the class names using the `get_text_embeddings` function (this only needs to be done once).\n", - "- [ ] For each batch of images:\n", - " - [ ] Get image embeddings using the `get_image_embeddings` function.\n", - " - [ ] Compute cosine similarity between the image and text embeddings using the `get_cosine_similarity` function.\n", - " - [ ] Save the predictions so that we can build a confusion matrix later.\n", - "- [ ] Report the accuracy of the predictions and the confusion matrix using the `accuracy_score` and `confusion_matrix` functions from `sklearn.metrics`." - ] - }, - { - "cell_type": "code", - "source": [ - "from sklearn.metrics import accuracy_score, confusion_matrix\n", - "\n", - "y_true, y_pred = [], []\n", - "\n", - "# Build text embeddings for the class names\n", - "text_features = get_text_embeddings(CLASS_NAMES)\n", - "\n", - "for pixel_values, labels in test_loader:\n", - " # Get image embeddings\n", - " image_features = get_image_embeddings(pixel_values)\n", - "\n", - " # Compute cosine similarity\n", - " similarity = get_cosine_similarity(image_features, text_features)\n", - "\n", - " # Get predictions\n", - " predictions = get_predictions(similarity)\n", - "\n", - " # Save the predictions and true labels\n", - " y_true.extend(labels.tolist())\n", - " y_pred.extend(predictions.tolist())\n", - "\n", - "# Report the accuracy of the predictions\n", - "accuracy = accuracy_score(y_true, y_pred)\n", - "print(f\"Accuracy: {accuracy:.4f}\")\n", - "\n", - "# Report the confusion matrix\n", - "def plot_confusion_matrix(y_true, y_pred, class_names):\n", - " cm = confusion_matrix(y_true, y_pred)\n", - " plt.figure(figsize=(10, 8))\n", - " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", - " plt.title('Confusion Matrix')\n", - " plt.colorbar()\n", - " tick_marks = np.arange(len(class_names))\n", - " plt.xticks(tick_marks, class_names, rotation=45)\n", - " plt.yticks(tick_marks, class_names)\n", - " plt.ylabel('True label')\n", - " plt.xlabel('Predicted label')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "plot_confusion_matrix(y_true, y_pred, CLASS_NAMES)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 825 - }, - "id": "67jlM9d3nXqL", - "outputId": "131bd7bd-0a45-4e58-d305-2006484a365c" - }, - "id": "67jlM9d3nXqL", - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy: 0.6240\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "id": "0857f4a8", - "metadata": { - "id": "0857f4a8" - }, - "source": [ - "Reflection: Consider the results. How does the performance of this zero-shot baseline compare to the CNN you trained in Assignment 1? What are the strengths and weaknesses of this approach?" - ] - }, - { - "cell_type": "markdown", - "source": [ - "Reflection: The accurtacy of the trained CNN baseline in Assignment-1 was 0.25% compared to this zero-shot CLIP model baseline accuracy of 62.40%. This is dramatically better model.\n", - "\n", - "Strength: The zero-shot model required no task-specific training and is off-the-shelf and trained on learned concepts and not just 10 categories.\n", - "\n", - "Weakness: 62.4% is not perfect. The confusion matrix shows it still struggles to tell similar items apart like \"Shirt\" and \"T-shirt/top\" or \"Sandal\" and \"Sneaker\"." - ], - "metadata": { - "id": "17X6qPx3apKI" - }, - "id": "17X6qPx3apKI" - }, - { - "cell_type": "markdown", - "id": "599e16af", - "metadata": { - "id": "599e16af" - }, - "source": [ - "## Improving Zero-Shot Classification with Prompt Engineering\n", - "\n", - "In the previous section, we directly used the class names as text prompts for zero-shot classification. However, we can often improve performance by crafting more descriptive prompts that better capture the visual characteristics of each class. For example, instead of just \"T-shirt/top\", we could use \"a photo of a T-shirt\" or \"a photo of a top\". This additional context can help the model make more accurate predictions.\n", - "\n", - "In this section, we will experiment with more detailed prompts for each class to see if we can improve the zero-shot classification performance. You should:\n", - "\n", - "- [ ] Create a list of improved prompts for each class. For example, instead of just \"T-shirt/top\", you could use \"a photo of a T-shirt\" or \"a photo of a top\".\n", - "- [ ] Use the `get_text_embeddings` function to create text embeddings for the improved prompts.\n", - "- [ ] Run the zero-shot classification baseline again using the improved prompts and report the accuracy and confusion matrix.\n", - "\n", - "Note: Take advantage of the confusion matrix above. If two classes are often confused, consider how you might improve the prompts to help the model distinguish them better.\n", - "\n", - "The aim for this section is for you to improve the performance of the model. However, if you find that the performance does not improve significantly, you can still reflect on the process and consider how you might further refine the prompts with more effort." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5abf5cc1", - "metadata": { - "id": "5abf5cc1", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 825 - }, - "outputId": "170eae40-9229-4017-98ca-ce49b446feb2" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy with improved prompts: 0.6477\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "# Improved prompts\n", - "improved_CLASS_NAMES = [\n", - " # Baseline prompt:\n", - " # \"T-shirt/top\",\n", - " # \"Trouser\",\n", - " # \"Pullover\",\n", - " # \"Dress\",\n", - " # \"Coat\",\n", - " # \"Sandal\",\n", - " # \"Shirt\",\n", - " # \"Sneaker\",\n", - " # \"Bag\",\n", - " # \"Ankle boot\"\n", - "\n", - " #Improved prompt:\n", - "\n", - " \"a photo of a casual short-sleeve T-shirt\", # 0: T-shirt/top Confused with Shirt, Pullover, Coat\n", - " \"a photo of a pair of trousers or pants\", # 1: Trouser Generally distinct\n", - " \"a photo of a long-sleeve knit pullover sweater\", # 2: Pullover Confused with T-shirt/top\n", - " \"a photo of a one-piece dress\", # 3: Dress Generally distinct\n", - " \"a photo of a heavy outerwear coat\", # 4: Coat Confused with Shirt\n", - " \"a photo of an open-toed sandal\", # 5: Sandal Confused with Sneaker, Ankle boot\n", - " \"a photo of a collared, button-down shirt\", # 6: Shirt Confused with T-shirt/top, Coat\n", - " \"a photo of an athletic lace-up sneaker\", # 7: Sneaker Confused with Sandal, Ankle boot\n", - " \"a photo of a handbag or purse\", # 8: Bag Generally distinct\n", - " \"a photo of a boot that covers the ankle\" # 9: Ankle boot Confused with Sneaker, Sandal\n", - "]\n", - "\n", - "y_true_improved, y_pred_improved = [], []\n", - "\n", - "# Build text embeddings for the improved class names\n", - "text_features_improved = get_text_embeddings(improved_CLASS_NAMES)\n", - "\n", - "for pixel_values, labels in test_loader:\n", - " # Get image embeddings\n", - " image_features_improved = get_image_embeddings(pixel_values)\n", - "\n", - " # Compute cosine similarity\n", - " similarity_improved = get_cosine_similarity(image_features_improved, text_features_improved)\n", - "\n", - " # Get predictions\n", - " predictions_improved = get_predictions(similarity_improved)\n", - "\n", - " # Save the predictions and true labels\n", - " y_true_improved.extend(labels.tolist())\n", - " y_pred_improved.extend(predictions_improved.tolist())\n", - "\n", - "# Report the accuracy of the predictions\n", - "accuracy_improved = accuracy_score(y_true_improved, y_pred_improved)\n", - "print(f\"Accuracy with improved prompts: {accuracy_improved:.4f}\")\n", - "\n", - "# Report the confusion matrix\n", - "plot_confusion_matrix(y_true_improved, y_pred_improved, CLASS_NAMES)" - ] - }, - { - "cell_type": "markdown", - "id": "28e88284", - "metadata": { - "id": "28e88284" - }, - "source": [ - "Reflection: How did your detailed prompts affect the zero-shot classification performance? Did you see a significant improvement compared to the baseline? What insights did you gain about the model's understanding of the classes? Do you think that with more effort you could further improve the performance? If so, how?" - ] - }, - { - "cell_type": "markdown", - "source": [ - "The accuracy with the improved prompts is 64.77% which is a slight improvement compared to the baseline accuracy of 62.40%.\n", - "\n", - "The confusion matrix shows some changes in how the model is predicting the classes. For instance, the confusion between \"T-shirt/top\" and \"Shirt\" seems to have decreased slightly compared to the baseline. Also the confusion between \"Sandal\" and \"Sneaker\" also appears to be reduced.\n", - "\n", - "The improved prompts have helped the model distinguish between some of the more confusable classes. However, there is still room for improvement, and some confusions, like those between \"Ankel boot\" and \"Sneaker\", or \"Coat\" and \"Pullover\" still persist.\n", - "\n", - "Improvments: Multiple-Description Classification (suggested Mini-Experiment below). Instead of one prompt per class we create multiple descriptive prompts for each." - ], - "metadata": { - "id": "Ya8Lzu_eodso" - }, - "id": "Ya8Lzu_eodso" - }, - { - "cell_type": "markdown", - "id": "e817d7b4", - "metadata": { - "id": "e817d7b4" - }, - "source": [ - "## Visualizing Image Embeddings with UMAP\n", - "\n", - "To better understand how the model perceives the different classes, we can visualize the image embeddings using UMAP (Uniform Manifold Approximation and Projection). UMAP is a dimensionality reduction technique that helps us see how similar or dissimilar the embeddings are in a lower-dimensional space.\n", - "\n", - "By visualizing the embeddings, we can gain insights into how well the model can distinguish certain images, even without considering the text prompts. This can help us identify clusters of similar images and see if there are any overlaps between classes.\n", - "\n", - "You should:\n", - "\n", - "- [ ] Use the `get_image_embeddings` function to get the image embeddings for the entire test set.\n", - "- [ ] Use UMAP to reduce the dimensionality of the image embeddings to 2D.\n", - "- [ ] Plot the 2D embeddings, coloring each point by its true class label.\n", - "\n", - "You may need to install the `umap-learn` library if you haven't already. You can do this by running `pip install umap-learn`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a7a20757", - "metadata": { - "id": "a7a20757", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "651b63be-6d83-4329-93a3-9cfbde948757" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: umap-learn in /usr/local/lib/python3.12/dist-packages (0.5.9.post2)\n", - "Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (2.0.2)\n", - "Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (1.16.3)\n", - "Requirement already satisfied: scikit-learn>=1.6 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (1.6.1)\n", - "Requirement already satisfied: numba>=0.51.2 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (0.60.0)\n", - "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (0.5.13)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from umap-learn) (4.67.1)\n", - "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.12/dist-packages (from numba>=0.51.2->umap-learn) (0.43.0)\n", - "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.12/dist-packages (from pynndescent>=0.5->umap-learn) (1.5.2)\n", - "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=1.6->umap-learn) (3.6.0)\n" - ] - } - ], - "source": [ - "# Uncomment the following line to install UMAP if you haven't already\n", - "!pip install umap-learn" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e5fd3b4b", - "metadata": { - "id": "e5fd3b4b" - }, - "outputs": [], - "source": [ - "from umap import UMAP\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# ------------------------------------------------------------\n", - "# 1. Collect image embeddings\n", - "# ------------------------------------------------------------\n", - "all_img_emb = []\n", - "all_labels = []\n", - "\n", - "with torch.no_grad():\n", - " for pixel_values, labels in test_loader:\n", - " image_features = get_image_embeddings(pixel_values.to(device))\n", - " all_img_emb.append(image_features.cpu().numpy())\n", - " all_labels.append(labels.numpy())\n", - "\n", - "all_img_emb = np.concatenate(all_img_emb)\n", - "all_labels = np.concatenate(all_labels)\n", - "\n", - "# ------------------------------------------------------------\n", - "# 2. Fit UMAP\n", - "# ------------------------------------------------------------\n", - "reducer = UMAP(n_components=2, random_state=42)\n", - "embedding = reducer.fit_transform(all_img_emb)\n", - "\n", - "# ------------------------------------------------------------\n", - "# 3. Plot coloured by ground-truth label\n", - "# ------------------------------------------------------------\n", - "plt.figure(figsize=(10, 8))\n", - "for i, class_name in enumerate(CLASS_NAMES):\n", - " plt.scatter(embedding[all_labels == i, 0], embedding[all_labels == i, 1], label=class_name, alpha=0.5, s=5)\n", - "\n", - "plt.title('UMAP Projection of Fashion-MNIST Image Embeddings')\n", - "plt.xlabel('UMAP Component 1')\n", - "plt.ylabel('UMAP Component 2')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "source": [ - "# ------------------------------------------------------------\n", - "# Importing cuml instead of umap\n", - "# ------------------------------------------------------------\n", - "from cuml import UMAP\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import torch\n", - "\n", - "# ------------------------------------------------------------\n", - "# 1. Collect image embeddings\n", - "# ------------------------------------------------------------\n", - "all_img_emb = []\n", - "all_labels = []\n", - "\n", - "print(\"Collecting all image embeddings (Device: GPU)\")\n", - "with torch.no_grad():\n", - " for pixel_values, labels in test_loader:\n", - " image_features = get_image_embeddings(pixel_values.to(device))\n", - "\n", - " all_img_emb.append(image_features)\n", - " all_labels.append(labels)\n", - "\n", - "all_img_emb_gpu = torch.cat(all_img_emb)\n", - "all_labels_cpu = torch.cat(all_labels)\n", - "print(f\"Collected {len(all_img_emb_gpu)} embeddings on the GPU.\")\n", - "\n", - "# ------------------------------------------------------------\n", - "# 2. Fit UMAP\n", - "# ------------------------------------------------------------\n", - "reducer = UMAP(n_components=2, random_state=42)\n", - "\n", - "print(\"Running UMAP on GPU\")\n", - "embedding_gpu = reducer.fit_transform(all_img_emb_gpu)\n", - "print(\"GPU UMAP Done!\")\n", - "\n", - "\n", - "# ------------------------------------------------------------\n", - "# 3. Plot coloured by ground-truth label\n", - "# ------------------------------------------------------------\n", - "\n", - "embedding_cpu = embedding_gpu.get() # .get() converts CuPy array to NumPy\n", - "all_labels_numpy = all_labels_cpu.numpy()\n", - "\n", - "plt.figure(figsize=(10, 8))\n", - "for i, class_name in enumerate(CLASS_NAMES):\n", - " # Use the NumPy arrays for plotting\n", - " mask = (all_labels_numpy == i)\n", - " plt.scatter(embedding_cpu[mask, 0], embedding_cpu[mask, 1], label=class_name, alpha=0.5, s=5)\n", - "\n", - "plt.title('Projection of Fashion-MNIST')\n", - "plt.xlabel('UMAP Component 1')\n", - "plt.ylabel('UMAP Component 2')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ], - "metadata": { - "id": "FmuhARQVywHi", - "outputId": "47988497-e7f9-4949-e29f-5a7324269636", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 807 - } - }, - "id": "FmuhARQVywHi", - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting all image embeddings (Device: GPU)\n", - "Collected 10000 embeddings on the GPU.\n", - "[2025-11-02 04:23:34.254] [CUML] [info] build_algo set to brute_force_knn because random_state is given\n", - "Running UMAP on GPU\n", - "GPU UMAP Done!\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1UAAAK9CAYAAADMn0adAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XeYXVW5+PHvLqe36S2ZyaQQklBCSQAJkATBIIKAoBdFAa+IF0GkiIoFQlNRQEQE9XeVABa4KE2lgyCdhBYghbTJTJIpmXLm9HN2Wb8/zsxhJjMJARKSwPt5nvMwZ++19157z8kw77xrvUtTSimEEEIIIYQQQrwv+o7ugBBCCCGEEELsyiSoEkIIIYQQQogPQIIqIYQQQgghhPgAJKgSQgghhBBCiA9AgiohhBBCCCGE+AAkqBJCCCGEEEKID0CCKiGEEEIIIYT4ACSoEkIIIYQQQogPQIIqIYQQQgghhPgAJKgSQohdWHNzM6effvqHft0FCxagaRotLS0f+rXfi1/84hdMmDABwzDYZ599dnR3gOL37JhjjnnXdk8++SSapvHkk09u/04JIYT4QCSoEkKIbWww4Bh8+f1+Jk+ezDnnnENnZ+eO7t578pOf/IR77713R3fjfXnkkUf47ne/y6xZs7jlllv4yU9+stm2p59++rDv2dDXQw899CH2eufQ0tJSuv8rr7xy1DannHIKmqYRDoeHbZ8zZw6apnHsscdu9rzXXHNNadtg8Pi3v/1tWNs33niDk046iXHjxuH3+xkzZgxHHnkkv/71rwGYP3/+Zr9nQ19z5sz5gE9DCCHenbmjOyCEEB9Vl19+OePHjyeXy/HMM89w880388ADD/Dmm28SDAa3yTWWL1+Orm+/v4/95Cc/4aSTTuL4448ftv0rX/kKJ598Mj6fb7td+4N64okn0HWdP/zhD3i93ndt7/P5+N///d8R26dPn749uveuDjvsMLLZ7Fb1fXvx+/389a9/5Uc/+tGw7el0mvvuuw+/37/ZY//5z3/y8ssvs//++7/n6z733HPMnTuXpqYmvv71r1NXV0dbWxsvvPACv/rVr/jWt77F5z73OSZNmlQ6JpVKcdZZZ3HCCSfwuc99rrS9trb2PV9fCCHeKwmqhBBiO/n0pz/NjBkzADjjjDOorKzkuuuu47777uOLX/ziqMek02lCodBWX2NHBTWGYWAYxg659tbq6uoiEAhsdVBimiZf/vKXt3Ovtp6u61sMWj4MRx99NHfffTevv/76sODyvvvuo1AocNRRR/HEE0+MOK6pqYlkMslll13G/fff/56ve9VVVxGLxVi4cCFlZWXD9nV1dQGw9957s/fee5e2d3d3c9ZZZ7H33nvvVN9HIcTHgwz/E0KID8nhhx8OwJo1a4DikLNwOMyqVas4+uijiUQinHLKKUAxuLrwwgtpbGzE5/Ox++67c80116CUGnbO0eZUxeNxzjvvvNKxkyZN4uqrr8Z13WHtXNflV7/6FXvttRd+v5/q6mqOOuooFi1aBICmaaTTaW699dbSUKrBa21uTtVNN93EHnvsgc/no6GhgbPPPpt4PD6szZw5c9hzzz1ZsmQJc+fOJRgMMmbMGH7+859v1XO0bZsrrriCiRMn4vP5aG5u5gc/+AH5fL7URtM0brnlFtLpdKnvCxYs2Krzj+bpp5/m85//PE1NTfh8PhobGzn//PPJZrPD2nV0dPDVr36VsWPH4vP5qK+v57jjjht17tkzzzzDAQccgN/vZ8KECdx2223D9m9uTtVdd93F/vvvTyAQoKqqii9/+cusX79+WJvBz9b69es5/vjjCYfDVFdX853vfAfHcbb6vj/xiU8wfvx4/vKXvwzb/uc//5mjjjqKioqKUY+LRCKcf/75/OMf/+CVV17Z6usNWrVqFXvssceIgAqgpqbmPZ9PCCG2NwmqhBDiQ7Jq1SoAKisrS9ts22bevHnU1NRwzTXXcOKJJ6KU4rOf/Sy//OUvOeqoo7juuuvYfffdueiii7jgggu2eI1MJsPs2bP505/+xKmnnsoNN9zArFmzuPjii0cc+7Wvfa0UfF199dV8//vfx+/388ILLwBw++234/P5OPTQQ7n99tu5/fbb+cY3vrHZa8+fP5+zzz6bhoYGrr32Wk488UR+97vf8alPfQrLsoa17evr46ijjmL69Olce+21TJkyhe9973s8+OCD7/oczzjjDC655BL2228/fvnLXzJ79mx++tOfcvLJJ5fa3H777Rx66KH4fL5S3w877LB3PXd3d/ewV39/P1AMZDKZDGeddRa//vWvmTdvHr/+9a859dRThx1/4okncs899/DVr36Vm266iXPPPZdkMklra+uwditXruSkk07iyCOP5Nprr6W8vJzTTz+dt956a4v9W7BgAV/4whcwDIOf/vSnfP3rX+fuu+/mkEMOGRG8Oo7DvHnzqKys5JprrmH27Nlce+21/P73v3/X5zDUF7/4Re64445SQN/d3c0jjzzCl770pS0e9+1vf5vy8nLmz5//nq4HMG7cOF5++WXefPPN93ysEELsEEoIIcQ2dcsttyhAPfbYY2rjxo2qra1N3XHHHaqyslIFAgG1bt06pZRSp512mgLU97///WHH33vvvQpQV1555bDtJ510ktI0Ta1cubK0bdy4ceq0004rvb/iiitUKBRSb7/99rBjv//97yvDMFRra6tSSqknnnhCAercc88d0X/XdUtfh0KhYeff9B7XrFmjlFKqq6tLeb1e9alPfUo5jlNqd+ONNypA/fGPfyxtmz17tgLUbbfdVtqWz+dVXV2dOvHEE0dca6jXXntNAeqMM84Ytv073/mOAtQTTzxR2nbaaaepUCi0xfMNbQuMeM2ePVsppVQmkxlxzE9/+lOlaZpau3atUkqpvr4+Bahf/OIXW7zWuHHjFKD+85//lLZ1dXUpn8+nLrzwwtK2f//73wpQ//73v5VSShUKBVVTU6P23HNPlc1mS+3++c9/KkBdcsklI+7n8ssvH3btfffdV+2///7v+jzWrFlTupc333xTAerpp59WSin1m9/8RoXDYZVOp0d9xrNnz1Z77LGHUkqpyy67TAHq5ZdfHnHeTe/zrrvuKm175JFHlGEYyjAM9YlPfEJ997vfVQ8//LAqFAqb7fPGjRsVoC699NJ3vT8hhNjWJFMlhBDbyRFHHEF1dTWNjY2cfPLJhMNh7rnnHsaMGTOs3VlnnTXs/QMPPIBhGJx77rnDtl944YUopbaYzbnrrrs49NBDKS8vH5ZxOeKII3Ach//85z8A/P3vf0fTNC699NIR59A07T3f62OPPUahUOC8884bVjjj61//OtFolH/961/D2ofD4WHzXrxeLwcccACrV6/e4nUeeOABgBFZtwsvvBBgxHXeC7/fz6OPPjrsde211wIQCARK7dLpNN3d3Rx88MEopXj11VdLbbxeL08++SR9fX1bvNa0adM49NBDS++rq6vZfffdt3j/ixYtoquri29+85vD5lp95jOfYcqUKaPe+//8z/8Me3/ooYe+6zPe1B577MHee+/NX//6VwD+8pe/cNxxx21VsZXBbNVll132nq555JFH8vzzz/PZz36W119/nZ///OfMmzePMWPGvK85WkIIsb1JoQohhNhOfvOb3zB58mRM06S2tpbdd999RKU+0zQZO3bssG1r166loaGBSCQybPvUqVNL+zdnxYoVLF68mOrq6lH3D07yX7VqFQ0NDZudE/NeDfZp9913H7bd6/UyYcKEEX0eO3bsiOCtvLycxYsXv+t1dF0fVvUNoK6ujrKysi0+m3djGAZHHHHEqPtaW1u55JJLuP/++0cETINDBH0+H1dffTUXXnghtbW1HHTQQRxzzDGceuqp1NXVDTumqalpxDXKy8u3GIxt7hkDTJkyhWeeeWbYtsF5clu6xsaNG4fNsQqHwyNKpAN86Utf4tprr+X888/nueee4wc/+MFm+zlULBbjvPPO49JLL+XVV1+lvLx8q44DmDlzJnfffTeFQoHXX3+de+65h1/+8pecdNJJvPbaa0ybNm2rzyWEENubZKqEEGI7OeCAAzjiiCOYM2cOU6dOHbX0uc/n26Yl0V3X5cgjjxyRcRl8nXjiidvsWh/E5ioHqk0KcWzO+8mmvV+O43DkkUfyr3/9i+9973vce++9PProo6XCF0MLgJx33nm8/fbb/PSnP8Xv9/PjH/+YqVOnlrJZgz7o/W+NranOOHPmTOrr60uvoetHDfXFL36R7u5uvv71r1NZWcmnPvWpre7Ht7/9bcrKyt5ztmqQ1+tl5syZ/OQnP+Hmm2/Gsizuuuuu93UuIYTYXiRTJYQQO5lx48bx2GOPkUwmh2Wrli1bVtq/ORMnTiSVSm024zK03cMPP0xvb+8Ws1VbG7wM9mn58uVMmDChtL1QKLBmzZp37c/WGjduHK7rsmLFilLmDqCzs5N4PL7FZ/N+vfHGG7z99tvceuutwwpTPProo6O2nzhxIhdeeCEXXnghK1asYJ999uHaa6/lT3/60wfqx9BnPFhJctDy5cvf173/+c9/HlbBcOj3bqimpiZmzZrFk08+yVlnnYVpbv2vD4PZqvnz53Paaae95z4ONbhEQXt7+wc6jxBCbGuSqRJCiJ3M0UcfjeM43HjjjcO2//KXv0TTND796U9v9tgvfOELPP/88zz88MMj9sXjcWzbBihVGRwtezA0WxIKhUZUlRvNEUccgdfr5YYbbhh2/B/+8Af6+/v5zGc+867n2BpHH300ANdff/2w7ddddx3ANrvOUIMZn6H3pZTiV7/61bB2mUyGXC43bNvEiROJRCLDyr2/XzNmzKCmpobf/va3w8734IMPsnTp0vd177NmzeKII44ovTYXVAFceeWVXHrppXzrW996z9c577zzKCsr4/LLL9+q9v/+979HzdoNzqkbbQikEELsSJKpEkKIncyxxx7L3Llz+eEPf0hLSwvTp0/nkUce4b777uO8885j4sSJmz32oosu4v777+eYY47h9NNPZ//99yedTvPGG2/wt7/9jZaWFqqqqpg7dy5f+cpXuOGGG1ixYgVHHXUUruvy9NNPM3fuXM455xwA9t9/fx577DGuu+46GhoaGD9+PAceeOCI61ZXV3PxxRdz2WWXcdRRR/HZz36W5cuXc9NNNzFz5sxtthjr9OnTOe200/j9739PPB5n9uzZvPTSS9x6660cf/zxzJ07d5tcZ6gpU6YwceJEvvOd77B+/Xqi0Sh///vfR8x/evvtt/nkJz/JF77wBaZNm4Zpmtxzzz10dnYOK/f+fnk8Hq6++mq++tWvMnv2bL74xS/S2dnJr371K5qbmzn//PM/8DW2ZPbs2cyePft9HRuLxfj2t7+91UMAv/Wtb5HJZDjhhBOYMmUKhUKB5557jjvvvJPm5ma++tWvvq9+CCHE9iJBlRBC7GR0Xef+++/nkksu4c477+SWW26hubmZX/ziF6Uqd5sTDAZ56qmn+MlPfsJdd93FbbfdRjQaZfLkyVx22WXEYrFS21tuuYW9996bP/zhD1x00UXEYjFmzJjBwQcfXGpz3XXXceaZZ/KjH/2IbDbLaaedNmpQBcV1qqqrq7nxxhs5//zzqaio4Mwzz+QnP/kJHo9n2zwc4H//93+ZMGECCxYs4J577qGuro6LL7541EqG24LH4+Ef//gH5557bmmu1AknnMA555zD9OnTS+0aGxv54he/yOOPP87tt9+OaZpMmTKF//u//9tmc9lOP/10gsEgP/vZz/je975HKBTihBNO4Oqrrx51odydyXnnncf1119fKuyxJddccw133XUXDzzwAL///e8pFAo0NTXxzW9+kx/96Ec7/b0KIT5+NLUtZ8UKIYT4UDU2NjJv3jz+93//d0d3RQghhPjYkjlVQgixi7Isi56eHqqqqnZ0V4QQQoiPNRn+J4QQu6CHH36YO+64g2w2yyc/+ckd3R0hhBDiY02G/wkhxC5o7ty5rFy5krPOOmurF2IVQgghxPYhQZUQQgghhBBCfAAyp0oIIYQQQgghPgAJqoQQQgghhBDiA5BCFZtwXZcNGzYQiUTQNG1Hd0cIIYQQQgixgyilSCaTNDQ0oOubz0dJULWJDRs20NjYuKO7IYQQQgghhNhJtLW1MXbs2M3ul6BqE5FIBCg+uGg0uoN7M5xlWTzyyCN86lOfwuPx7OjuiI84+byJD5t85sSHST5v4sMmn7ldUyKRoLGxsRQjbI4EVZsYHPIXjUZ3yqAqGAwSjUblH6PY7uTzJj5s8pkTHyb5vIkPm3zmdm3vNi1IClUIIYQQQgghxAcgQZUQQgghhBBCfAASVAkhhBBCCCHEByBzqoQQQgghhNiEUgrbtnEcZ5ucz7IsTNMkl8tts3OKD84wDEzT/MBLKUlQJYQQQgghxBCFQoH29nYymcw2O6dSirq6Otra2mQt1J1MMBikvr4er9f7vs8hQZUQQgghhBADXNdlzZo1GIZBQ0MDXq93mwRBruuSSqUIh8NbXERWfHiUUhQKBTZu3MiaNWvYbbfd3vf3RoIqIYQQQgghBhQKBVzXpbGxkWAwuM3O67ouhUIBv98vQdVOJBAI4PF4WLt2ben7837Id1QIIYQQQohNSODz8bEtvtfyaRFCCCGEEEKID0CCKiGEEEIIIYT4ACSoEkIIIYQQQgwzf/589tlnny22mTNnDuedd96H0p+dnQRVQgghhBBC7OI0Tdvia/78+dv8mnfffTdXXHHFFtu0tLSgaRqvvfbaqPsvu+wyvvzlLwPFe7j33nu3cS8/HFL9TwghhBBCiF1ce3t76es777yTSy65hOXLl5e2hcPhbX7NioqKLe4vFArveo777ruP73//+9uqSzuMZKqEEEIIIYTYxdXV1ZVesVgMTdOGbRstqHryySc54IADCIVClJWVMWvWLNauXTusze23305zczOxWIyTTz6ZZDJZ2rfp8L/m5mauuOIKTj31VKLRKGeeeSbjx48HYN9990XTNObMmVNq39bWxltvvcVRRx1Fc3MzACeccAKappXeA9x8881MnDgRr9fL7rvvzu233z6sj5qmcfPNN/PpT3+aQCDAhAkT+Nvf/vY+n+T7I0GVEEIIIYQQHzO2bXP88ccze/ZsFi9ezPPPP8+ZZ545bKHjVatWce+99/LPf/6Tf/7znzz11FP87Gc/2+J5r7nmGqZPn86rr77Kj3/8Y1566SUAHnvsMdrb27n77rtLbe+//37mzJlDNBpl4cKFANxyyy20t7eX3t9zzz18+9vf5sILL+TNN9/kG9/4Bl/96lf597//Pey6P/7xjznxxBN5/fXXOeWUUzj55JNZunTpNnlWW0OG/wkhhBBCCLEduK7ipZZe1vVlGRPzsXvlzvOrdyKRoL+/n2OOOYaJEycCMHXq1GFtXNdlwYIFRCIRAL7yla/w+OOPc9VVV232vIcffjgXXnhh6b1hGABUVlZSV1c3rO19993HcccdB0B1dTUAZWVlw9pdc801nH766Xzzm98E4IILLuCFF17gmmuuYe7cuaV2n//85znjjDMAuOKKK3j00Uf59a9/zU033fQensr7J5kqIYQQQgghtoOXWnr584trefStDv7yUhuvtCV2SD9aW1sJh8Ol109+8hMqKio4/fTTmTdvHsceeyy/+tWvhs3LguJwvsGACqC+vp6urq4tXmvGjBlb1adEIsFTTz3FZz/72S22W7p0KbNmzRq2bdasWSOyUJ/4xCdGvP8wM1USVAkhhBBCCLEdrOvLUrBcptRHydsuG/pzO6QfDQ0NvPbaa6XX//zP/wDFoXbPP/88Bx98MHfeeSeTJ0/mhRdeKB3n8XiGnUfTNFzX3eK1QqHQVvXpwQcfZNq0aTQ2Nr7Hu9k5SVAlhBBCCCHEdjC2PIDXo7OsPYHP1GmI+XdIP0zTZNKkSaXX0Kp9++67LxdffDHPPfcce+65J3/5y1+26bW9Xi8AjuMM2z506N8gj8czot3UqVN59tlnh2179tlnmTZt2rBtQ4PBwfebDmfcnnaegZ1CCCGEEEJ8hBzQXAxedsY5VWvWrOH3v/89n/3sZ2loaGD58uWsWLGCU089dZtep6amhkAgwEMPPcTYsWPx+/2EQiEefPBBvvOd7wxr29zczOOPP86sWbPw+XyUl5dz0UUX8YUvfIF9992XI444gn/84x/cfffdPPbYY8OOveuuu5gxYwaHHHIIf/7zn3nppZf4wx/+sE3vZUskUyWEEEIIIcR2oOsaB02o5KT9x3LghEr0IZX1drRgMMiyZcs48cQTmTx5MmeeeSZnn3023/jGN7bpdUzT5IYbbuB3v/sdDQ0NHHfccTz11FOEw2H222+/YW2vvfZaHn30URobG9l3330BOP744/nVr37FNddcwx577MHvfvc7brnllmGl2aG4iPAdd9zB3nvvzW233cZf//rXEdms7UlTSqkP7Wq7gEQiQSwWo7+/n2g0uqO7M4xlWTzwwAMcffTRI8a4CrGtyedNfNjkMyc+TPJ5E5uTy+VYs2YN48ePx+/fdsP1XNclkUgQjUbR9Y93XuPcc8/Ftu1tVplP0zTuuecejj/++Pd1/Ja+51sbG+w8OUghhBBCCCHER96ee+45olrfrk6CKiE+JlyleLYvyV/be+m3bQ4rj/K1sVWYH/O/lgkhhBDiw3XmmWfu6C5scxJUCfER4irF8/EU/+5JommK2eVRDi4Po2saL8TTXLZqA8vTOVwFz8XTtGTzXDV57E41xlsIIYQQ4r3YGWYzSVAlxE7MVYrn+lI81ZdAKY25lRE+URbebBD0QjzNL1s6WZnJoQGvJrLomsZBZSGe6EnQksljK1BA1lXc09nH+KCPmGnQ6PdxUFlIAiwhhBBCiPdIgiohdlKuUvxhXTe3ruui03JAKZ7qS3LpxAYOqYgMy0qhKSpNk2fiSZalsnh00JTGmkyOx3v6eSOZ4aHuODlXMfRvOf2Oy41rOxkf8FHnK64jcXB5eMfcsBBCCCHELkqCKiF2Qq5S/L+2jdzU1kVXwUYBGrAinePfvf0cUhHh+XiK+Ss30JrNYylwlYvSNGy3GDYZGoQNg6d6k/RZNj0D5xlKAX2WQ7nHAQq05fKABFVCCCGEEO+FzFAXYif0QjzNnzZ00z0kEFJAXikWJ3MA/LsnwepMjpTjknFdcgryrsIdaOvXNT5ZGSXtusRtBxtwRrmWAjoLFpZSNPp9QDGoezGeBuDFeBp3JxirLIQQQgixs5JMlRA7CVcpXoinacvlWZnJYSuFqWk4QwIaBSRtm2d6kzzXlyKv1IhASQEGxSzV2lyetO3gUsx0aQP7B2kU/7JiKNgnGuKAWBAoBnV3dPRwJHBHRw+6acqwQCGEEEKIzZCgSogdaGgglbBdFvWnyLsu6/M2BVehjRiwB6uyBc5d0kK/7eBsJoHk0SBk6PQVHCo8JhnXAk1RZhrkXRfbVSgFWaAA5F2XZaksL8TTHFIRoS2XJ++6MLBPhgUKIYQQQmyeBFVC7EDPx1P8sqWThG2TdRSGBlHTYH2uQMzUqfR66Lds0gMFJjQg6bgknWKGaXDb0NjKAAxNw1aKrOtyaHkEjSxp2yboMbAcly7LIem4pWNSCt5MZXmqL8EhFRHG+r2k7OL+lO0y1u/90J6JEEIIIcSuRoIqIT4Etutyy/oelqYy7B4KMC3sZ12uwN2dcd5IpgmZBknbQali9spS0BTw4rgKn6YRtx36bGdY8DQYUEExkGLgvVeDCq/JjGiQp+NpXu5PE/MYNAdDdOQLZGyX1JCAalDBVShVPOOmU6hkSpUQQgixc9PeZUmUSy+9lPnz5384nfkYkqBKiO1ocHjf7eu7eTqexHYV92/sp9prUuUxWJrOk3cVjlUMmHQ0FMViE8tSWer9Xhr8Hrx5jbTj4Kpi4FTgnaBKB7yANbDN0DR6CjbP9SbJKOgt2MRtnfZsHovieUaLkQKGxrpcnitXrmd9rkDcsgAIGRrr84UP43EJIYQQ4n1qb28vfX3nnXdyySWXsHz58tK2cPidYfxKKRzHwTR3vlCgUCjg9e56I2Sk+p8Q29EL8TS3ru/m6b4kfZZDQSmyjkt7rhikGK6LX9ex3MGiFC7WQMTjKjCU4lOVMf5nXA1fqq9knN9DhdckpGn4gZBWrOiXhVJ1v6yrSLuKDbZLwnEpAL22S4ft0mM75EaJqEwg77g83pvklvXdPNKbpD1vA5B23qkKuKsYXDT5zvYenutL4So16rZ3O0YIIYTYVdTV1ZVesVgMTdNK75ctW0YkEuHBBx9k//33x+fz8cwzz5DP5zn33HOpqanB7/dzyCGHsHDhwtI5FyxYQFlZ2bDr3HvvvcOyYq+//jpz584lEokQjUbZf//9WbRoUWn/M888w6GHHkogEKCxsZFzzz2XdDpd2t/c3MwVV1zBqaeeSjQa5cwzz9x+D2k72vnCUyE+Ilyl+HdvP28k01iqmH3KDAzn8+gaG/MWStfJDgzFyzsupg6mKmadfBoUFCxLZ/l8XSUNXg8rMnnW5/IkdbAcRg2Q7IH/bk1IMPgj0a9BWoHmuANztRQT/cVBhVVek7XZYhn3g8pCAKXiGo1+HweVhdC3MORgaDGOwfZDzzHG50XTYF2usFXn29J5B497Pp7iujUdtBcsDOCUhko0NO7t6iPnKPyGxudqK/jqmEqe7Uty/Zp2XkvmKAAVhs6BsRBQKxUPhRBCfKR8//vf55prrmHChAmUl5fz3e9+l7///e/ceuutjBs3jp///OfMmzePlStXUlFRsVXnPOWUU9h33325+eabMQyD1157DY/HA8CqVas46qijuPLKK/njH//Ixo0bOeecczjnnHO45ZZbSue45ppruOSSS7j00ku3y31/GCSoEmIbKjgO81e181Yyg9I0unN5Oi2HgqsI6FBwwadDo9/LpKCPZ+MpUg64DARD7jsFKHIKOgoWr/ZnyLqKMT4PBeUSMnTsvOLdw453N5iqtlXxmoOBmAb0WsVgrytf4K8dvdiu4oTaCqaG/PypvYe86+LTi2c4uDy82SDnhXia2zZ0D2sPlLYlLJd+2ybtFJ/T+KCP6ZEgcyuiHFweHhZgjayWmKaghvcD4N89SZamcxSUouC43Li2E1tBynFLwybbMu38rrWDdYXhRek3Oi4vJTIcKRUPhRBCfFCuC63PQbwVoo1QvscO7c7ll1/OkUceCUA6nebmm29mwYIFfPrTnwbg//2//8ejjz7KH/7wBy666KKtOmdraysXXXQRU6ZMAWC33XYr7fvpT3/KKaecwnnnnVfad8MNNzB79mxuvvlm/H4/AIcffjgXXnjhtrrNHUKCKiG2ofmr2vlre09pEV6D4lwlAOWCoYFH0ynzGIz1eXAH2g3lUvyHqWtgKUi7LjnHZUO+wOpMgYxjY3/AkWmDYYquFYtQDM6YGlpRcDD86bVsCjrELZu7O3oYH/SxKpNnajhAwrJL5dZHC54OLg+XyrNPCwd4K5nl8Z5+lqWzrErnGOf38WoiTW5grpgDrC/YPB9P87eOXm6Y2sTsylip0MeTvf10FRzG+Dx0FSxMTaM54GNZOssTPYlSFmxDPk9mYO6YAvpst7Qo8qBuxx19NWSg33Z2uSGPQgghdkKtz8GiP4KdB8OHMfULEPvUDuvOjBkzSl+vWrUKy7KYNWtWaZvH4+GAAw5g6dKlW33OCy64gDPOOIPbb7+dI444gs9//vNMnDgRKA4NXLx4MX/+859L7ZVSuK7LmjVrmDp16oh+7aokqBJiG3ormcFy3/nV3aE4J0mnOO8JBcp2aM3kyTouSXdkFb7B45yB7NG6vIWpa+wdDuAqRUjXUSh8GqQcVRru914M9tAaCGY8FDNl3oEhhzou/oGAo6fgkNUU1V6TnKt4I5kj6Th09yaZGPQzxuflub4Uf+vooT1XYFZ5hKXpbCnYavT78Ok6S1JZUo7Lf/pSrM8V6LcdWvP2iKBy8P47LYcrV3dwaEWUP6zr5tetnSSt4kLGulIkXBfLVbTmCmjAa8kMz8dTLEnlWJzMoWtaKbgdes+bfyLvZMQaAt5SgCaEEEK8b/HWYkBVuydax5vo/et2aHdCoff2/zZd11GbzDG2BgpZDZo/fz5f+tKX+Ne//sWDDz7IpZdeyh133MEJJ5xAKpXiG9/4Bueee+6Iczc1Nb3vfu2MpFCFENuIqxQVXg8uDAsUNv1lvgCsK9gsTWa3mHEauitl2/TkbWzl4moaHg32Coeo8hibPX40m/sHX2AgU6TAtG0iuSzdhTwApqaTV4q47ZCyHUwNDi0LU+012ScSRNOKQ/lWZvOsy1s825fEp+s0+n0o16V+/SoautrwJhNUewxc18Fv6GibPKfRtGRzvBBP85++BEnbQdc0LGBJOke6YJO0HVKWQ63XQwDFn9b38L9tXXQXLCpMg7ChEzU0ooax+eGSanDsoyo9o59MatiqeV1CCCHEFpU1gemDzjdRpg83NnZH96hk4sSJeL1enn322dI2y7JYuHAh06ZNA6C6uppkMjmssMRrr7024lyTJ0/m/PPP55FHHuFzn/tcab7Ufvvtx5IlS5g0adKI165Y4W9LJFMlxHuwuXlDrlL8YV03GzI5fBpkNwmWNh1lphjIXI1Cp5g5yg+8NyiuPdFRKKAUxC0HjwZ9jk2T30uXlX3X4KTU/1G2KYoZqmnhAF3JJG4hQ5lpkHCKf4mKeXR6Cw4p28XvM0k4Liv64lRaOfb0FGgLesm7LoeUhXkmnmJiwMdJdZUcVBZi3dI3ufuFl1hY1oBtZtBdyJheegsWLiMXLt6UX9dpy+WJmWZx3tlA4OMA6Bopq/hk30xlqfKaRAyLhO3gUJw/FTENlHLpK4xWRl4Nv7hSaJrLD8bVM7sytpVPVAghhNiCpoOL/x2YU+Xs4DlVQ4VCIc466ywuuugiKioqaGpq4uc//zmZTIavfe1rABx44IEEg0F+8IMfcO655/Liiy+yYMGC0jmy2SwXXXQRJ510EuPHj2fdunUsXLiQE088EYDvfe97HHTQQZxzzjmcccYZhEIhlixZwqOPPsqNN964I257u5GgSoj3YNN5Q65SaCjua1nHv5MF+hWYug6jLK77XgzOcRqc22Q5CtPUaPB5WJktoGsaa7N5GkyDKo+B7Tj0uVtX8W8oDQjpEDPNYmDlManq24CvkKU9XAE+6LJswqZJ3nGZGgrQkUgQbm9jv5429HwK/+yj8HmiLE3naPB5OamuslQwon9jJxt1Ez0cZkxPJy2xcgzTS8gwsGwHg2Klw9GYwBifh0a/j5PrTP4zUJbeoxXLzW8cWNtr8H3acSgzDaq9Jl0FG4UiYdlk3FGGSA4bylAMrirzaS6tjXDS+HrJUgkhhNg2dB2aDyl+7bqQSOzY/mziZz/7Ga7r8pWvfIVkMsmMGTN4+OGHKS8vB6CiooI//elPXHTRRfy///f/+OQnP8n8+fNLZc8Nw6Cnp4dTTz2Vzs5Oqqqq+NznPsdll10GwN57781TTz3FD3/4Qw499FCUUkycOJH/+q//2mH3vL1IUCXEe9CWy5NzXKKmwbJ0ltvXb2RpIsW6bIGCpqGhfaAiEoOD+VzAHCgioekaEY9BwnZJOsU5RYNzhdqUTdAwmBj04zUMdE3jtUSapLt1nTApBlQR08BScGhjHdUhjf/E0yQMP/S1gQKvrqGjE7dsKqwc+/W0Masyxsa1vUyId3Hq9AkjSqYDxKprqV6xhrdSKdaHYmB6qfCajA94ebovhaYGFywuZuNcpfBqGl5dJ6DrTAj6cJXCdhWuemeumUYxmCqo4rwwn6bh0XQcBWUek7Sr6C1YIzKGRaoUVBmuS61b4Fivw+lNUZqn7fWuK9ILIYQQO7vTTz+d008/vfR+zpw5I+ZGAfj9fm644QZuuOGGzZ7r+OOP5/jjjx+27etf/zoAXq+Xv/71r1vsy8yZM3nkkUc2u7+lpWWLx+8qJKgSYisMDvtbmcmxPm/xWjKDBmzIWcRtFzQNR9fQXPBRzDQZA9X74J1hbh6KAdNmis6VhsRpgKGKxSNMoNZj0lmwQb1Toa94Pg0XiHpNzm6q44BYkEtWbuCvG7o3E1AMpyhWunMADY0uy+FVb4xVYR+JgQWKdwv6aAwHmRYOEjF1/EYOPZ9i49peDI+X8ppa9i4PM1r58bFT9uAkpajb2EsiXI5RU8fLiQyvJjMU1DvDEb1o7BcJDswR00CDjQWbtO1y/dpOOvPFwhZDwx1XFYctWqpYSchQxaCz37Lptx3yxZIWo30zqUj2sfu6leze1caJB81k5tzj3v1hCSGEEEJsxi5VqOI///kPxx57LA0NDWiaxr333jtsv1KKSy65hPr6egKBAEcccQQrVqzYMZ0VHymDw/5WpHPkHIeABruH/KQcB5viPyTNBY9yaTA1AloxKAo6Nh7XIeRYeCkGWjrFoCmgMaJ4ggZ4tWJhBXdgv6VgcSpLr2VT7jGp9JilwCto6ng1jamhIAeXhzF1ncsnNTCnMvau61gNhC/klaK3YNNRsGjPF8i7LlPDgdLxtT4P08JBoqZOk9/HsXvtwb5HfpqJMw9i+pGfpmH3aTzXl+LO9h6e60thuy7P9Ca5atV6rlrdQWt9M+P23p+KMWOZEgqwfyyEyTvBo0ZxrlRnwSKnFN22Q8pxsFBETYOVmRztucKwOViKYsA5GLQ6FLNWHQWbLtshrxQobZNhfsXjd8v2c8Gzd3NqtpN9KOAPBt/bh0EIIYQQYhO7VKYqnU4zffp0/vu//5vPfe5zI/b//Oc/54YbbuDWW29l/Pjx/PjHP2bevHksWbKktLiYEO/FYIbqrvZulqWzjPN7MXQNhc7iVLZUktzSDTzAgQEvnx1bw5JMgWc7uum3bDIeD5ZdzChFDIOccglqOgys02ANydgUs1CKnOtgDGwP6xp5pbCVIuO6OK7Cbwy21IgYBjU+E3cggHixP03edvBpxQWEN3tvvFP0LqRr1HgN6r1e1hcsEpbN+KAPemHfaIiFwxbZreLgaXvRyF4APNeXGjbP7K1UiIe7+1mVyeEqxb+645hAudfEg4amafj0d2ZT6YCuazhA1GPyejKJX9cpKEV3wUYDJoUCvJXO4g5k6gaPHrw9B+gvDXnc9KYHyqW7LuP6ujj1hX/gpNPkgikilVWU1dS99w+GEEIIIcQQu1RQ9elPf7q04vOmlFJcf/31/OhHP+K444pDeW677TZqa2u59957Ofnkkz/MroqPiMEM1fJUlrW5Ah15i5BhMCHgI+fm8Xsg5Sqyjkul16Q8HGJCKMgX6iu5IJHg2VwO23Yo6EZxLpTj4CgIeyBoGozzB3gxkSE7EBCUCiooCBs6Gccl5brYqhhIpB2XgKYxLeijI18MOMo9Bg9v7KerYJFxFP/pTdJVsLAHApDNlczQAJ+uUXAVCqjyeDi8KoqGRlsuzxjToGfdMiKmTkG9s3jvEz0J1mZzpBxFxNRZlSlm7/aIBFmSyrI0laHftgkZBlnHoSNv4dFA1zQKShExdI6qjpLqsOm3HIKmTqVpEjMNFsZTZByHWp+HfsvGVBDUIOs41HhMgoZOb8Gmd4uFQDatKagVC364DuV9nXiUQguFqGgYw7RDD2fslJ2nEpMQQgghdk27VFC1JWvWrKGjo4MjjjiitC0Wi3HggQfy/PPPbzaoyufz5PP50vvEQFUWy7JGLG62ow32Z2fr10dZWzqNY1tM8Bn05RT1HgPHVRSsAsp2yLoOJlBl6MyJBknaFk9t7OWv6yyez1hkTC8u4AccVQyONCBvuVi2RpvtEMLFVIqwoRN3XDwUh7Ll7eLXhgJNg/EBH225ArajaEu75F1Fpcdkgtfg+XianmyOPtuhoBQmUG1o6KoYzMRtZ9Qqe8qBmK5RZejsF/Th2jYb8gXG+HzsG/TwGFBvGIRQvJ1Ikbdd3uq3eDXusiFnM8Zn4tGLo4jfTqQI6TrTAl7aMznW5AoUXIcwGlHdIJnJElAWIdPDyoTBAeEA+0aL87TqvV6WpjMsWNcLrsuqZBqfruPTNAK6Tr9tUWMaGJoig8Kntqa64kCGShUHDPodm3H5DLlclkhFFbsfPIe63aZgOw44m5vlJj5M8jNOfJjk8yY2x7Is1MBoEtf9YNV8hxosFDF4brHzcF0XpRSWZWEYw9cA3dqfEZoarRTILkDTNO65555SNZLnnnuOWbNmsWHDBurr60vtvvCFL6BpGnfeeeeo55k/f36p7ONQf/nLXwjKXAshhBBCiI8V0zSpq6ujsbHxI7dArRhdoVCgra2Njo4ObHv4QiyZTIYvfelL9Pf3E41GN3uOj0ym6v26+OKLueCCC0rvE4kEjY2NfOpTn9rig9sRLMvi0Ucf5cgjj8Tj8ezo7nwsuEqxsD9DWy5H2lEsT2V5qT9NxnFJuy4hXafaazAhGKA1m6OzYJN1Xao8Jm254l82hv7TLP0Fw3UxlYur6cRMHVvTafAahD0elqVz5BwXBQQNnT2CPiaHg+QcB5+u82xvkm7HxqfppFwH2y1eY3DQW9TQCWrFuUu9toNCoWsahlKkN1nsdpBP0xjj91LuMTi4PMKydJYjyoL4X1tE+QGz+L/ufvKuS8ou/mWtoN7JVNX5vZxcV8mBQ0qpb/r8XnpjMWrFW8ysiNLXtpbUvp/g+aqmYedcn82zvvDO04oYOhlXYSk1bDCfVyuWVd+a3JJBca2rKyc10NS1jkRPF9HKGhomT0HTd6k6PR8L8jNOfJjk8yY2J5fL0dbWRjgc3qZz8pVSJJNJIpGILN+xk8nlcgQCAQ477LAR3/PEVq4t9pEJqurqipPNOzs7h2WqOjs72WeffTZ7nM/nw+fzjdju8Xh22h+yO3PfdlWDBSmGrbWkFOtbWwn2xfH5w6hoDI/Xi2Pk6HcUBXTQdcb5/NQFfbyZK5BEJ+Eq+vIOrqajeOcfmc2Q+U0amIaOchU2OqZpclhNORMCPv7a3otS0JYv0OA12LM8yuGVUQ6Mhfjj+m4e7EuR0QzSSgEGugG5IetSOUoj5PNQcBwyaAQMg6zrEvOYBByXvqEFHQYiMU3T6HAUlq54M1vAZ3oYEwzRA7Q7Dmk0pkXDvJXMMinoZ2LQW5pT1TTwvDa3YO4h1V7G1ZXz+htJ+tb2oHm8LA6WszxnUeXxsCJfwKuBZpikNXfw8eAfKFOfc98JqjQGytXr71T+2xwPxflmR9ZWcEhNBXpt5Xv4RIgdSX7GiQ+TfN7EphzHQdM0dF1H34Z/gBsc8jd4brHz0HUdTdNG/XmwtT8fPjJB1fjx46mrq+Pxxx8vBVGJRIIXX3yRs846a8d2TuwUXKV4ri/Fv3sTtOctGvweZpdH0NB4sjfJa8kMYUPDPzCWtqG/m0WLFrEUD8/4omSDvcRNLxU+LzVek3qfl70jAeZWRHkrlaUjZ5FwBsZfD6aNdJ3CJv3QAA8Kj+PiVYqIrhFxbHbPZ6gvj9Dg9xYr6RnFH7irs3nWre/mXxv7eby7n7TtUG4aOKqYgeqzh+dsXKVIFCyqvB46ccg6xXNNDvpYk869E1Spd+YcKU0j77rUen3Mq4rR6PfiWMWsUdJ28WoaS1JZ/IbO4ZVRDi4fuSbVljTsPo2XC4pVff044SgrjQDrM3lWpPN4dQ2vrpEduI/Bdb76HVWqUDhosJz61qxtrAH1fi+frIxtNuATQgghhNgWdqmgKpVKsXLlytL7NWvW8Nprr1FRUUFTUxPnnXceV155JbvttluppHpDQ8OIVaDFx9ML8TTXr+1kSTpLxnYJGjpP9aaImQYJ22ZjwebQighJ26EtlycYj2PbNslwjIxloadT5IJRvKaO1+tleiTIDybWo2sa6/IFIoZOwikuBLylRaIUxRLsVZrBp40C+b4uYlae5b0GHbbDfuUVdOYtlqWzJC2HqbEQz8STvJrMkLQdHE0j7bg0B3wcX1vOK4k0D2zsLxWicIC0q7DzxS1BQ6Mp4GNSyM+L/Zl3OjL4VzJNIw94FMRtlyZ/MXP7f529HAm8msgwozxK1NTfyeINMVqWT9e0YdsTtssiM0qhKkxH3iJj2cQMnaztMDnoBxRrs5CxHQbLxnzQ0hFVHoMTaitG9FcIIYQQo2tubua8887jvPPOA0bWMBCbt0sFVYsWLWLu3Lml94NzoU477TQWLFjAd7/7XdLpNGeeeSbxeJxDDjmEhx56SNaoEgC05fL0Wzauq3CUomA7bMxkIeCnEYf1ls3rPXGmlEVp9PsoKyvDNE30ni6McCVWKICLoqXgEsLmtWSGF+JpDi4P0+T34TJkeN8WMinegZhranmYE90Ub6+P01U7ht9bHvI9OULJLioMnSzFLBF9SWylCOkGAZ9OV94iYujsGwsR8xjsHwvxbG+CHkcxWK8mqEGD30tLrkCj30fA0Mg7Ll4drFEKDhmAX9fosyye6EkwKeQj774zfypq6vxX/ejD516Ip7l1/UbaCxa2qzihtoKvja0qlaPPuy6deQuPpnFIRYSWTIHOgXLwaBppR1HpNYmaDv22U5ofpsOoFQvfjQF8Ihbi2821zCqPSJZKCCHEx8bpp5/OrbfeChSHrTU1NXHqqafygx/8ANPcpX7t3+XsUk93zpw5bKlYoaZpXH755Vx++eUfYq/EzmowU9KazeHtj5NJpujL6yQdF0cVAytsB79rs7RQwNB0wtkCR1YEi9mNWLH6Y2jFCnoSeToLWQzTBz4P+1RESFg2bbk8EOagshB1PpP2go3StMFi3pgMDwyK2zTKvSZzKmJUOCamaXJXDnq8XjQgVbDpAZr8XlyliJo6sytiLOpPDayTpTM9GmR5Osei/hRZRxEwTbyuTUEVAyuvadKaL5BzFa2ZHF7TxIdOtddDLmeNyAI5QMpxUei8lsxQ6/MMLPQLPr2YodpcRqotl6e9YNFvOcQtm3s6e9kjHKAtlyfvFte36rFsLFcNDCHUqPd5aPJ7ac0V2DcaoMLjobtg4TN0HMfFHXhWg0HipsMARxM1igshnzG2mm801UgwJYQQ4mPpqKOO4pZbbiGfz/PAAw9w9tln4/F4uPjii3d0196XQqGwS1RhlFly4iPrhXiaBeu6+N3ba7lqTTt/a91AOpkE28Zw7NK0p7TtktF0yj0GrlJk0mn0gUmkzc3NRPc/kFzjeIxYBUSiuKaHxckMnXmLhO3iDsxt+mxNBRUeA78GAV1jVizEDyfU0ez3ENA1AkA5UKcrxvhM3kykeEQPsM9++2EHIqDreLSB4YFA3HZKk1qnhvx8uaGSmbEws8rD9BVsVmVyZByXHssm4zjYSjGYK0vYDoWByCTmNSm4LquzWcKGTpPPM2x0ogZEdA2/DrsHfYQNjbChcXJdMTN1cl0lB5WFSpmnh7r7uW1DNy/E0wA0+n3YriJu2ZR5TDwDgVaj34dP11mSylLv9XBCbQVHVcX4XG0Fk0N+dF1j93CAk+oqOaw8TNJ2yAxUPRwMSvcK+5moQyXDy5sWFcMsHTggFuSySWP49bRmCaiEEEJ8rPl8Purq6hg3bhxnnXUWRxxxBPfffz9z5swpDesbdPzxx3P66adv9bnfeOMNDj/8cAKBAJWVlZx55pmkUikAHnnkEfx+P/F4fNgx3/72tzn88MNL75955hkOPfRQAoEAjY2NnHvuuaTT6dL+5uZmrrjiCk499VSi0Shnnnnme34GO4IEVeIjx3VdWlpaeGb5Sl7r7qMtb9Ntenk7GEVzLKL54rwiFzBRpE0TV9NpTvXj6AZJf+CdcynFk31JOjDI+gN0OC59tktH3ibnKhb1p3khnsZViikhH1ODAZoDXuaURzi+toy9oiF+PrmRz0c87JFLUJFPkc3leT2Z5eHeJL9b182/PREOq6vA1DTyQ9IxvbZDylW0ZvPcvqGbZek86/IFVmbyvJXKUnAVmYGhjLbtgmNTlcugnGJWqMJjoCnoKzjkXUXehdZcAQdFUANzSNxhA0HDRNeLhTrGBfzDSqTf1dHLEz0Jck4x85R3XVpzeZ7rS7E2m6PW58Wn6+ho1Pk8jPV7USjG+rxMCvr4SkMVXxtbxX/VV/LVMZXMiIUpNw32j4Y4IBZkWSZHynVxBr4vGsXs2YZ0Dq2/Bzufh6ELJSoFrsJ0bA7zwt+mT+SLDVUcXB6WgEoIIcROw1UuCzsWct/K+1jUuQh3qxav37YCgQCFwqZls967dDrNvHnzKC8vZ+HChdx111089thjnHPOOQB88pOfpKysjL///e+lYxzH4c477+SUU04BYNWqVRx11FGceOKJLF68mDvvvJNnnnmmdI5B11xzDdOnT+fVV1/lxz/+8Qfu+4dhlxr+J8TWaG1tZdGiRazxhNkYrKRgmhiuCwoM18VUDjnTgwMEDB3D9BDymCQDXmq9HvYZ805J/hfiaV5NZFmft+gfrE6nOeiaRqPfQ0G5xSGAcbhh7UaWprPkXZcNBYcey6bB7+XUhiq+pBe4NZ/ilVg1WVthK6j0mKRsh6WpDMfVlPGfeIq12UJpXpaiGOz0Wg6LkxlSjoNH05gWDrImnUMB8YKNrhSFgYXq4t4gmuugcOkaWPNJaWC5it6B/rs4FNQ762dpgOsq6gMevlBbTnPAj0Jxd2cvfuCv7d1kNJ2E5ZJwHDbkC0RNk37L4daebt5O52gfKNRhKRcd+GdXnNcSaTyGTr3Xg16hlYKdl/ozLOpP05EvsCydA2BpMoOuivPNCmqgcCJgKRscB9v04lEuFhoaCt11KVcucze8zdfH1eHdZPVzIYQQYmfwcufL/N/y/8NyLTyah6PHHs3s2OwP5dpKKR5//HEefvhhvvWtb7Fw4cIPdL6//OUv5HI5brvtNkKh4h9eb7zxRo499liuvvpqamtrOfnkk/nLX/7C1772NQAef/xx4vE4J554IgA//elPOeWUU0oZs912240bbriB2bNnc/PNN5fqIBx++OFceOGFH6i/HzYJqsRHTnygal9NZYRo3iLt6uR1AxdFOJ/DV8hTlUmysaIavCEafB5OrC2jx3LRNIVCw3ZdXurP8LeOHpLZLLWOQ4JiFiWvQFOK1rzFlFCARr+vOK8on6fguliuIu86KEUpo+P1hXnbF2a9A5ZeXL+qs2ARM02mhoN0FGxqPCbdeYvkJvXCu22Xftsl6yoqTYO30zn6bQefppFVCt3K42oa4WwGTAMfkDcjWBrYCjLu8MVzbVcx9O9kLsUS5u0Fi6wLuqZx24ZubMviWOCtVJa9y6L05C1yjoN/oNT7xkKB9lyezoJF0nGxXRdsjbjtkHMVBoo6X3EM9ODcs8GvO/IFNqYzbHRcrk9laQj6sSiWStcZWMTY1GnCxAUM2yIMZIwgDbrG7p0tTI13MDWfonzm9G38CRJCCCG2jQ2pDViuxeTyySzvXU5HpmO7X/Of//wn4XAYy7JwXZcvfelLzJ8/n8985jMf6LxLly5l+vTppYAKYNasWbiuy/Lly6mtreWUU07hoIMOYsOGDTQ0NPDnP/+Zz3zmM5SVlQHw+uuvs3jxYv785z+XzqGUwnVd1qxZw9SpUwGYMWPGB+rrjiBBlfjIKSsrwzAMnPb1VAbKcLwBLFNDR2NjuIxIIYuBQqc4dyhq6rTmLF5PZvBoGq3ZAs+0beDVZJYu26HDcsB1cU0PStfxaDoxj86+kSCfH5hvRBwMTcNSlDIyPbbNmICXpO2yyDFIhqPoNkz2aPTrJvVek2NqK/jqmEpe6s9gKYWhFYszbFpIwga6CjZp28an66Qcl5hpYOGSMzzoQM4XoEw5aP4gFhoOCnMgsNKHnFMfmE2m8061woCuoaNYmsoQNXXyrkvZQPanPW/R2d1P0DDw6jA1HKC/YLE+Z7M2VyDhFM+cVoBS2MrBUhA0dPoKxcIajf53Fthu9PtIZzJ05gtYmkZGc8mlc0S8JkFdAzTyjkOtz0NzwMcEH/w7nSdn+KgKh/j2uBqaqw36N0aJVdcydsoe2/5DJIQQQmwDDeEGPLqHt/vexqt7qQvWbfdrzp07l5tvvhmv10tDQ0Op6p+u6yMKvlnW+6mzu3kzZ85k4sSJ3HHHHZx11lncc889LFiwoLQ/lUrxjW98g3PPPXfEsU1NTaWvhwZuuwoJqsQuy3VdWltbicfjlJWV0dTUhK7rNDY28uabbxJbu4xpgT5UeS1mIILfLtAVKcNvW6S8frAKNG5MsLGmgYdTWbKuotzUyeXzdMV7SWk6tZkEKV+YnK7jRUPTdTweD2O8Xj5fV1laBPegshBfbqjiTxu6i+XPDYPDysLUB3wsSWVoL1hMrywj2ZdC85rsF/RzakNVqQBEy8C8pO6CjVd3SdkOimImCt5Z9DbpQt61sDDYaDlo6HhdC1AUDIMew4euikfYABroA4HVYGGOwsA5h2arcq4iYGhMDvp5PZnlrVQWy7KZA5iaRspxMXQNx9V4ujdJlddD1BwI0BT4dI3sQIZtcPie5brU+b0j1oo6qCzEoXaSVsck7/GhKZcgNlUeH3Mro0wM+knaLmFDY1zAzwGxZg7tzwyvOli5F43stf0+XEIIIcQ2sH/t/kAxY1UfqmeSf9J2v2YoFGLSpJHXqa6upr29vfTecRzefPPNYcsVbcnUqVNZsGAB6XS6FPQ8++yz6LrO7rvvXmp3yimn8Oc//5mxY8ei6/qwDNl+++3HkiVLRu3frk6CKrHLam1t5aVFi3hb8xD3dHJw1ub4KRNpa2tjzZo1ZDMZajIZCoUCSxrGk/IG0F2XrGnic2x0XWO94aWQy4Gj8KHotg0sq0BZVzvJyjo2+qO4SoHj4jEUDhDT4HN1wwMFXdP42tgqdE1jaSrD1HCQKSEff27vpT1XYH3eAqWYEPBR4/Uwxlcs5PBcX4rbN/SU5iVFTYN6r4kv6GdlKkveGT5UD6UoKA0NB69uFAs7mB5sXEAfUSMvr6Dc0Mk6LtqQrNXuYR8tmQKOUsQ8JhnHYWrIj6tc7uvsI+s6aE7xyo5SOGh4NI1DyyMsS2ep9ph49OK6U4/3JFBKYRjFDF3GcQkZOlVek3lVZaXnMvRZ7VUWpWxDnJxh4Gg6BUzKPB7mVsRKgepQxW0jtwshhBA7M13TmVk3Eyj+MTiRSOywvhx++OFccMEF/Otf/2LixIlcd911Iyr1bckpp5zCpZdeymmnncb8+fPZuHEj3/rWt/jKV75CbW3tsHbz58/nqquu4qSTTsLne2e0yve+9z0OOuggzjnnHM444wxCoRBLlizh0Ucf5cYbb9yWt/uhk6BK7DIcx2HhwoV0dXVRU1OD1+tlOR6e9cVIWwWWrG1nYzBMojPOem+QMtULSlHf34OuG2QjUfK6idexiBVy6LqOU1FFf9DPMgWG10s4k6G5o41IohcrWoHj8aL7/PisAmPyadYFwjT6DKaGRi4o/VJ/hpcTafKuy8uJNF2FAnnXZVZ5hGf7kkwM+pgaDrKoP8XC/hTPxZM0+b0sT2XZULDIOgqfBn0K4rliwYvhdexUserdQMrJwcWjGcX1tjZTyFMDcsrF0ItD+sKmMTBnSWPvaIh+22FjwSJgGCjgnxv7SdgOHu2d4YI+Q8dBJ2QYJB2HCUE/+0dDvJxIk7RtpoUD1HhNOvMWPZZFR96m3udhcsjP4ZXRUavxFWrHMD6j2L2QY5nuZXJZmLMaa4cFqkIIIYTYdv77v/+b119/nVNPPRXTNDn//PO3OksFEAwGefjhh/n2t7/NzJkzCQaDnHjiiVx33XXD2k2aNIkDDjiAl156ieuvv37Yvr333punnnqKH/7whxx66KEopZg4cSL/9V//tS1ucYeSoErsMhYuXMjzzz+PbdssX76cmpoa1mo++pWXqkyCdZrBghVraQwH6axsoFLz4LUtIvkMHsPA6/HQqMN4ZROpLqcjVkVrRQ2r8zaeZAq7UGDyxg1MWbeS5TVj8dg2Y9P9JJsmooWCWI6LgYbj8fGn9h50TRuWVRm62O2SVBalNHy6ztJ0lnq/l5PqKgeKNFjEbYc+y6Y1VyBpO6XheQnXxXFcnIEhdMMLrw5EU65CaQA6JorCYKlxTSu+NmEoMHTIAFnHYVLAx5yKCLPLozzZl+Cp3iRTwwESlk1nvljW3FIwGDYqBeMCXr5QX0HMNGj0+zggFiwt8Dv4/qX+DGuzOVKOImLqNA0M1RtNU8BPeVkZeddlf13n1IFy6EIIIYR4/4bOX9qUx+Phpptu4qabbtpsm5aWlmHvN52Dtddee/HEE0+8az9efPHFze6bOXMmjzzyyFb3YVchQZXYJbhK8XRPP2/FqqnVNcLr1tDT00MoGMXwRegNx3A0jUI2i+XadAQidNSHKMumsE0TXTfwK4c1uk6ht8CYaAXPBcroyFrE0WmORujI5FhdVk276cNULobrsjEQoQGXeU31LE1lWJXJM6s8wtJ0dlhFO2DYYrc+XWduZQSNdxbCPTAaoGvtavoTebo1D15dI+EU5z5pFOc++dHQdI2MUuQ3qQIIgK7jx8VCR9c00qpY2c/Ui1X9hgZVOsWS8Uq5xTlRgKZpVHhMJgb96JrGnPIo63IWSdvBbxjUeAxWZ4Eh9QLH+j3MqoxwxtjqYVmnTYfkHVwe3urAaDDYGjpPSgghhBBiVyVBldjpua7LvctX86QRpL+smlW2zZ7RCg4uj+BLJil0rqXfG8D2+mipquMNVyfnNVBo6MrF0g0MTcPIJOnx+bB9Ecr7kiQdDzEUHcEoy5SOpXvIh8swA2FMx2ZCbydN+SSHjavlK2OreCGe5rYN3SxNF4OmoRXtYPRAoRiEDJQSX/IGPPUQdbWT6IrVkjZM1EDfHN3A1DQOqojSX7B5PZ0Z9VkUq/jpxQqBAwGVTnGu1Ka8GijlknOLGS+D4npVq7N5HtoYJ+Uo9okEmB4OsDiZIWE55DNZAla+OENLKw4pnBD0M7citk0X1X0nyyfZKSGEEELs+iSoEju91tZWnluxioInwBjHoisQRFXVYGoW1VVV7NXdTV/PBrw+H04kSq83gKIYqPQFI5iugwG0RytwDAM0yLgOYb+PTC5PVIFpmljKot3VCDgWBd3ANU0OtRPMri5H17TNZldcpXghni5t/3xdxagBSP/GTlb4wlixCiL5LGlvADNgkHUUEaA64OXIyghjfV5+3drJa/1pko77TvZJ09CK/8FWxSDJoLjuVXH/8OvlB0oGDsZbzkCTpOOQU4qV6Sz9lkXKVWzMW3g0DcdxCFgFwh4DPVcM7E4eLBsvhBBCCCFGJUGV2GlsrkR6PB6nzMoRDpex0Qnh0zRqNEUymcR2XZZqHvqqxxDNZ4lpCscwcXUDDTBdB9118LsOKEjrBn7lEtCgPpWgXjlUNdSwxvCxPJ3FdPNY/gA+12WfynIOm7hnad2ETbMrripW7/t3bz+vJLLYroulFCfUVoyoeAcQq66lt6WddDbLlGyKNtPAn7HoMX2UBX00B/2MC/hYksqRsh0qNXBdm7xpogYW5Q3qkB2SebIZOlDvHdpmtisg5Sie70uhFHh1jfa8jQOUe3Qcx6DOyrJ/53pqlANV1RxYyrgJIYQQQojRSFAldgqu6/LSSy/xxhtvYBgGkUiEjo4O/H4/mUyGhmQfffE4XUojmsti9nXRASyuH8/b1Q2YKAK5HJWWRYXm0qcgqxXLdQecPB7HIekLoDSNnkiMsMfkSK/LnJoyGsaO5db2PoKGxvign5ihMS0c4HA7TSKRoLW1tRTgDTU4HHBVJsf6XHGR26Tj8L9tXQAjAquxU/bgwIKipS9DrraeBtPkcGy6/AG6vH6mhoM4ruKezl425Ivnq3YdPLk8jq5je32sc3VcpYFSWAML5Y5mtIBqkEaxEIUC1uftUoCWtByChs6RY2o5ulwnXFHN66tb3v83VQghhBDiY0KCKrFDDWanVq5cydKlS8nn8wQCAXK5HPF4nHA4TF88zgrDT69hEMmmGJvux1KKDbEqVlSPIen147cLKJ+fqqxFLNlPwRfEMr14HBvTsdEdB9N18Hs9WJpOv6aTr6ujqbGaF+LpUil0n67zhfoqGvq7WfTKK9i2XVqJvLm5eVifn+2M04fJlEiY1lyBHsvG0DQStsM9nb3sEQ4MK9yg6Tqfnb4XNUOGCioUL23oIW87vJxI01kokLEdLAUdjkskECAKFByHVgdc/Z2hgLgKXVco9FGCKMXmAq7BUunFOVfFloYGMY/BMdXlXDCpAVPXsSxLgiohhBBCiK0gQZXYYYZmpzKZDOl0GsMwSCaT+P3+0muJq7O4qhZlmmQr6lmb6qOxdyMJXwBdKUxcUr4AFPKM7evCsW3SDRPI+0NEcmn6QlFM3cHWDSzHxWMW12y6r6uPvSLBEaXQ23J5/H1xlmke7Np6zN5uJvTFaRpXnDv16rr1JFatwHYcUsEKlFI0+X105gtYrqLaa+LRtBHVAWHkEMI723uGXXtDzqKrYJNwXFzAoxn4Al7sTHF4YXGdqoFoyHXxAK6uowGegWqAuAPzsJSLd+h8KwUFhrxVxf6UmTph02DfSJC9wn5e6s/IHCohhBBCiPdAgiqxw7S2trJ48WJ6+/poDcXoKw9TZuVpyiSoq6vDMAw6OztJ+IIo04PHtmiPVkAoDFV1jLOyBD0e+k0Ppq7j1cB1HFzHwdJ1bMOkK1KOjmJsfCM9kTLS+EHXqA34MPV3yp0PLYXe6PfR4g/zfLCCQs7FG6xgmj9MLp7m1vUbWdmXJRmoYLYXDurdiL8iwicmNrAkneXezj48mkadz8tYn4e2JW/Qv7GTWHUtY6fsgbbJEMJNr13v8+A1NAx3sFqfS9x2yA3LOik05TI2k2BGWQStKkZH3qY9X6Atl0ehitX7KAacDX4fAdOkPZcnbrsEtWLGq97jAQ3KPAZeTaerYPNITwKfngJgZnh4dUMhhBBCCDE6CarEduM4DgsXLqSrq4uamhpmzpyJYRhAMUu1cuVKkskkrcEor1eNxTV0/IZBZTbO5Mm7UVFRwcqVK9mwsY8O02CjP4jX62WvkJf+gs20aDVBy+XJngR1uQx9+Txx04syvHhtm7HxLjqjFegKCqaHslSSBreXnmgluqYI+70sWdtGV9DPfmXlREyDcQE/B5WF+L9sGeGKHGNdm3W6SSFWRlsuT3vBIqPrxA0vTxUKnGAYfL62jOaKCAeXh9kzXMx8jfV5MF5fxOMLn8dULjGfF1cp1jdMGFY98IBYkDeSQZ7uS+DXdKq9JmHDoM9y8Wjg1XX2DAdp8Jnc195Dt+OiXEWNsjmzKsR/H7Qfmq7zQjzNEz0JHt3QxXorT143MJVLjenlK2Oq2SPk4/4Va3jOLmDoXqrDQc4dV4up67Tl8qzM5FiRzg3L1klQJYQQQgixdSSoEtvNwoULef7553Ech1WrVgFw0EEHAcUs1bp16ygUCvQFy3ANncp0gr5IGf3eABUVFTQ3N9PU1MSEtWuZ1N3Pm7qXt/IO63rjmK5Dsnsdn9h9dzqdCJ29NrqdJjJQBtxwXQqGh9psiolOAau/j2A6SUOyjw2RcrLhKATDPBctR/N4qM05nLXbuNIcqKaAn/JwmF7XpVzXaQr4geICu2k0qvxeAq5JtKFy1OqALS0tPLZ0Ccm8RSAUhnyWZzb28jTR0twtVyneSmX5fdtG4o6NF43OgsWh5RGe6UviKEW9z8cpDZUcXB5mbmWMp/oSKKUxtzLCJ8rCpUIYB5eHOagsxOzyEP9YsYZ12SxjAwGO3W08syqirF/6Joe8+AjVvjC9gQgHTp/OIRWR0jpaz/WlaMtZw7J1QgghhBBi60hQJbabrq4uHMehsrKSnp4eurq6Svv6entxC3nKggFihTymUvTHKvGh2C0afSdQ0XUmjB/PhPHFEuZ/WvQ6r+USTIxFKE+kGJ9Lc9Zuk7j/+XYSXW1U9/eUrpEKhGgOBdknYPJ21zps2wagob8H+ntYVttIb6SCqG2zIWexNpsrBVWbW5PqhNoK7unsLQ3x23dM1YiqgADxeBzD9BL0mGTSKfw+D9lw+Tvzp5JZ/vH2Kv6dtekY+GfoaoqOvMXn68o5tqZ8xCLCh1REOKQistnnrWsah1bGOLRynxH7+jd2oqwCsxpibFy7hon99cMqE452v87A8xJCCCHEruH000/n1ltvBYprcFZUVLD33nvzxS9+kdNPP33U31nEtiFBldhuampqWLVqFT09PRiGQU1NTaly3pq3l9Hb2YnmujTrGn6fF6uiijEek2Mm7TbqP3pd0zisuozg2pXYnQlM06QsFkXv72bqxvW09m3EYaDunabh8XgYP2E8uTUrcBxnxPkKhod+j49+3SCERspRw641tKDEoK+NrWKPcGBEsLWpsrIyIuXlJAHDLjBt2jSsCeN5vb2XJaksTipJV+tqctFqdG8AxzApKDA0jXEB/6jX/iBi1bUYHi8b167B8HiJVdcO2z/a/Y58YkIIIYTY2R111FHccsstOI5DZ2cnDz30EN/+9rf529/+xv3331+qajyUZVl4PJ4d0NuPDgmqxHYzc+ZMoJixqqqqoqamhieeeIJ169aRT/YDisqKMsx0gpm1MdxwCE3T0CjOx2praxuxEPBgBisejxOLRulau4Y3li4lnSug6zqBQIDVvhDL68cTiMVYFK1hfKQbXddLgZUC2mOV9AYjeG2LMV6XYKiciPnuf73ZXLC1qbGNjbyStelOpJgYDbPf5PHouo6mFecw2T2trIt30FZeQ9q1cQ2dCq+Xr4yp3C6V98ZO2QNgWNEMIYQQQnz0+Hw+6urqABgzZgz77bcfBx10EJ/85CdZsGABZ5xxBpqmcdNNN/Hggw/y+OOPc9FFFzF//nzuu+8+LrvsMpYsWUJDQwOnnXYaP/zhDzFNE6UUl112GX/84x/p7OyksrKSk046iRtuuAGAm266iV/+8pe0tbURi8U49NBD+dvf/rYjH8WHSoIqsUWDmaXB4KaxsXHUYGdz7Q844AB0XaelpYVFixbR3d1NOp2mtqKcdH+cTH+coNdDQTNIxeM4jkM8Hqezs5N169aNWCdK1/XSelFtS95g6cIXSOYtDMNA1wwKhQL9kUosTWNsup++dJjI2PH09MbxxHup7++hPVbJm/XjiQfDpL1+0kE/E3wemrbhPKKXElkecU3ywSirXJ26RJaDy8OlgKytr5JXX0/BmsUsK6ujpnkCx04uzukaOixvW9F0ncZpe9HIXtv83EIIIYQYnXJdMosWYa1fj9nQgNpttx3Sj8MPP5zp06dz9913c8YZZwAwf/58fvazn3H99ddjmiZPP/00p556KjfccAOHHnooq1at4swzzwTg0ksv5e9//zu//OUvueOOO9hjjz3o6Ojg9ddfB2DRokWce+653H777Rx88MH09vby9NNP75B73VEkqBKb5TgODz/8MCtWrMDj8VBVVcXzzz/P+vXr0TSNiooK5syZw7hx42htbaWvr4/29nbWrVuHaZpEIsX5P83NzcTjcSzLIhQK0dfXR28yRaCsEte2UMEQ67q6MUyT8ePH09nZSVdXF7ZtU1tbS2dnJ/F4fET/+jd2YiqXQChMOpVCMw0UUOHatOsa63QPvoLDGq+f3qp6CoEYAAlfkIzPj9I0NE3DNk1mxMJblSHaNGjcNKgcNNraV0OzW4OZogkbO/nSZsqtCyGEEGLXllm0iPidd+LmC2geD55jj4E5c3ZIX6ZMmcLixYtL77/0pS/x1a9+tfT+v//7v/n+97/PaaedBsCECRO44oor+O53v8ull15Ka2srdXV1HHHEEXg8HpqamjjggAOAYgGyUCjEMcccQyQSYdy4cey7774f7g3uYBJUiVG5rstDDz3EokWLUKo416inpwelFK7rous6+XyeV155pVj2fMMGbNtm48aNuK6LqevEezZSHvAzrqmJsrIyCoUC3d3dxaySrlNdV4dlWdTV1bFmzZrS2F/TNKmpqWHdunWl92VlZSP6GKuuJebzQj4LpkEhECBbKFDT01lsUFWDQ4S1fb1UppOs0z0kfEGi+QyOppMzvUSVQ73XQ9TUtypD1NrayqJFi0oZNKUUmqaNCLJGW/tqKMkcCSGEEB991vr1uPkC/t13J7dsGU57+w7ry+DvLINmzJgxbP/rr7/Os88+y1VXXVXa5jgOuVyOTCbD5z//ea6//nomTJjAUUcdxdFHH82xxx6LaZoceeSRjBs3rrTvqKOO4oQTTiAYDH5o97ejSVAlRtXa2srSpUtLARUwrNiD67rYtk17ezsbN24knU7j9/spFAoo16Uw0O7lRS9j2RZHHvNZGhoayGaz1NbWks1mAUilUqxZs4ZwOExTUxN+v780zLCurm5YsLKpofOEOlM5uvoTmD4fz3bHKYSj7B4Okunp4G09QL/XT0jXieYz1Pf3MHnjOlbVNlERi1Ln8251CfHevjjLNA92bT1mbzfRVatIxOMjhilurnqgEEIIIT4+PGPGoPu85JYvR/N6Merrd1hfli5dyvjx40vvQ6Hhv5ukUikuu+wyPve5z4041u/309jYyPLly3nsscd49NFH+eY3v8kvfvELnnrqKSKRCK+88gpPPvkkjzzyCJdccgnz589n4cKFo/5h/KNIgioxqng8PuyvGaMxDAO/309lZSXLli0jnU7juu6wNllg8dJllNc1sNtuu9Hf308+n8eyLAqFAoZh4DhOKYU8dCjd4NypzRnM9oxRe/L2slW8vGIVfYUCq6rHont9rLYc9unv5yBPhtZsnkrloEJhWssq2d1rsJ+Toqyinn3HVG110NPiD/N8sIJCzsUbrCCqHGpGGaa4tQUthBBCCPHRFRzIBg3OqbJ20JyqJ554gjfeeIPzzz9/s232228/li9fzqRJkzbbJhAIcOyxx3Lsscdy9tlnM2XKFN544w32228/TNPkiCOO4IgjjuDSSy+lrKyMJ554YtQg7aNIgioxqmg0SiAQIJlMbraNrusYhkFvby9KqYHqdtqwjJYB2I7LqlWrStUA4/E4GzdupLu7m9raWlpaWujq6qK1tXWzc5S25IV4mocdg3jNGNbE+3Eti711xSoHOmyXvTe2MsWyWBet4M3aJhxdp0XT+JRm8emQh+ZYcKuLQxRiZYQrcox1bdbpJlrAxOzv3uIwRSGEEEJ8PGm6Tmhg3pHruiQSie1+zXw+T0dHx7CS6j/96U855phjOPXUUzd73CWXXMIxxxxDU1MTJ510Erqu8/rrr/Pmm29y5ZVXsmDBAhzH4cADDyQYDPKnP/2JQCDAuHHj+Oc//8nq1as57LDDKC8v54EHHsB1XXbfffftfr87CwmqxKgG5wltSaFQIB6PU11dTSwWI5/Pk0wmixkupVAU1zrSgGQySVtbWyn71NLSQjweZ9myZSSTSXK5HPl8HhieoXKV4oV4esRCuEO15fIUlGKSz2CtUhSAFXkLD1Bn6vQ3NNGSztLjC2Kh4bdtuoNhFuUsJr7yyrCKgu+mKeCnPBym13Up13X2q69gTNS/xWGKQgghhBAfloceeoj6+npM06S8vJzp06dzww03cNppp23xD9fz5s3jn//8J5dffjlXX301Ho+HKVOmlKoFlpWV8bOf/YwLLrgAx3HYa6+9+Mc//kFlZSVlZWXcfffdzJ8/n1wux2677cZf//pX9tjj47OEiwRVYgTXdVm5ciWFQmGL7ZRSpb+4BAIBDMPA6/USCQToi8dLi8eG/H5M06Svr68UvDQ1NdHR0cH69etL10wmkyMCuRfiaW7b0E3edfEN/CAoDqt7x2BRiKXpPGXKYTfdwcomaAr4yfh8POuNkPdFyesGOcNDp9ePhmJDIMwrPf1UrFy51Rmy0eZK6RXRdz1OCCGEEGJ7W7BgAQsWLHjXdkPnzA81b9485s2bN+q+448/nuOPP37UfYcccghPPvnkVvbyo0mCKjHC2rVrWbVq1Va1VUqVMk2DVf36U0kcQAdcIJXNgGGQz+eHlSRPJpNEIhE8Hg/ZbBafz0dZWdmwNq8aAXLKwx6RkaXJB7NYa7M59o+GyDk5Ut3raMomsQp5gqbLw5ZLf9CLr5DH1gyC+RwAlekEBY+Xdttl3bp1tLa2blW2SuZKCSGEEEKITUlQJUZYtWoV6XQaXddHFJ4Yjeu6paF7w7YP/Nfn9RGLxfB4PLz00ku88cYbpQDMMAwCgQBer5e99tqLpqamYWXLE/4wTsN4lmiMKE3+fF+S365sI12wCHk9fGPiWMZG/axYsYINGzawob2d1spGegIh8IfwODZVqThe18HyePEol0a/F5/uDsuQbe1aVEIIIYQQQoAEVWITruvS399PLpd71+p/WyuXz5NKpVi1ahWdnZ0kk0l0Xcfn8zF27Fh8Ph81NTXMnDkTXdeJD5Qor6mpIdXSwp4eD29VjSHp8/NGMsMBsSCmrvPa+nY6e3upyqRY5/HxVCHNxQfPIB6P09PTw4ZoBe3hMlBguC4e26YqGSeWz5DwBalSNs0BD55odFiBiU3XooJ3r0QohBBCCCE+viSoEsO0traWik24rovH48GyrK06VgHtscrSArv1/T1oFAO1eDxOf38/ruuWxvHm83na29tLC/3W1dXR3NxMWVkZpmnS0tJCsr+ft6PVLExk0b02yzJ5dE3ja2MqcTvbsbMF2jQTI18gvWY1rWNrSkUzNlgKU7kECzlyphfTtYnlM4zLJPA5ecaNG8fEiROpqKgYVmBiMKjbtEy6EEIIIYQQo5GgSpS4rsvbb79Nd3c3UJwvtbUBFRQDqrfqx+PoOsbAsMGG/p7SuYcyDAMAy7Lw+Xx0dnby9ttvo5QiHo8zduxYOjo6AEhXVOO4MFaDLtdlaSpDa2ua0LoWpqby9Jle6gyYYCj6+vrIZrP09PQQMf2lOVSm47Bb13oaEr2UV1dTXV3NzJkzR81ADQZ1UiZdCCGEEEJsDQmqRElra2tpEd/BbNLg4rxbI+EL4ug6lekE68uqeLt6LEApYzWU67p4vV4cx2HFihUYhsHq1atpb2/H5/NhmiZjx47Ftm3Kk3GMYCUbFPh1nanhIPGe9QR8Pg4yDNavX08gEECPxXi4dQNv9/XjMXw09PfgOM6wzFlZWRkHHHAAlmWxYsUKVrz9NmGPwSoMkpFy9p4wngMbGwGkTLoQQgghhNgqElSJkng8jmVZaJpWCqocx9nqwCqaz2C4Lqsr68n4fBQME8v0AO9krAaFQiHC4TC5XI50Ok04HC6VcG9qaqKzsxOfz8eMGTNo7o0zzgjQ5fUzNRzkq2MqWeek8XiK566vrycUCrHYUjyHj0y4HPxRNgYieG2LWCFLXX8PHtPEcRy6urpKiw87lsWGYIRl9c1o/XmeL4A2dSIHyxwqIYQQQgixlSSoEkAxc5TNZrEsa8RQva3NVNX399AditEZKUMpDVfTSfv8JHxBxup9pfMGg0GamppwHIdQKEQqlaKvrw9N0wiHw3R0dODxeCgvL6e5uZnmZpixybUGs0eD2aTe3l7+vbIVRzeoSPWwLlbJipoxhK08plKYpocZIS89PT10dnaSz+fxer3kbYsejx/X56cp2UNfvmxY2XYhhBBCCCHejQRVAigO/Vu+fPl7mkO1KQ3w2hbhQo686ZAzvXgci5iVw+v1Yts2Pp+PiooKMpkMmqaRyWRKpdUHVVdXM2nSpC0Ou9N1fdh8qI6ODrzxPhzNS08ogqPpGMqlMp2gL1JGKhCip6cDwzCora2lq6uLRCKBZTuUFbJszGdZH4pR7fMPK9suhBBCCCHEu5Gg6mNs6HpM3d3dpQV4M5nMsHbDqvoVssUFfzep8Dcoms+UikN4XIcpvZ1MLGTwDCzsq5SioqKCbDaL1+vFNE1yuRy2bRMMBtFdl1x3J0ZDbaly4NasGeXz+djDVPgyfbTlCuQNDx2RMnrDMSojEQ6pK6e2J0pNTQ0zZsxg4cKFpFIpXMeh3NSY4gO9oY69J4znoLLQ9nvoQgghhBDiI0eCqo+xoesxDa5LNdpQv6FV/QqmCQq8jo3humiaRn28u9S2vr8HUCR8IcqsHLV9G8lqGhmlSutT9fX14fP58Pl8+P3+0pwtK5fDSqdoTfSSXNfKLKVwQ9FSH3XT5JWsjVVWTqPfx0FlIfSBtbTKy8spi0aZlEiQQ5HwGPgKWZpqqvjM/ntycEW01BYgEAhQVVVVKps+pWks++wzfbs/cyGEEEKI7amjo4OrrrqKf/3rX6xfv56amhr22WcfzjvvPD75yU9uk2s0Nzdz3nnncd55522T830USFD1MTZ0kd01a9ZgGAbhcBjXdXEcpzQHamhVv5aKWgDqE730hKL0ewPUDzmnBjTEe2hQ3TCQURoseuG6Lu5AIFYoFMhms5imSaFQwOfzoZSLi0L5gnTmsqxasYLy5omlNaOe7U3wRk+KoK3jGzj3weXFuU+DQwWf6uplXc7F1QxCPi/HTGpkVkV0xL1L2XQhhBBCfNS0tLQwa9YsysrK+MUvfsFee+2FZVk8/PDDnH322SxbtmxHd/Eja+Q4KvGxMXSR3Z6eHvr6+kpznUzTRNd1KioqSlX9ekJRfI6Fz7boCUUxXJdoPgNKDX9BKaAazdAgK5fL4boulmVh2Q6uAjubIe9CXy5PNBotBT9xjx88JtPCAfKuO1BQYvgQwVQgRLisgk+MrcMMBFiXH32OWFNTEzNmzGDKlCnMmDFDyqYLIYQQYpf3zW9+E03TeOmllzjxxBOZPHkye+yxBxdccAEvvPACUBypdNxxxxEOh4lGo3zhC1+gs7OzdI5Vq1Zx3HHHUVtbSzgcZubMmTz22GOl/XPmzGHt2rWcf/75aJqGpm26cM7Hk2SqPsYGA4nFixeTSqXQNI18Pk+hUMAwDFzXpbe3t5SJSviCRPLF+ValOVWloX8K0EDTBr7eMk3TSpkwXdeLw/t0HdP0kFYKwzToiidYuXIlY8eOxev14g1EedgxWJLK4tP1UkGJocMYE/4wTsN4lmgMa7OpTQtdCCGEEEJsa8pVbFgZJ9GTI1LuI1Sz/QKQ3t5eHnroIa666ipCoZHzw8vKynBdtxRQPfXUU9i2zdlnn81//dd/8eSTTwKQSqU4+uijueqqq/D5fNx2220ce+yxLF++nKamJu6++26mT5/OmWeeyde//vXtdj+7GgmqPsaGBha9vb309PSUhucNBjmu6xaH9PX30MDQtaYGvx4Ipgb+U/TuPzAG18IaDK40TaO2tpZ8Po9t21RXV9PV1cXq1aupqqpixowZ7DNuHHXxNG25PI1+HwdEA7S0tLB48WISiQTNzc24XV2M0Sx8VXWleVdCCCGEEDvChpVx3vzPehzLxTA1mvcvI7ZPbLtca+XKlSilmDJlymbbPP7447zxxhusWbOGxsZGAG677Tb22GMPFi5cyMyZM5k+fTrTp78zz/yKK67gnnvu4f777+ecc86hoqICwzCIRCLU1dVtl3vZFUlQ9TE2OGyur6+PyZMnk0gkWLNmTSm48ni9rA9E6B9S6Q+GVAIcrP6nVDGO2sr0r67rKKXQdR2/308+n8fv9xOJRPD7/SilSKfTAKVAKx6P09zcPDCHqjiPqqWlhUWLFpFIJOjv76elpYVoNMrB1WU011duj0cmhBBCCLHVEj05HMulamyY7nVJUn2F7XatwekVW7J06VIaGxtLARXAtGnTKCsrY+nSpcycOZNUKsX8+fP517/+RXt7O7Ztk81maW1t3W59/yiQoOpjbOiwOdM0sW2b7u7uUgXAtYFIqeqfMWRB4KHbBrNYoG3FoL/hNE0jEAhgGAbjxo0rlVwPhUKk02nWrVtHb28vmUwGj8dDLBZj3LhxpZLqvb29JBIJAoEAuVyOiooKpk+fjuM4PProoyQSCSKRCLvtttuw44QQQgghPgzRSj+GR6d7XQrD1AmXe7fbtXbbbTc0TfvAxSi+853v8Oijj3LNNdcwadIkAoEAJ510EoXC9gsIPwokqPqI29I6T4PV/wbLind1deE4Tmlo3tCqfz2hKAlfEKC0rS8cJekLomu9uPBOkYp3MTip0efzYZomkUiEhoYGNmzYgGVZrF+/njFjxlBbW8vq1avJZrO0tLSQSqWYPXt2cZif69LR0cHGjRtRSuHxePB6vaxcsYKlb71FKp3GcV18Ph/t7e2l44QQQgghPiwNk8oAPpQ5VRUVFcybN4/f/OY3nHvuuSPmVcXjcaZOnUpbWxttbW2lbNWSJUuIx+NMmzYNgGeffZbTTz+dE044ASjOsWppaRl2Lq/XO+oyPB9nElR9xG2ajQJKwcWmZcXD4TB9fX2l9PHQqn+lSn/wzjbHJZzP4GxlMDVIKYXP5yMajTJp0iQmTZpEX18ftm3j9/vZsGEDuVyuVCzDNE1c16W/v5++vj6am5tpbW1l3bp1AKVKhatXryabTpHJ5hgslzF43OLFiwE2u3iwEEIIIcS2pukaYyaXM4bi7ySJRGK7Xu83v/kNs2bN4oADDuDyyy9n7733xrZtHn30UW6++WaWLFnCXnvtxSmnnML111+Pbdt885vfZPbs2cyYMQMoZrzuvvtujj32WDRN48c//nGpuNig5uZm/vOf/3DyySfj8/moqqrarve1K5DfLj/ihmajbNsmHo+X9jU1NbH//vtTVVVFLBajoaGBYDCIYRgANCR62aN9Dc3d7ezRvob6/h7q+3tGbAO2KlAZDOoMw2C33XbD4/GQyRQDtaEBHhTnUhmGgW3bpFIp0uk0iUSCDRs24Lou8Xi8FAgC2LZNMpmkULCKwxAH/hBk2zb5fJ7u7m4WLVok44GFEEII8ZE1YcIEXnnlFebOncuFF17InnvuyZFHHsnjjz/OzTffjKZp3HfffZSXl3PYYYdxxBFHMGHCBO68887SOa677jrKy8s5+OCDOfbYY5k3bx777bffsOtcfvnltLS0MHHiRKqrqz/s29wpSabqI25Li9zquo6maaXAKx6Po5TCMIxStmpk1b/Rtw0et7lU8GBFQSgGOsuXLy8Fb4sWLWK//fZjxowZvP3226xevZrOzk58Ph/BYJBkMgmA4zisXr2a1tZWysrKiEQiAHg8HrLZbClAAzAVhDw6wcoqdI+X5uZmurq6hgWVQgghhBAfNfX19dx4443ceOONo+5vamrivvvu2+zxzc3NPPHEE8O2nX322cPeH3TQQbz++usfvLMfIRJU7UIGU69vvPEGFRUVWzWUbXAtqqFzqoYamsnq6+vDMAx0XcdxHJRSpQIW70YptdmAytC0YUMElVLk83l8Ph/l5eUUCgUSiQT77LMPruuWhv/l8/nSYsSD5deTySQvv/wyxx13XKn/uVyOZ599thQIappGTVUF8+bOwfaHePmVV+jq6hoRVAohhBBCCLEtSFC1C2lrawNg4cKF+P1+DjvsMMaPH7/FY95tkdtoNEoul2P58uUApQWA/X4/hUJhxBjazRktS2XoOmjaqMGW1+tF0zS6urqoqqoiGo2yevVqnnvuORKJBI2NjXR2dmIYBoFAgHQ6jVIKy7JYvXo1ixYt4qCDDgLgtddewzTN0twrwzAYN3kKjdP2Kq6BpeubDSqFEEIIIYT4oCSo2gUMVvB78cUXCYfDWJZFKpVi1apV7xpUvZvBSnyDlFK4rkuhUMA0za0un7lp4KRpGq5SqM0EZY5lYfp9VFZWMmbMGFauXMmyZctIJpO4rks2myUWixGLxXAcB8uysCyrdP+Dc6+gOMQxFouRz+dxXZeysjImTZoEvHtQKYQQQgghxAclQdVOzHVdVq9ezQMPPFAamrfXXnthWRawdYu8vZv+/n58Ph9NTU0sXryYXC4HgGVZpQV630/JTK9pkLc2P2zQUQo3m6N93To6OzvJ5XKkUqnSfsuyMHCJGhrRqhqCwSArV64klUqh63ppPpbruriuS0NDA+FwmFgsxqRJkxg3btx77rMQQgghhBDvhwRVO7FVq1Zxxx13lIKawaF4rutSWVnJxIkTP9D5Xdcll8uRSCTo7e0lm81i23YpWLNtG4/Hg67rWz0McNCWAiqUgoHsWDKdQjdGztuybZuuji76DY2GzvV4G96ZP6brOh6PByiWjH/llVdKJeN32203yUwJIYQQQogPlZRU30m5rsuDDz44apaotraWOXPmfODgobW1lba2tlI58qHDAAdZlvWeA6otGsyuKUUO0HUD0zRHvU8XsNCJ5y36++KlzJlSqlQRcEsl44UQQgghhPgwSFC1k1q7du1mA4QDDzyQ5ubmD7yIbTwex3EcysvLMQyDysrK0lpS79Xm1gcfsX0wcNM0/D4f9fX16KhRj3cBy3FA04iVV+DxePB6vXg8HmKxGLDlkvFCCCGEEEJ8GGT4305q1apVA18pYrEufL4kllUOsM0q2A0GJP39/biuS29vL1Cs5Oe67nuas7VVLZUqDf0zdJ1DDj2UxMZONrSuHfV4NfCyAmEaxo0jVSiQy+UIBAKlQhTvVjJeCCGEEEKI7U2Cqp3U4GK6kUg7dfXL0XUX6AWO+MAZKninwMNgxiedTtPf3w9QWpvq3QpUaJqGBrgDwZfH48G2bQzDwO/3k0qlhgdLSoHrgq5hKI2FLzxPvmBhDWmkAfomQV08keSFF19k9913p6GhgfLy8lLwJNX9hBBCCCHEjibD/3ZSEydOpLKyEp8/ha675HNVA4HVB+e6Li+99BKPP/44bW1tpQxVKBRC07StLqOulMJVCkO5oFxsy0LTNHRdJ5vNbtoYdL0YUOkGloL+VJpcoTA88NI0vF5vqbrf4HUSiQTLli2jrKxsmwx9FEIIIYQQ28bpp5/O8ccfv9XtW1pa0DSN1157bbv16cMmv5nupJqbm/nUpz5FefkkwCQQ7EXTtk1isbW1lTfeeIP+/v5Sxb/BAhCDQVE4HC5V/hsMYIYGOqW5V66LoxQoUMrFa5rEYrFhbd8pTuGCUjiuiwK8nuH3owEe0yQcDhMOh0f0O5vNDhkWKYQQQgghhtq4cSNnnXUWTU1N+Hw+6urqmDdvHs8+++yO7tpHngz/20npus6ECRNoavoBL774J/r7VxGJTGQgqfS+ua7LypUrS5mkRCJBOBxm//33J51Os2HDBjo7O0sL6fp8PnRdL5U893g85HK5gffuwDwpF8N1cTweUAqv18u4ceNYsWJF8aKaNjCXSsf0eoiGg/QlUhQGyq4bhoHH4yESiVAoFErrTXV0dNDR0TGs/9tibS4hhBBCiI+iE088kUKhwK233sqECRPo7Ozk8ccfp6enZ0d37SNPMlU7OdP0MGvWVzn66Cs56KAvv+/zuK5LS0sLTzzxBCtXriSTyZDNZnFdF4/HQ319PUceeSR77bUXVVVVlJWV4fF4aGxsZOzYsUyePJlIJIKmaXg8noFqfToYOug6rm5gaDpK09A0jXw+j2doJkrT0AeONTUdr8eDNjDUz+v14vP5yOVyFAoFOjs72bBhA5ZlEYlEShX/qqqqPvDaXEIIIYQQH0XxeJynn36aq6++mrlz5zJu3DgOOOAALr74Yj772c8CcN1117HXXnsRCoVobGzkm9/8JqlUqnSOBQsWUFZWxsMPP8zUqVMJh8McddRRtLe3l9o4jsMFF1xAWVkZlZWVfPe73x3xR++HHnqIQw45pNTmmGOO+ciPNpKg6iNqMIh67bXXaGlpoaWlhUWLFrFy5UrS6TTRaBTTNKmrqyMWi5WKVFRUVBCJREprU8XjcUzTZL/99mP3yZMJ+rzUV5RhDgzvM9BA11GmiatpWJZFLpejr68Pj8dbGiaoAYahU8jn6In3l+ZtDQ4tDIVCBINBQqEQ+Xyevr4+0uk0Xq+XyZMnc8ABB/CpT31KilIIIYQQYpehXJe2JW/w5lOPsW7Jm6htufbnJganT9x7773k8/lR2+i6zg033MBbb73FrbfeyhNPPMF3v/vdYW0ymQzXXHMNt99+O//5z39obW3lO9/5Tmn/tddey4IFC/jjH//IM888Q29vL/fcc8+wc6TTaS644AIWLVrE448/jq7rnHDCCdt27dOdjAz/+whyXZcXX3yRl19+Gdd1iUajNDQ0lBbJXb16NbZt4/f7S5mnwfWdmpqa6OjooK+vj2g0WspWjRs3jo1r1+CmU/QnHXSloWngopXqqeu6juM4dHd3o+s6mqaV/vF4fT78HoNUKlM8TkF7tIKEL0ilazO1oYG+nh7Wr1+P4zill8/nY+LEiey777476GkKIYQQQrw/65a9xeuPPohjFTBMDxMPPozYjAO3y7VM02TBggV8/etf57e//S377bcfs2fP5uSTT2bvvfcG4Lzzziu1b25u5sorr+R//ud/uOmmm0rbLcvit7/9bWl00DnnnMPll19e2n/99ddz8cUX87nPfQ6A3/72tzz88MPD+nLiiScOe//HP/6R6upqlixZwp577rlN73tnIUHVR0ihUOC2225j/fr1pTTs4ByoSCSCaZrkcjmqqqpoaGggEong8/lGlCj3+/1Eo1Fqa2vp7OzE7/cDkOrrxaMcgmVlpONxcr4AoViMvr4+CoVC6ZrawAK/hmHgOE6pRHtWuf+fvTuPs6uuD///Otvdt9m3zGRPhmyEsIMCLsgqVlFbaqs0yPeh/am1Vi3aryJaVGjVb9WHtioW/baKtvJFQUCBArJDCFnIvsxkMvt69+Usn/P74yaXDEkgwSwkvJ88YmbuOfeczzlzM973fX8+7zcegA9DyQY2tM3G03X6fUW743NmWxvDw8NomlatLKgUxWLxoJ+2CCGEEEK8nmXGRvAcm6aZsxnd1UNuYvyonu/qq6/miiuu4LHHHuPpp5/mvvvu49Zbb+VHP/oR1157LQ8++CBf+9rX2Lx5M9lsFtd1KZfLFItFIpEIAJFIZNpyi7a2NkZHR6vXk8kwNDTE2We/FBiapskZZ5wxbQrgtm3b+OIXv8gzzzzD+Ph47UP2vr4+CarE65tSih/96Ee1F/1ejuOg6zqJRIL58+dPa5J7sLLkqVQKwzDo6enB8zwGBwcZHR1l5+AIJR+K6TQBrVohMJFMEgwGmZycpFAoANViEr7v16b47S1ysW/fq2wwgqfrNBSyTEQTbJ4Y57RgI4ZhTAvOLMsiEAgc8fslhBBCCHG0JZtaMKwAY7t6ME2LeEPjUT9nKBTi4osv5uKLL+YLX/gCH/7wh7nxxhu56KKLuPLKK/noRz/KzTffTH19PY8//jjXXXcdtm3XgirLsqYdb++H3Yfjne98JzNnzuSHP/wh7e3tKKVYsmTJIbftORFJUHWS6OvrY3z8wJ9+RKNR5s6de8jrkfZOAVy/fj22bfPiiy9WN/g+dY3NTEyl0SwTTSls2yYajZLL5dBhzycRPj4aQ3VNZIMREpUibZmJPcUtqhKVIoZSTEQTGEoRKeTYunUS0zRrGa69Ga+T+R+gEEIIIU5eM7oXA9WMVaKhmXj7jGM+hkWLFnHXXXfVloV84xvfqH2w/stf/vKwjpVMJmlra+OZZ57hggsuAKofnj///POsWLECgImJCbZs2cIPf/hD3vzmNwPw+OOPH8Eren2SoOokkU6nCQaD+zXdNQyDU0455YABlVKKvr6+/bJXe6cAxuNxCoUCk5OTWJaF67pM5fJohoFhBSgUChQKBSzLqlUSBNB0jcFYfW16n7Hn8fbMS+U82/Z8vTfoas1MULAsIpEIkUgEz/NoaGgAIBgMHo1bJoQQQghxVGm6TueipXSyFKUU2Wz2qJ1rYmKC973vfaxcuZJly5YRj8dZtWoVt956K+9617uYN28ejuPwne98h3e+85088cQT/Ou//uthn+dv/uZv+PrXv878+fPp7u7mm9/8Jul0ura9rq6OhoYGfvCDH9DW1kZfXx833HDDEbzS1ycJqk4SqVSKWbNmsX37dhzHAarrozo6Opg/f/4Bp/r19fWxatUqXNetVenbG3ylUilM06xN6YNqgFZXV0c+n6dUKr10TKVQnseexBK+p8iGo9Om92WDEdp5KajSqAZZ+z7mui6ZTIZIJEJzczOhUAjTNKmrqzuCd0oIIYQQ4uQTi8U4++yz+da3vsWOHTtwHIfOzk6uv/56Pv/5zxMOh/nmN7/JLbfcwuc+9zkuuOACvva1r/HBD37wsM7zd3/3dwwNDfGhD30IXddZuXIl7373u2uVpHVd54477uATn/gES5YsYeHChXz729/moosuOgpX/fqh+dJNdZpsNlsrMZ5IJI73cKZxHId7772Xyy+/fL/5rnuzTlNTU5TL5ep0PF1n7ty5zJw584BB1Zo1a9i8eTMtLS0MDw9TX19PLpejUqkwe/ZsWlpaWL16NTt27Kj1s+ru7qanp4dMJlOt8AdoroPr14oAooDBZAMb22fjatVM1eKhnmmZqlcSDAa56KKLCIfDr7r+Sxw9r/R6E+JokNecOJbk9SYOplwu09PTw+zZs2vFuo6EvZmqRCIh72teZ17pZ36osYFkqk4Suq4za9asw+rjtDcbNTIyQqVSYfPmzWQyGZRS9PT0sHDhQlKpFE1NTUQiEUqlEplMBsdxiEaj2LZNPBTEz2WomAFKFRt3z7H3Tu/Lh6M0KYdUZoKQYVDxPHyoTSfcu/hx39jedV2CwSDLly8/YvdHCCGEEEKIo0WCqjewvWXU0+k0Y2Nj7N69u7YuynVdtmzZQkdHB47jkMlkCIVCtfVNlmXh+z5trS1kKkWKlenFJPZO7yMzgWWaKMDeU/3PMk1M0yQYDOJ5HpVKZVpQtW/lQCGEEEIIIV7vJPf4BrY3u7V8+XLi8fh+Xa5936dUKtVKomuaRkdHBw0NDViWRUNDA8vPPJtIRye2pteyVFCdCjiYbGBzcycjDS3UN9RjGAahYBBjT0B1yimnkIpF0V923lgsJsUphBBCCCHECUMyVaI2x9cwjFoAtfdx27ZJJBLMnj2bkZERwuEwF110EZOTk9i2zY6dOxlLZ9F0A029NI1v3+a+g/iY6WGarSLBUIhIJMLSpUtpbm4mOzJIUQfPDFCybaKRCJ2dnVKcQgghhBBCnDAkqHoDU0qxa9cuVq9eTV9fX60v1F6WZdHa2ophGIyMjNQq8e1dt7Vq1SrGx8fJ5/P7rYua1tw3EmfIUTS6DhBixowZtSIY6UKZoq9hOTZhQ2dW1wxWnHlmbWqiEEIIIYQQr3cSVL2B9fX18Yc//IHBwUEcx5lW/cg0zVr1PcMw0DSNOXPmoJRi9erV7Ny5k7GxMWKxGJlMBm/Peqm9XmruG8dQinilhO24qFyO/v5+AoEAruuSamggm8+TjEUIBMPMXtB9WMU2hBBCCCGEON4kqHoDS6fTlEolAoEAnudh2zaGYaDrOkopSqUSvb29tcBq8+bN9Pf3UywWKRQKaJpGOp1G0zRCoRBKKSqVSrU/Vm4KgGwwTKJcrFUDdD2PiYkJgsEg4XCYfLFIIBTCisaJJxLU1dcfz1sihBBCCCHEYZOg6g0smUwCUCqVUEpV+05pGpqmEYlEALBLJbSQxXiuyIDrUrFtHMfB8zwCgQBKqVo9/70ZLdM08TyPuZUC5ewk/ssKUVQqFXbv3k1jY2OtH5brurS0tNDZ2Xlsb4IQQgghhBB/JKn+9wbm+z6hUIhAIIBhGHR2dhKNRmtT/6KhIHY+x0DfbtJTE1QqFQA8z8MwjFog1tTUhGVZNDY2snTpUhKJBPF4vFo23bLgZWu1oFqyfWRkhC1btjA2NkapVKK/v5/du3cf69sghBBCCPGGoGkad91110G3P/LII7WZSOLwSFD1BpZOp1FK1UqkZ7NZfN8nFothWRbNyTgtFtTXpdAB5bl4nodlWcRiMRKJBNFolNHR0Vr2KhgMkkgk6O7uJhIMElEOQdcGz8U4QHCVzWbJZrO1bJX8IxZCCCGEeG3Gxsb46Ec/SldXF8FgkNbWVi655BKeeOKJQ3r+eeedx9DQUG0208Fce+21/Mmf/MkRGPHJQ6b/vYHZtk0mk8F1XTRNIxwO4zgOdXV1lEolknX1BCZGGM1lCOg6Te3t5MoVmpqamDNnDqlUiu3bt7N582ai0Siu66KUwjRNRkdHMXXQ8SmbJjgunuuCqbNvLK/rOrqu16oLplKp43Y/hBBCCCFOZFdffTW2bfOTn/yEOXPmMDIywkMPPcTExMQhPT8QCNDa2nrQ7Z7n7VctWlRJpuoNzLIsgsEg8XicaDRKMpmkUqkwMDBQLWKhoNLciZ9qIFxXT8WrBkwdHR2kUimy2Syu6+K6LlNTU2SzWWKxGGeccQYL5s/HAIYrLkUFGCboerUr8B66rlNXV8eKFSvo7u7mjDPOkFLqQgghhBCvQTqd5rHHHuOWW27hLW95CzNnzuSss87ic5/7HFdddVVtv/Hxcd797ncTiUSYP38+v/nNb2rbXj797/bbbyeVSvGb3/yGRYsWEQwGWblyJT/5yU/49a9/XVuL/8gjjxzjq339kUzVG1ilUiGbzdbKqTc0NBAMBtF1nXK5zM6eHhzHwfd9NE3DL5WJRCJs2bKFLVu2EAwGGR8fx/f9WrPefD5POp1muK+XwbEJfF0HNPD9aWurDMMgkUgwf/58zjnnHHRd4nshhBBCiNcqFosRi8W46667OOeccwgGgwfc76abbuLWW2/ln/7pn/jOd77DBz7wAXbt2kX9QSowF4tFbrnlFn70ox/R0NBAW1sbpVKJbDbLv//7vwMc9LlvJPJO9g1sb3+qvaXQR0ZGKJfL5PN5KpUKjuMQCATQdZ1SqYRt2/i+TzabpVwuEwqFKBaL2LZNOp2mUCiwdetWVq9ezcYt23ABtD0vMW1vYEXtU42967eEEEIIIU5GvvKp7ExTeH6Eys4Mvu+/+pNeI9M0uf322/nJT35CKpXi/PPP5/Of/zzr1q2btt+1117LNddcw7x58/jqV79KPp/n2WefPehxHcfhe9/7Hueddx4LFy4kkUgQDodra7ZaW1sJBAJH7bpOFBJUvYGVy2U8z8P3fXzfJ5PJEAwG6ejoIB6PEwgEsG271r8qHA5TKpXQdZ1QKFRbBzVjxgws06SYz5NNp5kYGaZUKTNtrh+ArmPpBgHTRAMClsXu3bvp6+s7HpcvhBBCCHFU2b0Z8s8MUdowQeHZYdTuwlE939VXX83g4CC/+c1vuPTSS3nkkUdYsWIFt99+e22fZcuW1b6ORqMkEglGR0cPesxAIDDtOeLAZPrfG9icOXPo6enB8zwAlFLYtg1AJBIhGo2i6zq+75PL5VBKoZRiyZIltLS0sGPHDgYHBzFNE10DXBs8j0LFr2ao9H0WMvo+mmdjmSaGr1C6RntjA2XPk4p/QgghhDgpuVMVfMfHaotiD+VRGfuonzMUCnHxxRdz8cUX84UvfIEPf/jD3HjjjVx77bVAdU39vjRNQ72sp+i+wuGwFKc4BBJUvYGdffbZrF69mvHxcUzTRClFLBajsbGRwcFBXNfFsixWrFiBruuk02lSqRRdXV3ous6sWbPo6+tjcnKSDfkMuUnQNAN8B93QUVCd8rcn1a2Vy3imSXNHB+lMhtGxUepb26XinxBCCCFOSmZdEM3ScIYKaKaOnjz20+QWLVr0ir2pXotAIFD7UF5USVD1BmYYBitWrODxxx/H8zwCgUBt8aHnebS0tDA6Oko2m2X58uX7PX9vYAWgdBPL0LAdF8NX4Ks966l88BwwLFQgBK6NWcjSEgrQPHsOcxcvkYp/QgghhDgpBWYliVHNWOnJAOWGo3euiYkJ3ve+97Fy5UqWLVtGPB5n1apV3HrrrbzrXe86oueaNWsWv/vd79iyZQsNDQ0kk8n9MmBvNBJUvcGdffbZ6Lpe7StlmuTzefL5PJlMht7eXhKJxKtmktLpNMFIlPkLu9nZtwvlekQtk8liEc/T8TQdTXlYToUFs2ayYPEiUs2tzOhejCZV/4QQQghxktJ0jeCcFEGqyywq2exRO1csFuPss8/mW9/6Fjt27MBxHDo7O7n++uv5/Oc/f0TPdf311/PII49wxhlnkM/nefjhh7nooouO6DlONBJUvcEZhsE555wDwJo1a9i8eTOzZs2ip6eHYDBIKpXC932UUgcte55KpTAti5LrUtfYTHZqinQug+lD3NIxIjEMu0x7YzPL3nQBmqmTGRsBkMBKCCGEEOIICAaDfO1rX+NrX/vaQfc5UPXBfde2X3TRRdP2ufbaa2trsfbV1NTE73//+z9qvCcbCapETSqVwjAMent7KZVKOI5TW0ulaVptqt/L7Z2+l06nKRaLrH7mafAhGA5Tpyp0zWgjOzaK69g8+rN/p5TPYZoWsYYGzn3PNXQtkYoyQgghhBDixCVBlQCqKWnleZRyGSZGx1A+KN8nGo0C1YBJKUVfX99+BSv2rq1SSvHQQw/haxpR08CplHCDFr7vky5X8AyL4WIZz/UxdI2psUlCTz/JjEVLpPmvEEIIIYQ4YUlQJQDo6+vjsUceZnhgAFv5aBooNAYGBmhrayOVStHX18eqVatwXRfTrL509s1e9fX1MTg4iO0pPMMiGo2xaPmpeJ7HwI4+ym4BP9kMro1umGiew0AmS19f30GzYEIIIYQQQrzeSXpAANVMVKVUJKQDmobnQygQIBKJ0N7eTldXF+l0Gtd1aWlpwXXd/fpLTU5O4rouyWQSwzQJhIIMbtvC6tWrqfjg6wboOhgmvqYRsCxC8aT0qRJCCCGEECc0yVQJoLqeKhiOUEinCWgKX9OIx2I0trYyf/58dF2vFqQwTUZGRjBNc7+qgLZtk81mKZfLVCoVctkMw3YF5fv4wTBogKaBrmOYJlY0ih4Ikkgkjss1CyGEEEIIcSRIUCWAarGJC97yVnZu2ohml4nW1RFI1FFXX18rRLFvQYq9a6r2FQwGSSaT2JVKtU8VGl4gCHYFPBd0HU3TMHUwrQCGVW2AJ126hRBCCCHEiUyCKgFUG/nOnjOH2XPmvOI+r7T2qa6ujkQiwdDgAKBVM1NooBuY2Uk008LUDZxoDDQN0zRRSpHJZI705QghhBBCCHHMSFAlXjNfKfo3byAzNkKisRkVjpFIJAgFQzhuDnwN9vQ68DWdYG6KQH0j4UQSIxyhVCoRCARetbmwEEIIIYQQr2cSVInXrH/zBtY+cB+eY1PEoJJqpFCxyReLgF6dAugrMAz8SBw3HKGts5Ngcxu5fJ5AIMDSpUv3m0YohBBCCCHEiUSq/4nXLDM2gufYNHbNYrJcYXh4mMmJiZc6cWuAtuclpmt4mkFxfIxAdpKZLY28/W1v46yzzpIeVUIIIYQQJ4ne3l40TWPNmjXHeyjHlLybFa9ZsqkFwwrQ09NDwfWouC6e576UofIBpdAcB6WbeLrO1PgoQ88/xcTzT2OWCxJQCSGEEEIcIWNjY3z0ox+lq6uLYDBIa2srl1xyCU888cTxHtpJT6b/iddsRvdiANauW0d6dz9GqUTO80H51dLpvgLA1zTAB+XhhaIoglSKeTJjI3Sy9DhegRBCCCHEyePqq6/Gtm1+8pOfMGfOHEZGRnjooYeYmJg43kP7oziOg2VZx3sYr0jSBOI103SdzkVLWXLO+cTrG9B1nYBro1eKaJ6DUciiO2U018Z0Xax8BgVUPEUwEiPZ1HK8L0EIIYQQ4qSQTqd57LHHuOWWW3jLW97CzJkzOeuss/jc5z7HVVddBVTb2PzoRz/i3e9+N5FIhPnz5/Ob3/xm2nFefPFFLrvsMmKxGC0tLfzlX/4l4+Pjte33338/b3rTm0ilUjQ0NHDllVeyY8eOg47L8zxWrlxJd3c3fX19APz6179mxYoVhEIh5syZw0033YTrurXnaJrG97//fa666iqi0Sg333zzkbxVR4UEVeKPtrfH1bLTVrB41kwaDB9DM/BiKYxwmJBTxlAuRGOE4zHc089j/PL309c6E7V3/ZUQQgghhHjNYrEYsViMu+66i0qlctD9brrpJt7//vezbt06Lr/8cj7wgQ8wOTkJVAOzt771rZx22mmsWrWK+++/n5GREd7//vfXnl8oFPjUpz7FqlWreOihh9B1nXe/+90opfY7V6VS4X3vex9r1qzhscceo6uri8cee4wPfvCD/M3f/A0bN27k3/7t37j99tv3C5y+9KUv8e53v5v169ezcuXKI3SXjh6Z/iemUUrR19c3rcHvq6172rfHla8Uv/jZ/2ViRy+a76MMg6Z5pxBTFQLJerwzzuf3fgDb91k3NImm6ZxXFztGVyeEEEIIcezs+74qkUhQV1d31M5lmia33347119/Pf/6r//KihUruPDCC/mzP/szli1bVtvv2muv5ZprrgHgq1/9Kt/+9rd59tlnufTSS/nud7/Laaedxle/+tXa/j/+8Y/p7Oxk69atLFiwgKuvvnraeX/84x/T1NTExo0bWbJkSe3xfD7PFVdcQaVS4eGHHyaZTALVoO6GG27gQx/6EABz5szhK1/5Cp/97Ge58cYba8//8z//c/7qr/7qyN+oo0QyVWKavr4+nnvuOVavXs0DDzzAs88+e8BPHg5G03VKjkLTdeKJBL7vU/DhrKvey1V/eS1OXQOZbIbG8WHSmTR9pfJRvBohhBBCiOOnr6+PVatWsXnzZp5//nkGBgaO6vmuvvpqBgcH+c1vfsOll17KI488wooVK7j99ttr++wbYEWjURKJBKOjowCsXbuWhx9+uJb1isVidHd3A9Sm+G3bto1rrrmGOXPmkEgkmDVrVu1a93XNNddQKBT4/e9/Xwuo9p7jy1/+8rRzXH/99QwNDVEsFmv7nXHGGUf03hxtkqkS06TTaXK5HKVSiVKpxPr162ltba39g3k1SilCoRC+UmSzWTTAc11WrVoFQCidpTg8xHogQIZQRIf2xqN2PUIIIYQQx0s6ncZ1XVpaWhgeHiaTyRz1c4ZCIS6++GIuvvhivvCFL/DhD3+YG2+8kWuvvRZgv4IPmqbVPkDP5/O8853v5JZbbtnvuG1tbQC8853vZObMmfzwhz+kvb0dpRRLlizBtu1p+19++eX8x3/8B0899RRvfetba4/n83luuukm3vOe9xxw7HtFo9HXdgOOEwmqxDSpVArP8yiVSoTDYQzDIJ1OH/Lz+/r6qORzmPg4gAHMbGul4rqk02nmpMd582gPlfYugoN9zKl/fVdyEUIIIYR4rVKpFKZpMjIygmma0zI2x8qiRYu46667DmnfFStW8Ktf/YpZs2ZhmvuHCRMTE2zZsoUf/vCHvPnNbwbg8ccfP+CxPvrRj7JkyRKuuuoqfvvb33LhhRfWzrFlyxbmzZv32i7odUqCKlGjlML3ferr6ymXy4RCIeLxOKlUatp+vlL0b95AZmyEZFMLM7oXo+1Zd5VOp7ErZepNjZIZoFIs0tezk1RzM5WpCSpD/cwc7SeQm8AMBKlrlgqAQgghhDg5dXV1ARyTNVUTExO8733vY+XKlSxbtox4PM6qVau49dZbede73nVIx/j//r//jx/+8Idcc801fPazn6W+vp7t27dzxx138KMf/Yi6ujoaGhr4wQ9+QFtbG319fdxwww0HPd7HP/5xPM/jyiuv5L777uNNb3oTX/ziF7nyyivp6urive99L7qus3btWl588UX+8R//8UjdjmNOgipRs2vXLv7whz9QKpWwLIvOzk4WLFhQ+4WwV//mDax94D48x8awAkC1Z9XuTS+yc/1asvkCjuuj2QWsfIagH0P1F+kd6CEYiYAG9R2dzF5+eq3XlRBCCCHEyUbX9doSCrVnacTREovFOPvss/nWt77Fjh07cByHzs5Orr/+ej7/+c8f0jHa29t54okn+Pu//3ve8Y53UKlUmDlzJpdeeim6rqNpGnfccQef+MQnWLJkCQsXLuTb3/42F1100UGP+clPfhKlFJdffjn3338/l1xyCffccw9f/vKXueWWW7Asi+7ubj784Q8foTtxfEhQJYDqP/S9Cyj3/qPRNO2Aa6kyYyN4jk3TzNmM7eohMzYCwBP3/Zb+YoWyAnSNoBUkFY3SvWQxO55/lny5TDDciee61Ld10LlIGv8KIYQQQhwJwWCQr33ta3zta1876D7+AVrZvHyZx/z587nzzjsPeoy3v/3tbNy48aDHnTVr1n7n+dSnPsWnPvWp2veXXHIJl1xyyWGN8/VOgioBVNdC9fX1Yds2mqah6/pBP01JNrVgWAHGdvVgWAGSTS1kxkYo2TaYFrrtoBkGZjCIbmmM9/UCUCkVGNq+FcMwqZSKBzy2EEIIIYQQJxoJqgRQ/ZTCNE1M08T3fQzDIB6PH3DfvVP29l1TBRAOBFDFCi6guR6u5dO6cBGddQkm+nczuGUT4Xicci5HIBI5VpcmhBBCCCHEUSVBlQCq1Wni8Tj5fB7XdTFNk1gshlJqv+a/mq7TuWgpnbw0fW9G92LO8xTFhx5krFAkkUgSjERp6ZrF4uXLWX3/3SjPpZTPEa9vINXceqwvUQghhBBCiKNCmv8KoFqdpru7u1ZG3fd9tm7dul8jt4PR9qzD0rOTmKUixYlxTN+nLpVi9f13s+nxR3ArFTzHpW1BtxSoEEIIIYQQJw3JVAmgWp0mHA4TDodrfQnK5fJh9ajqXbeaymA/kViCiqtIzWjHKOXZ/MSj5CbGMa0AuuNQSE8dpasQQgghhBDi2JNMlahJpVKEQiFKpRKFQgHgsJrU+fgo10HLpgnmp0hFgmTHR9FNE9OyKKQnKWYzDG/fSv/mDUfrMoQQQgghhDimJKgSNV1dXSxYsIBwOIxlWTiOw/DwMEqp2j6+UuzeuJ4XH32Q3RvX4++zLZqsA03DsSugaUSTdSSbWojXNaAbJmYgQNv8hQTC4VoZdiGEEEIIIU50Mv1P1Oi6TiQSIRqNous6pVKJF198kba2tlq/qgM1/u3oXkxfXx/D2TzB9k7aohHKhTyhSJSOBacw2rsTu1zCCoUwTBMzECTZ1HIcr1QIIYQQQogjR4IqMU0qlcLzPEqlEuFwGMdxWLduHVDNZB2o8a8XibNq1SpyuSL5QBQtXyDouVRKRfq3bGBo2xZ00yQUi9Ewo4vZy0+XQhVCCCGEEOKkIdP/xDRdXV0sXryYYDBIuVwml8sxNjbGqlWr6OvrO2Dj33Q6jeu6zJo3j2A0Rln56KbJ0NZNrHvo90wNDRAIR/Acl1Iuc7wvUQghhBBCiCNKMlViP5qmoWkanufh+z6WZTE4OMgTTzzBueecw7K3X0p2fLTW+Nfp6SE7OsxIz3Y8u0LEc0i1dzI1PEg5l8MuFZkY2I1yXUZ37eTJqTwtBY/l8+ZxTiqKrmnH+5KFEEIIIU541157LT/5yU9q39fX13PmmWdy6623smzZsuM4spOfZKrENH19fbz44ouUy2Usy8L3ffr7+8lmswwMDPDY44+jogmWXPh2OhctRdN1Jjevp7JjE3Z/L17fTsoDu+hbv4ZKPkcoFiMQDuPZNr5S9NS18oeGTu4eGOGng+M8nS4c70sWQgghhDhpXHrppQwNDTE0NMRDDz2EaZpceeWVx3tYJ72TKqj60pe+VMuy7P3T3d19vId1Qkmn0xiGQTgcxnVdLMvCNE0ikQjhcJhSqbRf76qJvl346UnM9Dh6PoPvegA0z5yDYVrkxsdr+2biKVzdoC03SUUpdpcrx/LyhBBCCCFOasFgkNbWVlpbW1m+fDk33HADu3fvZmxsDIC///u/Z8GCBUQiEebMmcMXvvAFHMeZdox//Md/pLm5mXg8zoc//GFuuOEGli9ffhyu5sRx0k3/W7x4MQ8++GDt+72NbMWhSaVSxONxAAKBAB0dHYyOjjIxMYFt2yQSCVKpFEopdvX2snPTRgZHR7ArFTS/Wl5deS6ubdM6bwGFTJr08CCFjIvveSRzaSx8Jlu7aNV1OkPB43m5QgghhBAnrXw+z3/8x38wb948GhoaAIjH49x+++20t7ezfv16rr/+euLxOJ/97GcB+M///E9uvvlmvve973H++edzxx138I1vfIPZs2cfz0t53TvpIg7TNGltbT3ewzhhdXV1AdWMVSqVorOzk127drFjxw40TWPu3Ll0dXXR29vLA/fdR3piHGUr9EgMs5AFwAgEidXVE4hGaZ49l+zoMOP9fRSmpliqe5za1kB40QK6ImHOSUWP5+UKIYQQQhw1vq9Ip5+jVO4nGGxH1xYe9XPec889xGIxAAqFAm1tbdxzzz3oenWC2v/+3/+7tu+sWbP49Kc/zR133FELqr7zne9w3XXX8Vd/9VcAfPGLX+T3v/89+Xz+qI/9RHbSBVXbtm2jvb2dUCjEueeey9e+9rVaoHAglUqFSuWlKWjZbDUwcBxnv1To8bZ3PEd7XG1tbXiex+TkJJ7nMWPGDGZ0dDC4dTPZXdvpzWf47dAkqwNR4kmf5vQ4JOoxXBvlKaxIhGAixa71a5mo76dlXjedS5djlysEImGSjS20NybQdB3XtqvHnRgl0dBM+4JuNP2kmpV6wjpWrzch9pLXnDiW5PUmDsZxHHzfRymFUuqPOtZU+hkGB+5AqQqaHqAu9S4SiYv+6OMejO/7XHTRRXzve9+rnn9qiu9///tcdtllPP3008ycOZNf/OIXfPe732XHjh3k83lc1yWRSNTGtGXLFj7ykY9MG+OZZ57Jww8/fNTGfbwppfB9H8dxMAxj2rZD/R2h+b7vH43BHQ/33Xcf+XyehQsXMjQ0xE033cTAwAAvvvhibUrby33pS1/ipptu2u/xn/3sZ0QikaM9ZCGEEEII8Tqyd9ZTZ2cngUDgjzrW+MTdpNOPEgnPp1jaRip1IY0N7zxCI93fX//1X5PJZPjP//zP2mOe5zFz5kw+8pGP8I53vIPLL7+cG264gbe97W0kEgnuvPNOvvvd77Jr1y6gmr36+te/zp/92Z/VjvEP//AP/OEPf+Cxxx47amM/nmzbZvfu3QwPD+O67rRtxWKRP//zPyeTyZBIJA56jJMqU3XZZZfVvl62bBlnn302M2fO5Je//CXXXXfdAZ/zuc99jk996lO177PZLJ2dnbzjHe94xRt3PDiOwwMPPMDFF1+MZVlH7Tzr169n69atNDc3Mzo6yoIFCzCzE+x8/lmMhhYecHR2ts+mwS4xgEHn8G4Wbl2DUcgS8j0CwRC6rmPu+UWklE8kmcIu5kk0NVPJFzjlwrew6E1vYdNjD7PhsYcJx2KU8nkWv/ktnPLmtxy1axOH7li93oTYS15z4liS15s4mHK5zO7du4nFYoRCoT/qWJ6aR6m4CsfpIRCIEAy0E4/H0Y5SO5m9Bcb2fQ+rlELXdZRSrFu3jpkzZ/LlL3+5tv173/semqbVntPd3c2LL7447Rjr1q3DMIzX3XvjI6VcLhMOh7ngggv2+5nvncX2ak6qoOrlUqkUCxYsYPv27QfdJxgMEgzuXyzBsqzX7S/Zoz22+vp6TNNkdHQU0zSpS6WYHN7N2FSabLaE0dCG4ynS9S0EC3ki+QyObuBGk6j0OLh5AqEQhVwWHx/DtFBOhWI6TW58lEAwjFsqYVkWTrlEbmSY9KCLYZg45dLr9r6/Ub2e/y2Ik5O85sSxJK838XKe56FpGrqu19YhvVb1dWeja/q0NVV7j300aJqGbduMjo4C1el/3/3ud8nn81x11VVks1n6+vr45S9/yZlnnslvf/tb7rrrLoDamD7+8Y9z/fXXc+aZZ3Leeefxi1/8gnXr1jFnzpyjNu7jTdd1NE074O+DQ/39cFIHVfl8nh07dvCXf/mXx3soJ5Suzk5Ge3cyOjxEc2MzejHL4NbNeLqJ8n1ODxk02HmCoQb08UkqE8NodgU3EEKZFk4+g1IK0zTxPRcrEKSho4tKsUCisRkrFCIQCtO3YS29a1ejlEc4Fsd1HfJTk/hKyboqIYQQQpzwNE2nru5s6jgbpdQhZz3+GPfffz9tbW1AtdJfd3c3//Vf/8VFF10EwN/+7d/ysY99jEqlwhVXXMEXvvAFvvSlL9We/4EPfICdO3fy6U9/mnK5zPvf/36uvfZann322aM+9hPZSRVUffrTn+ad73wnM2fOZHBwkBtvvBHDMLjmmmuO99BOKANbNjK6dhWeYzM6MkC5qRnlOsyYM4ft/YMUbZtu36arNMnQ1AibNQ0nEEJTHrprA+BWymgEAY1yscDwzm045Qr58XHqO7uolIo8/d93MNKzA6dSpphJYwYC7Fr3ArtPPZ2uJdL1WwghhBDicNx+++3cfvvtr7jPrbfeyq233jrtsU9+8pPTvv/CF77AF77whdr3F198MfPmzTtSwzwpnVRBVX9/P9dccw0TExM0NTXxpje9iaeffpqmpqbjPbQTSmZsBM+xaZo5m7FdPQAYVgB3bJiWUIDm2XOIRyOMvPAsuclxwoUcuu9DqYheKYEGKB+nUiEQCqHpGnaxgK88KqUC+akJiukpspMTKOXh+z74Pr6nKGbS9K5bLUGVEEIIIcRxUCwW+dd//VcuueQSDMPg5z//OQ8++CAPPPDA8R7a69pJFVTdcccdx3sIJ4VkUwuGFWBsVw+GFWDWshVoukZmbIREYxO+gmcefoCRkVG8fBZvcgxLNzAtCz8QRHkuvqZjhcPgK9A0NF3HNC1c26aUzTLetwvPruDaNuwpQOk6Nl42w9TIsEwBFEIIIYQ4DjRN49577+Xmm2+mXC6zcOFCfvWrX/H2t7/9eA/tde2kCqrEkTGjezFQzVglm1qY0b0YTdfpZCm7N67nid/dw2CuQNnz8a0wgWAEw6lghUI0zZxNIBIlPzmBrzyU5xGra2D3hnVUSkXwfdyKz8TAbuxyuRZQ7eV7HkNbNtK/eQOdi5Yej8sXQgghhHjDCofDPPjgg8d7GCccCarEfjRdp3PRUjrZP6hJjw4zVa7gB8OYpRIV3UAFQqhKESsU5syrrmZG92L6N2+oBWXt8xZy3/f/D30vrkU3DOxSEddxcErFA57fqVTIjI0c8PxCCCGEEEK83khQJQ5KKUVfXx/pdJpUKkVXVxclDErolG0HPxRBL5cwPQdN12lon0H7vIXTAqq9Wa5TL672EMtPjDM+0Ec5n+VgfaeV5zGyYzu+75Nqbq0dQwghhBBCiNcjCarEQfX19bFq1Spc18UwDIaHhxmaTOOhYeLh6Rqm72J5Nm40wUA6y4N3/pLKUD/FqUk8z6X7/AtZcek7a1MK06PD7Fz1LNtXPX3Q8/o+bHzsYbY89RixhgYu/MBKZi5dfoyuWgghhBBCiMMjH/+Lg0qn07iuS0tLC7lcjvXr17Nz21aKtk3Z83FdD9fzKYUTVOL15DWdrb27GJ9KUy7kyY6PsfmJR+nfvKE2pXDJBW+jrq39gJknTdMwTJNgOIRTKlIpFpjq72f9w1JtRgghhBBCvH5JUCUOKpVKYRoGvdu2kZuaxC2X0V0HzfcxDR0fDTcYwk01oEyToKZhhSKUlU8+PYmmaTh2hczIcO2Y/Zs3MLxzGxratHNphkE4mSKcTOHaNr7vo2kanvKwC4VjfelCCCGEEEIcMgmqxEF1dXXR1dSAlh4jUiliZ9P4mo7m+yjHRccnauhYloURCBJsaKahYwYt7R1oaHiOjV0oVKv+Ab5S9Kx5nnKhQLK5BU2rBla6aZJobKK+fQbJxmYMy0LTdTzXQ9d0Es0t+Eqxe+N6Xnz0QXZvXI+v1PG8NUIIIYQQQtTImipxULquE9EUdbpP47w59PT0kJgxg6mxcdLpSVQgQqK+jkBwF83NBnVNjTQ1nU62Zxv5nVvQDAPf86r9qtiTpdq+lVImjecpInV1GGaAZHMLDTNnsXvtC+imiWbohBNJoqk6fM+jec5c+jdvYO0D9+E5NoYVAJCS60IIIYQQR8GXvvQl7rrrLtasWXPA7bfffjuf/OQnSafTr/kc1157Lel0mrvuuus1H+P1RDJVYhqlFL29vaxZs4be3l4Sjc0YpkXfhnVo2SmilRINODT5DrFimubUCPNPGad1xiSRyCpSqTHccplKsUghPUWlWMQplYBq36tAOEzX0uUEwyGC4QhNM2cRSSTJj46SGR1hon83lVweKxQi2dRMQ+dM6lrayIyN4Dk2TTNn4zk2mbGR43ynhBBCCCFen5566ikMw+CKK6443kM57i666CI++clPHvXzSKZKTLNvxT/TNDl9xQraFnSTeeJRDMNkaPsWXMcG5ePmc9hjJegMEI8vI5/fTKncTzCcIN7YBPgUMxkKU5P4SpFsasEMBLGLBUKxOLphEoxEGe/rRbkeRsAiHk9SzGYIx+KE4nEaO2fRseAUAAwrwNiuHgwrQLKp5fjeKCGEEEKI16nbbruNj3/849x2220MDg7S3t5+vId00pNMlZhm34p/ruuSyWYJRqPE6urpWnoqpmnhlsuU8jnCsTjYcVxbkc9vRtcDhEMzSLa0Ylgm2dFRXLvC8I5t9G/ewIzuxZx68WXMPfMcus+/EMM06Vu3hmImjeNU0HWDSqmIbhh4nks5l2No2xYGtm6a9txTL76sVqJdCCGEEEK8JJ/P84tf/IKPfvSjXHHFFdx+++3Ttj/yyCNomsZDDz3EGWecQSQS4bzzzmPLli0HPeaOHTuYM2cOH/vYxw7aZ/TXv/41K1asIBQKMWfOHG666SZc133V8d500000NTWRSCT4yEc+gm3btW2VSoVPfOITNDc3EwqFeNOb3sRzzz037fmPPvooZ511FsFgkLa2Nm644Ybaea+99loeffRR/uVf/gVN09A0jd7e3lcd02shQZWYJpVKYZomIyMjmKZJMpGgUiiQm5pk0/p1pHUTo6kNKxrFiybIOnMoTC6hPN6G5Z5DMnE6M7oX0zp3AZFUkq6lywmEw2TGRl4qq37h21lx6TtpnffSPvUdnXQtXc6cFWfQtXQ58YamaVP99n1u56Kl0gxYCCGEEOIAfvnLX9Ld3c3ChQv5i7/4C3784x8fMBD6h3/4B77xjW+watUqTNNk5cqVBzzeunXreNOb3sSf//mf893vfrdWaGxfjz32GB/84Af5m7/5GzZu3Mi//du/cfvtt3PzzTe/4lgfeughNm3axCOPPMLPf/5z7rzzTm666aba9s9+9rP86le/4ic/+QmrV69m3rx5XHLJJUxOTgIwMDDA5ZdfzplnnsnatWv5/ve/z2233cY//uM/AvAv//IvnHvuuVx//fUMDQ0xNDREZ2fnId/LwyHT/8Q0XV1dQDVjlUql0AtZhrZuIu8qphwXdIUGaI5ClWx0D0ZXecTsEo2JEmFrEV2LT2X28tPJjo1iFwuYgeB+0/U0Xa/uMzpCemQI5brMXLyMhq7Z3L9lK9vGJ2mZyNAtU/2EEEIIcYJSvs/T6QK7yxVmBC1O0Q6c5TmSbrvtNv7iL/4CgEsvvZRMJsOjjz7KRRddNG2/m2++mQsvvBCAG264gSuuuIJyuUwoFKrt8+STT3LllVfyD//wD/zd3/3dQc950003ccMNN/ChD30IgDlz5vCVr3yFz372s9x4440HfV4gEODHP/4xkUiExYsX8+Uvf5nPfOYzfOUrX6FUKvH973+f22+/ncsuuwyAH/7whzzwwAPcdtttfOYzn+F73/senZ2dtWCvu7ubwcFB/v7v/54vfvGLJJNJAoEAkUiE1tbW13Q/D5UEVWIaXdeZNWsWSin6+vpYt349I1NpbDQ8z0Mv5HCDYdBM8DyM9CReKEyhXEbLTNKzdjWappMeHaZt/kKC4QjJltb9puv5SuErH03XKOeyhKJxtq96hvu39/BUxwLshjA9kSgLOhpkqp8QQgghTkhPpwv8dHCcilIENI2rk2Henjx659uyZQvPPvss/+///T8ATNPkT//0T7ntttv2C6qWLVtW+7qtrQ2A0dHR2gfsfX19XHzxxdx8882vWuhh7dq1PPHEE9MyU57nUS6XKRaLRCKRAz7v1FNPnbbt3HPPJZ/Ps3v3bjKZDI7jcP7559e2W5bFWWedxaZNmwDYtGkT55577rTs2fnnn08+n6e/v792LceCBFXigHb19vKHh/+H3OQEOReUq/A1HTcYRvcVul1CGQZeMIymFFqlhOvY5MfGppU+P/Xiy6aVPveVon/zBnrWPM/wjq2UCwXsUonWeQsZ7+thNGVgxGJ0TYww1dhEpaVDpvoJIYQQ4oS0u1yhohSLYmE25EoM2M5RPd9tt92G67rTClP4vk8wGOS73/0uyeRLEZ1lWbWv9wYlap8+oE1NTbS3t/Pzn/+clStXkkgkDnrefD7PTTfdxHve8579tu2b+TqZybtVcUA7N21kcngQr1zCNSxcs9obyqiUsNJjWFNjWOkxzNwUVnoMvZhH1w18eMXS53v7Te1at5qpgQEiiST4MN7XQzASoxkPlc8zEE0SDYboDAWPw9ULIYQQQvzxOkNBgrrOxnyJoK7REbBe/Umvkeu6/PSnP+Ub3/gGa9asqf1Zu3ZtLTg6HOFwmHvuuYdQKMQll1xCLpc76L4rVqxgy5YtzJs3b78/+it8OL527VpKe1rvADz99NPEYjE6OzuZO3cugUCAJ554orbdcRyee+45Fi1aBMApp5zCU089NW3N2BNPPEE8HmfGjBlAdYqh53mHde2vhWSq3mD2Tuvbu2aqq6vrgC923amAUpR1E6UpdM/FVwrdLmMW89V9inl008QMBmmYO7/23PzUJKVslmh9w37rofb2m2rsmk1fZg2lTIa6jg5a5y5g1qmncZaCWRNTZGN1LJszm3NS0aN7Q4QQQgghjpK972NeWlN19N7c33PPPUxNTXHddddNy0gBXH311dx222185CMfOaxjRqNRfvvb33LZZZdx2WWXcf/99xOLxfbb74tf/CJXXnklXV1dvPe970XXddauXcuLL75YKxpxILZtc9111/G///f/pre3lxtvvJGPfexj6LpONBrlox/9KJ/5zGeor6+nq6uLW2+9lWKxyHXXXQfAX//1X/N//s//4eMf/zgf+9jH2LJlCzfeeCOf+tSnau9vZ82axTPPPENvby+xWIz6+vpXDPReKwmq3mD6+vpY9dxz5Kam8FybZYsWceYFF02bYqeUIpJMoWs6TqUCvo8XDGNUSuiuPe14hmkRCIbRNR3dNClm0uimiee6tC/o3m89VLKpBd20SA8NEgiHqe/oYOlbL6Vz0ZLaGGYd9bsghBBCCHH06ZrGeXUxIIZSimw2e9TOddttt/H2t799v4AKqkHVrbfeyrp16w77uLFYjPvuu49LLrmEK664gnvvvXe/fS655BLuuecevvzlL3PLLbdgWRbd3d18+MMffsVjv+1tb2P+/PlccMEFVCoVrrnmGr70pS/Vtn/9619HKcVf/uVfksvlOOOMM/jd735HXV0dAB0dHdx777185jOf4dRTT6W+vr4WpO316U9/mg996EMsWrSIUqlET08Ps2bNOuz78Go0/2DF5t+gstksyWSSTCbzinNHjwfHcbj33nu5/PLLp82DPRxr1qzhhWefxZ4cpei41AUt3nblVdPWPfX29rLquefYtWMH+WIBzXHw8TFyU1iFLMoMoLk2GqBHYkRCIVob62mYpzE1uhlTbyQ/HGHpRe9gyVsurh1X+T5PTeV4/Llnqax7nrn5CZJ1DZz6jsunnV+8PhyJ15sQh0Nec+JYktebOJhyuUxPTw+zZ88+ouuB9gZViUTiqGRKxGv3Sj/zQ40NJFP1BpNMJChl02SzOSzTxHPKZEaGpwU16XQa1/OwNPAB3zTRPBc0HSfVhK/psPeXga/wTIu68BBlrQctOoWjduL4HVRKb5p27qfTBf7v4AQDnonTMZ+43Ux0fIDM2AidvHpQtbfIRWZshGRTCzO6F0sRCyGEEEIIcdxJUPUGY5TyGBPD6Ok0vmNT1nwqpWJtu68UdnqS/OQ4xXIZlEKrlNEApelouo5eLuEk6kDTMIt5rHgS3+qnUspSTkcI1ZcJpyAQieArxe6NL9K7bjWPhOsYiTXSMNpPrxFky/AIHU7xkPpQ+Uqx+v672fzEoxiGSbS+AYDORUsl2BJCCCGEEMeVBFUnuZcHHJmxEcLKBU1hKxfNNMhPTeLvKaG5+r7fsP7B+6noBjoGhmZUC1TsKaNOMIgbje/JVOm4kTiVShlVCqElLaJNLq6t4XsJUs2t9G/ewNP/7w6mBgYoNXdQmH8amUCUsK7RaJdomTv/kPpQ9W/ewOYnHiU7PkY4FgeoZbj2VhTcW8YdkOmEQgghhBDimJGg6iT38oCjbf5CQrEYuYlxlG1j6CFGdmyjf/MGANY9dD+5sRE0TUd3HALhCMoMoLs2ejFPXVMTI/kivl+tEKiCYSzPoa3uLMqFBLY7THkKkonF+MonMz5CpVDACoeYk53A2/gsE8EIqUKWlvFBcskouzetB18jOzE6LdO0b0A4OdCPbpiEY3FK+RxWKFTLcO2tKNg0czZju3oOeTqhEEIIIYQQR4IEVSe5lwccwXCEc979Z6y6504mB/tJNLeSHRvhud/8isYZXdjFEr5SeMpF8/1a+XQ0jWA0RshzsDJT+Ml6fN3AsMvELJ1SNkskcQqZPotCehLNHmfdg/fRtqCbQCRCbmwM17FpdYdp9X00TcPxYXjnNoq/SoMGwXAEwwrg+wpN06sNgrdvJRAOY5dKGKZJMBbDCoXoPv/CWoYr2dSCYQUY29WDYQUOaTrh4ZDphUIIIcQbj9Rye+M4Ej9rCapOcvsFHC2tdC5aiqZrPPXfP2e8dyd2uUwpk2F45zbccqXaTdv3QdOqfwOmFaChayZubJRUfJJKsUhuOIbhOmiRMOP9Dr7yyI6N4rku9p5GbrPCEeadfjbF9BTlfA5VUtX1WcpHQxFNpqgU84DGjO7FjO3qoXftC2THRpkaGqCYSdO15FSKuSyhSITWrgXMWrZiWgn2vcHVvkHPkSTTC4UQQog3jr3VIIvFIuFw+DiPRhwLxWK1vsAfUwlUgqqTXC3gGBmmUiqSHh0GoGPBKbTOW8DUyBAEIxSVgkqZgFLEGhopTE7iKw/dtLBCIXRdx42NEGrvJeg7KJVDtyuUe3UKxTwl00TTdDzHAaBSyDPe18umxx/GMAOEYgla5y5gx+rn8BwH3QDdCIAPwUgMNGqBH7CnQfAs+tatYWDrZpTjoDc2kR0bRdO1aZkiTdfpXLT0qE35k+mFQgghxBuHYRikUilGR0cBiEQiaJr2Rx9XKYVt25TLZSmp/jrh+z7FYpHR0VFSqRSGYbzmY0lQdZJRnsu29f9FZmo7ybp5zF/6vlpW5eXZlllLT2P982vI+z4+oIfj6JlxfKWwQiGcShnlOlTy1UDJtB2Cvk1pKkK4roiV9ChT/aWgXHe/sdilEn0vrkc3dDTdoFzIE6tvwPc8AuEwLXPm0TxnHqnmFlA+vevXABBNpsiOjlApFqjr6MC0LOxiic4lyxjv6z3mQc3Rnl4ohBBCiNeX1tZWgFpgdST4vk+pVCIcDh+RIE0cOalUqvYzf60kqDrJbFv/X/Tu/Hd8HMZGH2Jo2xbikeX0rltDeniQ9gXdVEpFMmMj2KUwZdfANz2MchEViuCHwoTCQULRGPmJcSrFQu3YTsZA+Qah+jJKGTiZQ4nmfZTngVIU01MEYzE828YuFrFCIRae+2ZmdC9m9f130/fiWjzbRrcCtM9fSMvsuSRbWvF9xboHf8d4X+9RC2qU7/N0usDucoXOUJBzUlH0Pb/wZnRXi270rluNj89Izw4yI8MkW1plfZUQQghxEtI0jba2Npqbm3H2zML5YzmOwx/+8AcuuOACaTj9OmJZ1h+VodpLgqqTTGZqOz4Obi6BrXYz0f8YmZ2b8JXCVx47X1hF08xZJJta2PzkVgzXw7B0vGAYTXmYrkeyuZXh7VumBVQAudE4bl8DVqSCUwxSGrWxKBxkJNPphoFu6CjXwQqFAZ9KoUBmbASAzU88SnpkCN9T6IaOrmssOOf8Wh8qTdMPe83UywOlsxNhBrdsPGDBiafTBX46OE5FKYJ7HjuvLgZUpxdqukZ2bJT8xDjZ8THijU0YpkkkVUeioZFoqo5ANEqqWQItIYQQ4mRhGMYRecO991iu6xIKhSSoOglJUHWSSSTmMLT7Xlx3Ct/TKE762KUimm5g7vkHvLc31PjuHObTFcxyAc3QsZRHqmmSij6OlbQhYwIvpad9M0h2vA6jUsILhjHNKTjEoMpXfrU4hVIU0lNouk6iMcjkQD+TA/1ohoFuBdiarGPXzIUEw2HUyAQfOMVHf41rpmqBkqfw8jnWDW6lftMLBMIRzH2qDGbGRlgbbaISiLMoHmZjvkRfqUznUE8tAEsPD5EZHyU3NkohncaplHFdB1030A0D3/MIJxLEG5o45+o/pWvxqYc1ViGEEEIIceKSoOoksbfs97bHdjO6O4YRMbBzFoWhalbI91wczwVNwy1X2PDY/9DQ3sQpb7qAzY/fRyU/RbytSHLuJLrhE6gzcN04hcFI7Ry6a6P5qprV8hW6a790foBUAw4aul3GKObRpg8Qt1JBuS5WOIRTrpAdH+XB3t0U6pupr28jZ4T4n1POJZ1IYSiYcGFuulDLGB2u3eUKFaWYUc6xamSI9b19nLJzB63zFwLQu2Y1wzu3UckXGK5rIbP0HJ6vqyeVSBIaHWTto/fX1qCF4wkmd+/GLlWDyHI+B0AgHMEuFVGui+PYFHM5nr7zF2x6/BEaO2dx2juuQDfln5kQQgghxMlM3u2dwPbtn1TO5Xjx0YeY2L0L/CAQnL6zpmFYFpqmsWPVMwxs3UQoGuXsP3k/llVm67NPEW3uB11RmggRrCsSiNkUeCmoMvb0rNrbDHjv9wBeJAb1zShP4TnVYMvcZ7vv+3iOjVIGwWgMuzTF7rZZrG2ehbKChBPN0DSTTKIOZZh4hsaQCbtK5VcMql6ph1RnKEhQ19lYKEO5THRqlEq5xK71awhHo/Rv2oDn2ISiMVIDu1lk23gz53L2qacyJz1Kzz4V/xy7AgdYU2qXirWvleNgOw6DWzYx0b+bXWvXAHD65e86pJ+nEEIIIYQ4MUlQdQLbt3/S0I7tFCbHD76zv6dghO9TqFRwHZvsyDAvPvx7lrz1YnrXv0B+zKWuDoL1JXxXx85Nn++rMT1Q2pcyA7i2g+lUcK0gygwcYAg+eB65iTHwfTLxFK6m0zTaz0RTO65m4ANK01GaRt5T7B4YwG+tP+gapWk9pEyL0d6dBKNRkk0tnL1wEdDI2lKGwSfXkty5udp3y/cp5XK1Y+Rsh/72WdiRKO0TI8yeGqGupZU+K8Bo707sUgkzEMA4xIyT5zhYwSBOucL47t5Deo4QQgghhDhxSVB1AlKuy7P3/Zpn7vpv7MKBg5yXMwMBrGAI3TQpZtK1DMto707Ge3txMhkKmRDKq8eK2Tj5AIWh0Csc0SfaViQQd3DyQTJpB18DLxBCVwrD86Y1D4ZqwQfDCuBWygAks1MYnstYYxsB36dzpIdcfRNTukFIeaQ8m8mNffRH9YM22923h9SuF9eSeeJRYnX1tbLx53YvZkZI47FihhEO3DG7v7WLNd2n45kWm5RH7sGHWOwWqWttxwwEKRcLZMfH0E0TTTfwlfeq9zszMkwwGqWxc9ar7iuEEEIIIU5sElSdgNb9z+958r9+hmfbr74zYIUjtM6ey9TwEHalxN55bNFUHcFYnKHVq/CzOUzPpzgYxtejoGlouo+v1AGPGZtRoaE7g274KK8ImzRyWQs/EER3bAy3DKZZnXZoWui6TrlYxLUraIYBvs/MiUGM7WvJJuqpK2SZMbyLYCjM2hnzsXSNJg1Cw7vpWeMdtKLe3h5So7t6KOeyeJ5LMBojMzZKz5rnUcrlmV/9ktzEBL5/4GvJxFN4hknT5Ahjdc2M6ibNvTuZHNiNFQyBphEIhfA9D8OycCuvHlShadS1zeC0d1xxSD8jIYQQQghx4pKg6gQ0MbAL7xB6JuimhRUKUtfSTsu8BSjPIxiPMd63C3yfWEMj8boG9LFxbOWhDAPNczFNE8f18FU1q2OFwjjl0rRjW5Eymq4ojQcJNZSxYhX04Sy6rqEbJoFojIb2DprnzGdqcIBd61aDr8D3MYxqxieciHNOLEQoZuFH6xktTnHmltW0ZsbJJeqJTozQMj7AcCVP/+YNB8xW7S2v3rPmebLjoxRHMwxMvogZDDG8YyvZ8TFG+3qr4z9AlgogmUtjKI+x+hYM1yGZywDVaXzKdfF9qBQLhGPx2nqxV733uk73+RdIkQohhBBCiDcAecd3AmromIlhWa+YqTICQVItrVjBIN3nX0jTzFnkxsfwHJvWuQtoX9BdW3s0/OQTDPbuQFceZcD1vGqGSgN89guoAOyche9phOrL+J6OnTXxPRdP6WiahhUIcsoFb2XJhW/nji/9PUr5hOMJStkMrmNjBoJsjdaTMaI050vMy09RymQIRiLMGOghUcmjPIfGpcupFPJkxkYOWFJd21NuPTM2wuRgP1YgyPjuXdS1teM5DkPbNuOUitX1ZFCdkggYVgDPrgAanWMD6FteIB1PkpgaZ8bQrmnHBw3fV7iHmBkEaJu3ULJUQgghhBBvEBJUnYCWvfUd+J7DC/fdTT49BftO0dM0mmbOZtGb30IoGiPZ0lrL5ry8ge7e6XQjO7djWyaVsovSNEzD2FPU4uBjKAxVqwIG4s6e0u17qgT6CtdxcB2bZFMLAE1dsxnZsZ1yIV+bDtjT2M7aectwDZPtyqO4aRWddgXfV5hWgGRLK/g+drGAGQjWjnUwyaYWTCuAhkYwHKVSLJIbG61W7dt3XRcaZjBAKJ6gMDmBUgrdVyyy88QIMTIxiKcBmoFuVvtP+cpD13V83z/gmqyX61q8jPfc8CXJUgkhhBBCvEHIu74TkG6anHXVeznrqvfiK0XfhnWsf/gB7GKBmUuXc9olVx7wDf3eBrovL0Oen5rE00APWPiOg+u6NIU6iZoJCm6WsfLuA4xCozAUpTD0skeNapPhju5FtWDuor9YCUDvutXYpRK+UmSiiWrlv/EhxpvaySbqCEyN4O1porv0rW9Hw6B33WoAfF/hK3XQKoAzuhfjK5+eNavQLYvx3X04lQovjwx9DeKNzfi+j6bp6GY1s9Yydx6BQIipwX7q2zqYHBogEk9SzE7huR66YVSbKBvVjBy+j25amIEAdnFPA2RNI1bfwDnvvQYjsH/1QyGEEEIIcXKSoOoEp+k6M5cuZ+bS5Yf8nGllyK0AxUymNrXN932agp3MjZ+Krhkovzpt7sCB1cvoOvGGJpJNzSx72yVouo6vFIPbt9A8Zy74PlufeZJKsUCqkMNUirH6FkzXIZnPohkG+p5iEuO9vTTOmkVmdITC5AS7N6yj+/wLWXHpO2vH3RsYJhqa8fFY/z8PMrB5A16lQqlQ4ICpNqWYGtiNblooz61W9POrWbzGmbPYtX4Nk0MDGIbJjEVLmBjoY2pgAMeuYJgWhq5jF6pNlE3LItnUjF0p45SKGFaA0y+7qhZMCiGEEEKINwYJqt6A9i1DPrarB6dSAh+UqvaxippxdM0gY4+RDDQRNROMvewYPtWGv/s2Ao4mUyw453zmnHZGLbDYG8DlJsYZ370Lt1Kd4tcx2IPvKzKxFMncFB2j/ZR9HzSwh4d4/Bf/l0RTM+g6KJ9CZorV9/0GfJ+mWbPpXfsCwzu2EghFyE1kKeVzlHMTuHYFwzQxLPOga85830d5bnUqn6ZhBkMkGhpra6DGd/fS2DmLU996CWsfup9d1ho8x6aQzZIeGtxz9dVmyr6mseDs86nvmLHftEohhBBCCPHGIEHVG9DeMuRju3owrACJ5lbG+3ahPBeAgptF+R7JQBPK9yi42f2O4UViOKkmfE1H8xWgYQUCNMzonFalb28Ah+/jlIr4gK4bmIbOrPEB1HAf+NVpfQof9lQcdD2PycEBDMtCNww0NCrFAuseup9oqp5SIUd2ZAQrHKOcL+0pRKHw8XEdu7p2ywrg46MOUClRN018pQiEwzTOmEk0VcfGxx4GoH3hIpJNzax96H42Pf4ImfExnHK5WtnQNPB9E+V5VAp5Yg0NzF5++kH7aAkhhBBCiJOfBFVvQHuzSHvXVCnPo3ftarArwEtT/V5pTZUyA/iajlEp4QXDKNOiUirtV1Ai2dSCYVpMDQ3Uijwo5REIWKDp1dLwvo/vHaD3097HdQPN1AmGI5RyOSqlEoZh4JRL2OVq363qlMBqBkk3dALhCFYgCBrkJyenNew1AyGsSBjTNGlf0E3bvIUMbttCfmqC3NgYicYmdNMkPzVBemS4Nrbq/2oYAYtQOIxpBZi19DSZ7ieEEEII8QYnQdUb0N4y5HtLlPtKkWpuZrQnX9tnrLx7vyl/+9JdG81XeMEwmq/QXZtEW9u0AMNXqtpwV9dx9wlq8H1c14U9a5l8/+DNdJXyiSWTlLIZchMTaLqGcj1q66U0Y091vwDBSBzXyWAFAlihEJVCHs9x8H2FFQrhlMsEIhEaO2dRzKbB9ymk0+TTU7VsWrlYIE4j5UKOci53gGCv2mervqOTeF0Ds087Q6b7CSGEEEK8wUlQJdB0na4lyxnt2XnIzzGK1QBs3zVV8bqGaQFG/+YNrHvwd0wNDWDoBno0hl0s4uOD76NcF90wqiuUNB3NNFEva66raeCUSrjl6los0JlWgML3QNOwQhaem8H3HMqFCuV8Dk3Xqv22oFoJUKt+P97XA2hE6+qY2L0L17bxXIfcxDjKdZkaHCDW0HjAYEnTdFrmzmfRBW+praESQgghhBBvbBJUCQBmLz+DNb//LW6lckj7a4C5J7BC00DXSY8M8fxv7yIQjZJsaqbnhecZ2bkdwzKrU/M0Dd0yq4Ui9qxz2tuU1/cVvrN/YQndMKtl2PdUBfT37cm1l++j6T5upTz94T3rs4KxGJ5dzVg1z5rLSM92fOXjVirY5TKlfA7d0AlFY7TMmUcxm2HW0uU45TIbH38Eu1SqrvsyDOo7ujj73e9l5pLTDuk+CSGEEEKIk58EVQKAzkVLOPe91/D0f9+B87Lg5FXtWSuVGR3mqV/9nFA8DmjYxSKVYgFNq1bKC8dTFNMuak+/KR8wDKOaQfI8fNhvup1SHr53gEDqZSr53MGH5ynq2jvIT04w3r8LXdcJRCOAhhUK0bGgm8zIMJ7nYgUC1Ld1MPu0M+hYcAqptnbGd/ViBoM0zZ5DXUubZKeEEEIIIcQ0hxVU3Xvvvdx5553U19ezcuVKuru7a9umpqa4+uqr+Z//+Z8jPkhx9Gm6zplXvgfDMHnqzjuo5POv/iQAfKJtRYJJhVsKkesP4vsadqlQC7Y0XcfzFL6axPM8DN3ADEeqlfT2ZIzQDaLJFIWpyVoVQtg/yDr4MA7Qk2oPpRTBSJR4QxNjfT0EgiFC8QTx+noK6TSVYoFofQPtC7oJRqPTSqOffvm7DvE+CCGEEEKIN6pDDqp+9rOf8cEPfpBLL72ULVu28J3vfIcf/ehHfOADHwDAtm0effTRozZQcfRpuk4wFqO+o5PRnTuqxRteQVOok0R7BWP+Nmzy+F4O36unOGpOC3Jq65rK1QBJ4aA5Bo2dXQSjMUZ7d+KUy+SnJjAMsxqEHezcmrbnoAcPoqbvruMrj5Gd2wiEwoQSCeafeS7jfb10LTuNVHNrrQqi9JgSQgghhBCvxSEHVf/0T//EN7/5TT7xiU8A8Mtf/pKVK1dSLpe57rrrjtoAxbGVbGohUd/IZP/uVwyqmkKdzI0vx6vbgGvFsNMprMYpoo0Guf79+0K9nK88poYHCcbiuHua9PqeQuFhhkJ4ngsHWT9V7THlTyuTflC6BpqGUopKsYDrOPRtWEe8vpFUc+u0KohCCCGEEEK8FoccVG3bto13vvOdte/f//7309TUxFVXXYXjOLz73e8+KgMUx1bHglMY7d1J38b1r7hf1EygawbZnEG4PYTZCnq8A3dTBU3zDimRpGkaTqmEr7w9Pax8lOdiFwvVHXS9mpF62cFMy0IpheeAbhooz8MwTXzPQ+3ZX9N14o3N6LpOfnIc5XlEUnUEQmHq2ztY9Oa3ytooIYQQQghxRBxyUJVIJBgZGWH27Nm1x97ylrdwzz33cOWVV9Lf339UBiiOHV8pXvjdPax96P5XXVNVcLMo3yNeOI3S6FzG2sq0lDTczGbMYBrPcapro/ZM19NNE03T8OyXsl+e6xKIBDADQTzPRTk20bYigbiDnbMojyfxHHe/c9uVCqFoDCscwVceZiDAnNPOZHDbZrKjoxiGQUPnTM5975+Br7H+f+5ntHcnoWicWEMji978VjoXSXZKCCGEEEIcGYccVJ111lncd999nHPOOdMev/DCC7n77ru58sorj/jgxLHVv3kD6x66n+zoCMp/5Yp7Y5V+zECQhoZF+N1vZ8nCRtpLgzy1fpxKvoCma5iBOL5SaBpEU/W0Leimb8M6yrksTrlCOB4nHE+AppGfnMBoytBwShpNV/hKZ3wjFIai1cBsn2yVBuD7BIJBAuEwoWicBeecx8Jz3kzvutUAzDr1NDpPWbqn0fES+jdvmLZ2SgghhBBCiCPlkIOqv/3bv+XJJ5884LaLLrqIu+++m5/+9KdHbGDi2MuMjaA8hW6Y+D4o38cwLQLhMK7j4DkOmq7hOS6GZZIzJjHNXs6ddS6dcxvxVT1wTS2wyY6NMrBpA1Y4RCmbJRiOcPGH/5pNz/8HxVwfjW3zmdihqG/vZHjHdvJuH5quKE+GCNWXCcQdCkPsF1TpuoHr2KicoqN7MXaxQG5igiUXvp2uJcv2u65qYCVrp4QQQgghxNFxyEHVhRdeyIUXXnjQ7W95y1t4y1veckQGJY6PZFML8YZGSvkcvlKYgTD17Z0sPPdNAGx56jF0w6SUy+IrRfvCU6gUC2TGRuikmhXqWrKsFtj84We3M7B5A6BV00uaRry9TF1mFEbGKXiPEEgtxqk0kxkdwrMC+EonVF/GVzp2zqoOzPcxg8FqIQu/+icciuJ7ivG+HuraZpBsajk+N00IIYQQQrzhSfNfUTOjezHnvOfPWPc/v2Nwy0acSoX85Dg7Vz/HOVf/KS1z5pEZG6FSKDC0dTN2sYBpBQ4a0Mw69TSGt2+lUswTq6tn1qmnUSpvJxgN09x+FlMTa6kU8+x64QUqxQJ2KcK4r2prqgpDkeqBfB980Ayd+qY2cpMTBMIRYg0NtM1byOzlp8uUPiGEEEIIcdxIUCVq9maasmMjjOzYhue6oOmUi3my42MsufDtdLIUXyn6Z8151TVKnacsRXuvPm2/dKaMYQQpubtwyi7ZoQqlXAnDtNANk8JwjMLQ/qUDXbuCpuv4PrTMnkvrvAW1YEp6SwkhhBBCiONJgiqxn0qpuCdzVERDI5yIT8tGHeoapQPtl0qdCcC20d+R6+1BFRrw3D4cz0XTdMxAALdSOeDxfKWwQiHOfe81EkwJIYQQQojXDQmqxH6scJhwPIluGDiVCnVtHXQsOOWPPq6v1J4qfDlyvc1Mbt+J542gUa3kZ4Ui5CYnXvEYkURCyqELIYQQQojXlcP+qH/lypXkcrn9Hi8UCqxcufKIDEocX06xSG5ynEJ6CrtYYHDTBlbffzcvPvoguzeux1evXG79YPo3b2DtA/ex47mnGdq+hUA0Stu8BcSbmjFDYVzHxgoEqjvv6W+1L90wmbl0+R9xZUIIIYQQQhx5hx1U/eQnP6FUKu33eKlUkpLqJ4nc5AROuVQrY56fmuTZX/832599iqd+9XMeu+Onrym4yoyN4Dk2TTNnY5oWViCIpmk0dnQxd8VZJBqbiDc2EYxECScSJJtbSba0EYzECMXiLH3bOzjtEumHJoQQQgghXl8OefpfNpvF93183yeXyxEKhWrbPM/j3nvvpbm5+agMUhw7vlLsWr8GNxxFmQF018Yo5illM7i2zdTAAJVCnuzYKMBhTcVLNrVgWAHGdvUQrW+gfUE3wWiUZFMLHQtOYWDrJjIjw1RKRQKRCMmmZvA1shOjtUIXso5KCCGEEEK83hxyUJVKpdA0DU3TWLBgwX7bNU3jpptuOqKDE8de/+YNjE2mcVJN+JqO5lezUWYxz9iuneiGSWPXbOx9+lMdqr1VAvetBrhvkNS5aKmslxJCCCGEECecQw6qHn74YXzf561vfSu/+tWvqK+vr20LBALMnDmT9vb2ozJIcexkxkbwLAtf0zEqJbxgGGVW1zklmlqxgkEqhTxmIHjYDXcPtWqgEEIIIYQQJ5JDDqouvPBCAHp6eujs7ESXaVgnpWRTC9FQCMdXeMEwmq/QXZtQPMGbrvkLdN181f5UQgghhBBCvJEcdkn1mTNnkk6nefbZZxkdHUW9rFjBBz/4wSM2OHHszehezJUf+jC/u+P/MpXOYLg28VCQM674E7oWnVrNNkmmSQghhBBCiJrDDqruvvtuPvCBD5DP50kkEmj7lL7WNE2CqhOcpuvMWr6C/7Vs+Z6eUgde/ySEEEIIIYSoOuyg6u/+7u9YuXIlX/3qV4lEIkdjTOJ1QNY/CSGEEEIIcWgOO/UwMDDAJz7xCQmohBBCCCGEEILXEFRdcsklrFq16miMRQghhBBCCCFOOIc9/e+KK67gM5/5DBs3bmTp0qVYljVt+1VXXXXEBieEEEIIIYQQr3eHHVRdf/31AHz5y1/eb5umaXie98ePSgghhBBCCCFOEIcdVL28hLoQQgghhBBCvJH9UTWyy+XykRqHEEIIIYQQQpyQDjuo8jyPr3zlK3R0dBCLxdi5cycAX/jCF7jtttuO+ACFEEIIIYQQ4vXssIOqm2++mdtvv51bb72VQCBQe3zJkiX86Ec/OqKDE0IIIYQQQojXu8MOqn7605/ygx/8gA984AMYhlF7/NRTT2Xz5s1HdHBCCCGEEEII8Xr3mpr/zps3b7/HlVI4jnNEBiWEEEIIIYQQJ4rDrv63aNEiHnvsMWbOnDnt8f/+7//mtNNOO2IDE0IIIY4l5StWDa/iycEn8ZRH2S3Tn+8nHoizuGExiWCCGfEZnN5yOrr2R9V5OuTxPD/yPIP5Qdpj7cfsvEIIIQ7fYQdVX/ziF/nQhz7EwMAASinuvPNOtmzZwk9/+lPuueeeozFGIYQQ4qh7fuR5/m3dv9Gb6aXoFim5JZSv0ND4Xe/vqA/VE7NiLGpcxHvmv4czW888qkHOc8PP8YN1PyBn54gH4vyvZf+Ls9vOPmrnE0II8dod9v8bvOtd7+Luu+/mwQcfJBqN8sUvfpFNmzZx9913c/HFFx+NMQohhBBHla98ejaNEN7ZRkduPhXHxvM9fHzUnv8myhPsyu3ikd2P8G9r/43nR54/auNRvuJXW37FupF1hAagfovF0089gud5R+2cQgghXrvDzlQBvPnNb+aBBx440mMRQgghjjnXcbnzp08yuMGnTVtEKDhMU+sIg8nt0/bz8QHwfI/R4iiD+cGjNqZVw6t4fPBx5uc7eUf6HCxloW3VePGFVZx6hmSrhBDi9eY1BVUAtm0zOjqKUmra411dXX/0oIQQQohjQfmKn/zyN2TWBDDcAGE9ieaYzB8/Ex+foUS1F2Nbdg7xSj254CRjyT4qboXezaPcu/0Jls0+hY75dfia/5rWQL187dSpTafyg3U/IO/kaXbqsZRFT2iAeXYXmZGJo31LhBBCvAaHHVRt27aNlStX8uSTT0573Pd9NE2TqQlCCCFOGM+PPE9P7xB1qgsPB9MLEvVMmnMzsFwLA5OmUgezR09F6YqClabHWkODV096s8Zzagv9G6e4/IrzGEru4JdbfomjHCzdAuDM1jNfteDE8yPPT3veA70PsH5sPT4+o9Ykju4wu9xBRa+wVe3kPM9j1a40/VMlZtSFOWtWPbquHa9bKIQQgtcQVF177bWYpsk999xDW1sbmia/yIUQQpx4fOWzc+MIViGCoUx030BDA3RML0TETjIjvZDZ6aWEK3EqZgnfh1g5SaWsKFVKjEcGsacqrNm5AWfeOKPFUZLBJKPFUfpz/ZzZeuZ+QZPyFbqmM5gfpCXawp1b72Tt6FrmpOagDMXWqa2YuklABQAYNqvZqTXRzWRtG2vtKazaUoftKAJWNTg7Z07D8bqNQggheA1B1Zo1a3j++efp7u4+GuMRQgghjon+rZNM/I9BfKoFza82s3e0CgBBN0LZyhG16wg5MTR0wnYcR6swZgyhUDQwk2S+maJe4Jn84yx0OhnMD7IjvQOATRObeNe8dzGYH6TiVrCVzcb0RnZld2FoBpZhUXaqZdttz2ZyeJKWaAutkVYc5bAwP5PL02/CUhaO7qA0n4JXZNtkH7aTpLstweahLP1TpeN2D4UQQlS9pj5V4+PjR2MsQgghxDHzzLMbcMZ1LAw0qoUodN/A010KgQzbGleTcOpQuour2xiuRTGQIWk3MRUYZWPzE8Tt6jqrkKFYbi7A1E2Ur1C+Yv34ep4feZ72WDtDxSF6Mj14ymOkOIKhG4SMEJ7vobka56kVGFnFWC7N5sRmbGW/tJ4qOMCpxQW8NXMmm6K7mV/XRWZMZ/NQloClM6Mu/IrX6SsfuzeDO1XBrAsSmJUEoNKTprwtDb5PaH49wTlJNJlGKIQQr8lhB1W33HILn/3sZ/nqV7/K0qVLsSxr2vZEInHEBieEEEIcab7yGdyeZmJnCR19z5Q/9vztUbQybGp5kg1tjzOvuJQZ5QXE/AQFJ4uOQdfEKbTp89jY8gRbm58DIFwMs2FsA+OlcSpeBVMzmSxPcs+Oe7hizhU0BhvppZeIGSHrZFFK4SgHXdM5tbSQsya7axmpe3kcgBmVFiIqxKnFBTQ79RiawWnF5ZySPI35Z3k8OfAcujWFHvZQft1+RTH2BlOlLZNU+rK4U2X8oocWM8HS8acq4PqgQenFcVJ/Mp/w/Lpj+rMQQoiTxWEHVW9/+9sBeNvb3jbtcSlUIYQQ4kTQv3WSh+96kfKEj74nR8U+gZXm60ScOJqvYyubicQAfqiAgUFjoZ6+wDasySjxSj1DVKf6VbwKjw0+hqMcfHwc32G0OMqzw8+ydWorFbeC53vknFxtHDo6lm7R7jTXKvzNLndweu4UFlRmEXXCaDpMGlkmA1mcToMuowsvXWFn5CFW5+7F1E0Gt7zA1qktxAPxaYUw7N4M+WeGcMdKOGMllKPQ8FGTar8mld5EhfxzQxJUCSHEa3TYQdXDDz98NMYhhBBCHBPPPreRqaEiuC/lqPbSMYjZKeaNn05zYSa6r0PAw3DDNM+Oky+7RLP1OJZNLjhZfY6mo6NTcktoaJiaieu7eL7HeLFavMJX1YzQ3l5XUP3a930GrVGW6HOYXe7A0R0a3Tpm2C2UtDJhL8R4NEt9ookuoxPd0tnp72Lz6hdYPrQITw+wPdXHvbl7aY41T6s66E5V8B0fqzVKZaiAtufcGj5+LT/3Ukjp7Hop4BNCCHF4DjuouvDCC4/GOIQQQohjIu/k8ZW/J7RQ6OjsG1hpaISdOCEnhjJchsJbsZwAazOr6YvvpMWeg65pGBhofjXnYxkWyUCSglvAU9UZG5ZuUVEVDM14WRhT5ePjKY81oc24KZdmp55Ra5IVuVPA90HTwAcV0zDPqCdEA1YqzNCaXt629c1YdhBHc4mnm+grDTOzsZN+rYeB9kHObAWzLohmaaiyi2d5GLY2LZTy9xmPDyhHZpoIIcRr9Zqa/6bTaW677TY2bdoEwOLFi1m5ciXJZPKIDk4IIYQ40jpOSTK0eTeBQhTHtzGVhY5R225g4msKz/cwvQDtYwsg6bJFfwrfU9Q5zXiuort8DslQksHYdjRDo+yUMTWT+nA9BaeAhoarXEzdpOJV2CdJVePhgQbro9vQfI0lxXmgQdrM4fswFcyyMdnDqsJO3r/gfbRtbadhYxTDDjDmeEQtjY5KO3ZvPcUhm0Z/EUawAX+uT2BWkhjgTlUYaUijP1kk6gWmB3f+3gyahhsw8JUvxSqEEOI1ePVW7y+zatUq5s6dy7e+9S0mJyeZnJzkm9/8JnPnzmX16tVHY4xCCCHEEXPB6WdgLMwzlRpkqH4rw7EdKFwUHmrPf7rSsfwgum9g+AH0YoC6YhvxSj2+B5PRQXRlorImjlLYnk3Fr+AqF4DmSDMzYjOwdAvHqxakMHyDpYX5vC19NksL89H86cHLkuI8Lp96E4uLcwn4AQZCY/yq+SHcGRYt40l4JI373DBxVyduWLQYQSw3hHJDRJw4cSdFzK6jvDnIwNYpBren2TlSYipmkZizkN6mAE5tst/etWTVvzTfx8jYpH+zHeWqY/rzEEKIk8FhZ6r+9m//lquuuoof/vCHmGb16a7r8uEPf5hPfvKT/OEPfzjigxRCCCGOlD88/zzlAQ3LDWF4AUZjfQTdKAm7EU1V10cpqp866ugYAR3lQ3O5k1LrFP6ER0d6AbpvYTgh8m4JTXMBH83XSUy0MVubzy61g0ani5hdRzY4QaOpT+s7BdUM1V7NTj0NToq4ihLzwsxwmzENE7W7wPKJJTS4Ycyig2OZgKKiPEYtHRW1YKgAJZNYQwjX9njy/20nP1nG9yEQMrFCBq5tMaE8WnT2WUvmo2nVrzUfCs8Ngwapd86TjJUQQhyGww6qVq1aNS2gAjBNk89+9rOcccYZR3RwQgghxJGkfMUTm59B5eME3QDBSpSAHWY8OoCOScROoKsg1TVHCg0d3wbdMDADBmectpgX0/1EBlIYymDe+KlovgepIZqdelw7RDA9m4gRZ2G5GR+wjTKe7lIXnWJmpR0Pha40zjAX1dZRvRjZzqg1iekb1LsJbM0l4FqcW1rOHDWLGVoT8Y4GCtsmMDyXkqVDVz1u1iE/UsQwdHwfPEdRsRWVooPyAA3KhWr2rDVskPd9UkGDsMGe7S+FVxqAB+UtU9hLMwTnpI7Hj0gIIU5Ihx1UJRIJ+vr66O7unvb47t27icfjR2xgQgghxJHkK5+Hn32GzGiJeqeJYDmKgUlUJQllopTNIkCtd9XeUg6RhEXJLLA7uI2nezbQYS1CMzQ0pZMsN/He8UtpzrvkglPYrsEOz2Z9eCtd+cUADCd2Ul9oZ16li1a7oVpREJ9Abjkd9lAta/ViZDtbw7vocJsJEyLsBUlNJjBDFk65xNbCJjzfp+AFGdFsAkVoTDZgl1wa2qOkx0roukYxW8Euu+D5tRl+DYZGBz66pmF7iqChY1ga+D7KZVoJDeUqSlsmCcySZsBCCHGoDjuo+tM//VOuu+46/vmf/5nzzjsPgCeeeILPfOYzXHPNNUd8gEIIIcSRMLg9za6ns9SVWqpBkaZhKBMNDd03MO0gGhoNhkZU1ykojwnfpRSwGQ8MMGYOMFmexDIHafG6idhxWnSLOXqQsAem41PQysQ0RX2hHdssoaFTX2jH013Ao6I5ZM08DU4KUxm13lTNTj2+5rMjtJu5lU4MTWdWqYOArTOlZbH9Clu1PrZZWeL+Atz6Ah2VGLHOEL4PdtnDtHTCcYtixp523ZoGERN0NHI+xDXwPUDXwPPR91leBeCXXOzdeexeyVYJIcShOuyg6p//+Z/RNI0PfvCDuG51SoFlWXz0ox/l61//+hEfoBBCCHEkZMZL2BWXkl4g4cZqVfD2dm/S8GkwNGYHdQw0PAw812OTvp3RcB+pShNkYEdoA6HGJEuHLyBMAFf3KAMxL0IukGNd/fMMUaQQzBA2QgRLSSYDg8TcTronO4ioMBXDJmcWmF3uwNM9xgNpLCxGA1OMW1N0VJpxDQ/PVETcEFkjx5N1mxiwcywfb6Eu00AkFaZrUT34sPGJQaZGbAqZCpWSQyBs4Jk+uqkRCJr4noeqKBLV5BQKQPkcqG4FysdNlyltnZJslRBCHKLDDqoCgQD/8i//wte+9jV27Kh2kp87dy6RSOSID04IIYQ4Uob0XUzl0qSyMzA8E3wNT3cwVAAAhSKsV7tWZZQiFvAIBspMmCO0jM3FckK0miXyMwq82PYYAKmJ82kgQUgD2yzwaHIVf6h7jNbcbBKVBuxIgb6WDeTUJHerHgC6Ku30BQbpC43Q4CbIRcr0xUbRXZ11ka0oFCvyp7DU9ojqETzX45HYap4PbkYLGhQTac4wltMWaa0FRZmxEnbRqZZHV5BqiZKbKBGOBwhGTBo6o+R3ZPGmyrjxAFbeRvk+ekAHT8HeFlUaoEDlHUobxjFiFrHzOiSwEkKIV/Ga+lQBRCIRUqlU7WshhBDi9SzXMMpUYpB4qRXXcqkrtALgo/D3/F3yfTxNETehojn068OEy3GSpWZco0KkFGfm1Ck0mhrNoQr5uu302g1UzBJ/qH+KF6PbaM3OYdHI+RjKRKU9bO9JcslJlK74dcMjABh7+mIpFIZmEPJDxMwYwak67HIdq900aWMnLeE4vfFxnjemmBU+C7fcwpl17yY26pHOlchNDhJvCKEbOqGoRSFTwfU9xiYm0T2DGR112EWX5qBFZEkDPTszTOkaYcAqOihXoRk6gWQAz96zDstV6AEDVfIorh0n0B6TaYBCCPEqDjuocl2Xm266iW9/+9vk83kAYrEYH//4x7nxxhuxLOuID1IIIYT4Y3XE2xmquxcnA4liE45hUw7kSZQa9jT/tZhwFVOxEZIqxKA1ysNNDzEjewp+bk9KSIMuVc/s9JtocFK0OA2ogE/Gy9NfaefF6DbilXoMZTIZHaS+0E6snGKpOb9W6W9nYpBoIErBLlDxK3jKw1Uup3nnExppJVxJELPrKIYy7Cpk6bWGSXgh6sdmE4x0kEjqeI5D44wY4/05hnJDaKpMvR8gFFdsivfiGjbhdD3DUwatlTDa+jKYGrMDBsWZKfL45HI2jg+WUpjNIWbMq6O0PY07VEC5CiNq4dsehRdGAWQqoBBCvILDDqo+/vGPc+edd3Lrrbdy7rnnAvDUU0/xpS99iYmJCb7//e8f8UEKIYQQf6zTW04n0/AVRkojdKa7ac7PJFZJATq+5lcXG+k+ac9gMDrGxpYnGYrsJGRFaMnPJKaSlMIZQrEyEaeRsukQtkO4jkejSnJh9kx2hgYYD07i6W6tQEWrFuGc9BKCKoCtOzxqvcBkogg+2BUbUzMxdZNEqQEIUbaKJCuNlIw8eBqt6dkESzGCfpBwqQTtYxhWivH+PBkvw6SznlP8FgJmgERIZ8esQZJz6+nfuoXmfJzGXRaW42HrGoGATrOpkXd8NAXhgEbe9jHSDnZfDr/koWlgmDqapuGXXdzREvlnhoiBZKyEEOIgDjuo+tnPfsYdd9zBZZddVnts2bJldHZ2cs0110hQJYQQ4nVJ13RigRg2ZTKhMYpWjo70AkJOHH1PBUA/5FJqGadYKjJreBmpaAtjs7eSS/YwK3g66ytbcUojnDI0gwYnheEb+J5PxszhaR7NTj3rk88CEK/UkwtOskg1YlUsesNDzKl00Kna8ILjJINJyhNlIlYE5SsCKQ3H9AkVIijNI2hHUIbCzFuYThCvLUsgHyc32k80kqdg2GyPbWPWsEWbm2BcS6Mcg0BOZ+vUFgItAbpzMeKuhw7g+SjPx8vaJJSipINn+2R1aK8P4mVs/LKDcn30sIGeCkDZJDA3iTtcxJ2qEDyuP0EhhHj9OuygKhgMMmvWrP0enz17NoFA4EiMSQghhDgq3mZeRc9AFsMJYJslepMbiNop4nYdvgZeNM/S5KmMDhVRng/ppZTc0wguLbJ4RQdL9Bk8s2o9k4MxCp5Lhiwxw6VoFJkw04xak2ga5OpHGHF7WFycy4x8CxEVYnapHd+CYH2U0IiiyW3H9QOMB/toibYwp6ONp4trmRrNE1JRAuUwzcWZ6G6IqJ3CHg0QD3rUj2sY/hQpTzHfijO70ErQs+ikBR8f7EXMiM5gkTeP2LCGq1wU1RoUuqHhK0WiPoSKBfDGirR3JZh1ThvZ3+/CK7gYUQs9ZBJoiWL35yitHQMdgsUEvvJlCqAQQhzAYQdVH/vYx/jKV77Cv//7vxMMVj+zqlQq3HzzzXzsYx874gMUQgghjpQFxdMoODspkiNSTtBkZkHz0ZWB4ZsY6QRTJQ/f0SgGMkQL9QQGUpSKGr8tPkz30i6avHYmLJstsZ0YkxG8yAClyDCj1iQvRrbj4+P4Disqi3hH5hx0VwegPzjCi6md5MZNZg6dBlqUOebphM0woTrF73bdTzlZZtgcRvmK2SPLqau0UKnPYUwYFCJTxOMOZr6NgBcgYcdoLjWjKQMfHV2r1kafUaqjZU2SkFYt6WcaWrXSn6ahmRpOfwHP9QgYGmbEItYQQtMhsqyR4roxNF1HT1rocQtVclE5Gx8orB7BaosRmpc6bj8/IYR4vTrsoOqFF17goYceYsaMGZx66qkArF27Ftu2edvb3sZ73vOe2r533nnnkRupEEII8UcaLYziK4VuGOBD0AsD4Jo2mqehNA/bzqApg1AhAb6Gj0Z4qo7KmgqPjLxAnd5Iwm3BLMWxKZPUUqSyUdxQCD3ci6c5uMqj3W3GUiY7wwPMs7uYiOYYqp/itPRFaH6QQnyCWYVFlPuKbCk+y2iyn4ZwPRErgqEZBBJB3EmXYCZJPjDF7tb1pOpmoG/SibphSrpLwLEIKA2jGrfh+z4aGpbS8aBaY9Dw0XUNDB1N1/EyFWx8fDQUNvmig92fJ/6WTlIdMdypCmZdEGeyjG9XKyOifNzxEvnnhgjOkYIVQgjxcocdVKVSKa6++uppj3V2dh6xAQkhhBBHivIVz488z2B+kPZYO3b7FJWtRbRCgLJRJhucwFQWMbsOzdfxlUM6PMpUdITm9CwS5UZ0z8DAoC7dTqAYxYtUyLYNEouHqRs3OD19CpryWVHoptNpYii6m1FrihFjEkyNBc4szKCFVR+iNdaK55aJBBoITXZQLP3/7P13sKXZXd/7v9d64s7h5NR9OofpyUmjURyQkJEsX/C1hCVsysLgMsYGqwwWRSGjutdVprApmfuDwi6ukH8GlSVxLxYgLCGEhCZpQk/s6RxPTjuHJ6617h+7pzWjGcGMNCPNoOdVJZ3unc46z/PMPvvTa63vN8V28yxGNxPrmIZYo+7X8USV08556lOblKMJBrktmv4qe1emsFQO29ikQpNqiUSgAWkMQozCjkRiAG2B5VgIzxrNOg0TjDIYCY4ZNf81/YQk7hOda1H94b3P2TfVBilGZda1ASlI1gbElztZwYpMJpP5Ji87VP3e7/3eqzGOTCaTyWReccc3j/OZM58hVjH9pM+4P040kaca7CKVCbm0yOXaCbaKS1SScYZum9gLmEg9itVNKq0y/SgPCCxjk0uLqIHHMNrh7/yDN5J+McLtpqRjFrmtkDcOrmdN10lkwheqD/L16ZPcVbidQWRRUYe4W9xI5SaPbi3myvE2sUpYLZ1jbDjLdGM/Y8kMbz16Fw23xE7nj2hUz0DbUA6r/EDwZt7Y3M+48bGxcCxNywYrFSQGLCEQxiCkvPbzS1siCw4qiCHVoEd7qxz9jWMkDJhEodoRRhviyx3SVoRVdSncPkX3aysQpAhXgoCkEeLt/a6fykwmk3lN+7ab/2YymUwm81q31l8j0QkFp8Bjm4+xZC+xoI9RkbuIrIBCXCGxQ55Y+DpSSOZ7h3jb6j1cl9aRCPAsVoyikRps7TJh2eQth4neIs88dYmpQolpu0ipZ4Odpy86XPJX2RPOMZ5U+Lr7BMfKb2Hz0RiT2nQvDunFPfR0n+25NSrhImPDGTydZ3qwh5xxMc9UGTvgUBdHKbUEb9u4iwOqzoQuUpMWlrQAgaMtJOAKjX91hgoxmqFCjIpS2BN5TKQgNWBJEKM0JXMOup8gn3OsDKNA1X9oHZMYhCPI3z6Fv6tMdL6N0QYTKUyUfndPYiaTybwOvOxQ1Wg0+OhHP8pXvvIVtra20Fo/7/5ms/mKDS6TyWQyme/EbHEWRzo8uf0kgQrwbR+RWhSiCgVVRSCY7xwkKvTp1DaoxZOUlYuxElrKULYlIqdRicOkstnjOEggiQs8cd8ax/cd54bqEfYne5mpj9HeOs2ecI5Epmw5Te6Mb2TucgUd99gabxNsxGwur7FpzrDlblGfXscd5JmMFxgLZxiWW2z3bKqXh7yzmMNp3MX1yS7K0saSAouruUgYhIGiEFiWRGgNQgCjJsXSFjgTedy9ZZIrfUg1qhuDFEjPxqr7pBLMIAHHGoWwqkdwtkWyHeBMF9BBSnS+Q9qNMJZA2BLp2wg/+/fYTCaT+WYv+53xH/2jf8T58+f5yZ/8Saampq6t385kMplM5rXm1qlbAfi9E79HN+7i2z7KTtDFiCRNyA9rjEfzFLaLPM29bNkrtMQhdqkyE8ZiIAas1JY4569Sj64j7U7TVYaioyimHjOrR+n2i1zIR8SJRtcmWdk5w7K3St7K8w+H76YQ5cmlmngzom0FTE5W2TKCkltifn8V58Q5jiYVErWH9laOSV1gLvXIbXuUgwIuFsqAEAILgxgVTkcayAmQWowC1dXfxwYQRQdrLIeOFEZpcCSy6CDLLt58EXumiAlTuo9tkQ4T7LyLXfSIzjTR/ZjofII9kcNoDYlB2BKTjl7HqfvfuxOayWQyr1EvO1Tde++93Hfffdcq/2UymUwm81olheT26dvRWvOpr/xP1IZF0SlTrRXpbgcgNdqL2duZY3/4Hp4ee4yzco18WKOKT8MOMekEff8Ejfk1ps/4FPtlVCoZaI3VLOCoPNJ2aW0GWM40RcdiPp7nmF9kt9iFvTdP8PR5fBmyXT1LQyyzM9jBkhblUyvcdTyhap3jojXGwN9L3rMoqjxdIyhdnX1yhcBmtB8KIcAYjBBXi1N84+c1jCr+mX5KeL4FxiAcazRDZUtQhmQzwB7L0e3GrCcaaUCGCSx38VyJt79KfKUHtoBYo8MUEyvQYJddnF3l78WpzGQymde0lx2qDh8+TBAEr8ZYMplMJpN5xWmj6V1R7Fu9DaEk9UKNuDhE9xMKpshkb4r9dg4ZVzi4PsPFOKWVCrYEdPIGUk0xqvFM/gLt3R0mt2ew7CKXB13m+vspihwyski0ws153Hz4CDvLPfZJh95Ok/bmEiuscXr8BJf9k3SHXVKdIo1kZn2eSX8/66UCQWcKRxdIEwchbGpCMMSwFmsmHEnNkiA1AoMUElsDQoz2UF0ln10BmOrRVwXYFmaYoAxI30Y1QlQ3IrElxX6C41tEkSZdH+JWXNQwAqUh1EQrPcTVJYMmUahuTLLUzar/ZTKZzDd52aHqt3/7t/nIRz7CRz/6UY4dO4bjOM+7v1zO/gUrk8lkMq8dxzePc9/ph/CjSdJqH7vpEUQhA2sAqcu0qCARpMZQsmxmLUk7TcEI6p053GnF/JEC5UWLM80zfL16HNdyWegdYnpzHDfM0wv6lMtDimFCcB5KliBpdWnEDaIo5HjpBO2ozY2tfWw5LYa4HOns47DcQ6GQsNfUqBTzrCmHRiq5ZBS+bWjLIS0pECJPXlrkkSg0PTHA0i5F6TKavzJoobHNqHjFKFhdPQDmakl0AzpSoAwm1jjzJZJGiB4maN/G1hpZdDBSoBpD4s0+VnJ1r5YAmbMxic6q/2UymcyL+Lb6VHW7Xe65557n3W6u9sdQSr1ig8tkMplM5tuhjebRjUd5YO0BzjbPEhjBAVEn3DJshTtokbJZvMJ0ZDGMyzg21CyJEFCzJROpRUOBkIDQLJTmKRYlf3bpz+hGXcY7u9hdPEBhXnJpeIK4v83scBbX5CmHEidfIE5jrtQ2KbQcykmBA9u7cbSNnfqsD31qOk/OLhBOhJT6OeraxlUCHadsqoSh6LBUf4Z9ZopisJ9UaIwedZFyjI2xIVYaV0gGpDgI7NE8Fs/d7WxiBb6FSAFHQGLAlfixgrEcaZCQjxVSGZK1AUqA6qdIni17Ya4e0xQEWfW/TCaTeREvO1R98IMfxHEcPvWpT2WFKjKZTCbzmnR841G++Ff/k/G1AofNJI/6J3mw+nm8oIRXyDPVXWSytQhasGQ1mUmnyDkWkQYJ5CQgFN2pdVAWq6efIUg2uNK9wlz3ADNLR2k7CU1xmUH5AnuGFQraY9PrUehUUTrBhDZT7Wma9jYmB462WfLXubl1KybJ05MpMnUphXk8VxC4KU4fbBnQ1g0uTz7JiZn72L/1oxBo+gZKGGwkLj7CQCJGgSfSAiEhFRobgTCj2SWRt8EW2OM5dDcGDXLCIXd0DCvvUqy6hGdbBCcaoA1qmKBj9fxQxtW9XJ7Eruay6n+ZTCbzIl72O+OJEyd4/PHHOXTo0Ksxnkwmk8lkvmPd89vcfekY48MKQkuOiaN8rnoffznxVQQCbTT7d25BiZS+3aavakiRoyAF2hgohLStBjpQ1HUdcQXSUEMVDrYXmY3rtP02fr/MzcGNeBgKJsd0YGO0pAm0BxptwZqrWfZ7HNaL3N67DgeXdWUhFKS2JtUxnu0xIfPoaZvlSpcr5hGW/FPc3D/MjFck9fo4qQtpDmkkBoGAa32mfCHpiYCgmDKmq8hQoDFIbbDzHsIIZN7FDBPsio8zU0AgSVshJlZorTGhQhYd0AadfGPVybMBS0iJVXWz6n+ZTCbzIl52qLrttttYXl7OQlUmk8lkXrOm4joDFZMYjaMcKpS5rnEzp9wl1isXiO2AodulVdig1h/NJvX1DIk0uAiYj1irPMX4pf0kDYlPjWJngvdVptltHDxy0KvQUQk+LttigAe0SWhHIZ2eQqeaXjFAxQUmBruQjoVjuRR0jj22JCcdckLgaE1QTPDcAvkDVf7eO29iYXuWBx78S2aWCtjaZij7XCgvEzdvZrepcbXFL4kGJQ0DDNu+oj02oBbmcXSOvDbkXZvJcR9fGaRvE7YCkq0hva+sjHZipQbVDpFSoBGI1CAciYwVWn0jtCHA3VOmeOcM7mLle3RWM5lM5rXrZYeqf/kv/yU/93M/xy/8wi9w/fXXv6BQxQ033PCKDS6TyWQymZdDG80jG49wNj7BPneM8tDHwkaScCCd5E07b+QPS5foeU2UTKkNplFWymZpk3a3jI/HUBqW/GXyu2C2NYtqu/gli7gBs8EsZTthrbjNghijGfVIYouKJQmU5pTcIbQj5r0S1WGRQVBnQ4ZMWx6lnMX4zATWOYNv66vBaLScT7YM/ZkhlYN7sCyL26dvJ3C3CawGy8VN6t0CysrxyDDBkoqyJYm1YQBMOA7SKKYCD3lZsBEGbIuEcKLEPldSLPv4vZDoShcda+yCg+rHCEA4FipIcaYLiBKYOEUmArmYI7ncBQ24EqvqkTtQz6r+ZTKZzLfwskPV+9//fgA+9KEPXbtNCJEVqshkMpnM99zxzeP8lyf/CxfaF9hXnuOHh2/jyHAvHi6zwuEd4TG2dt7MvRP34Vke/rDEMNfhydoOvZ038CbvbqrzdRYO7OYtvTtxayXOrjVIugrPgbFyjmIYsjceY1josz7RJLhYwRvaSCulLF1uYYKqspGOJLQtvIrEtcvM5Su42ymRiRgiqCAQGEIUKGh0dxg0z3D97jtIlrpMBFWaJmBhOEXP6qGHJWZMHk8KJIKcBMux8MsOoY6p9Gw8Y1OxNBMagnZKtSDJY7AKDolgtA9ruYcsOBhHojaHkGrSzSFW3UdIiU4S1HaAVfVHlQLlaOlfuj0kutjGXawgZLafOpPJZJ7rZYeqS5cuvRrjyGQymUzmO7bSW+FK9wr9tM9j/kmO5vdwMNxFqiWJUPjG4WD3EA9OfZ31ygX8uk/Fq3C4dJjhTMCD7kMcqh/iR62/w/DsFs1gh/n8EGFZGO2QjxWWsPG9Aq7ncMSZ4nICtk6Zk3AgKVLEwgChMAhpMdGzoOTgzheJmgFxL6HgCFSkMWhCmZDTLnZkw2M9BtEa8XKP8aSMkzc4tT7xjE/vsU32SoeckDgSXCFwSFFATThoUjpKk5Mwb0mUlNSNwVnpE2sDlkDkbfQwxQiw8jaqEYAtMVojLIEoOjjTBZKNAfa4j+4nqE6M7sVEV7qoTkQRshmrTCaT+SYvO1Tt3r371RhHJpPJZDLfEW00pxqnaEdtEp1wbLifQ+EithgVcrCNoK01XjLOHeatLOfPcMPEDfSTPsNkyFPbTyGE4MG1B5nyCywOJnhIP8GimcA4sF4ecvPwwKiceTlPZZiQbhq0iJjO2ViJxgLk1Ya8LmBZhroR+ICz1ofpPP2tgGI/RlkwVNDXhpiYZ9wtbo72EW8MUO0YWbApW0WmD+7jjtunufj0pwlSieNATghioxmYhCYb7K3NQ2qTG6a4QmKkxKt7yEFEp9PEjWxsY4EEu+ohfRv9bINgAAPCs7A8CxMqnPEcSIG+WrwijRRWwcEkhrQV4X3PznImk8m8Nn1bdVEvXLjAxz/+cU6dOgXA0aNH+bmf+zn27dv3ig4uk8lkMpmX6vjmcU7snMASFkooppI6oYx4onyGGztHSIzFaqGHnU5x58q7WfCvI2g2ETnDGfMUe7pzHO0dI0RzeXKHCV2k0vbpywCDxgkTelafUlDGXh8QA0VLUBICqQxSgkagjAEhSIymaxnKysGKNcnGANmLKegYLI30JY5x2UhDao5m1swiZBFpS4KdIXpNIYXEWe1hbp1i0ptCWxEKC4PEEoAVU04cgvYAx/JQdRtTKFGPU+JegA5S3KHAyBQlwLIk4TAh1RrpSFxLIhwBGpy5AvnDY6StCB0kBCcb6H6MSQ1CCvQgxaq62LUsUmUymcw3e9mh6otf/CLvfe97uemmm7j77rsBuP/++7nuuuv4kz/5E97xjne84oPMZDKZTOZvstZfw7Ecan6NreEWLb+PFdnsE3vJ5XLYxmJX4pP0E1p9qOlFxirztL02M+Fu3pYs4huHGMXZVszDMyucyz/Gqr2JMDCVjnEibHBXfDszeAwETEsLIcGYUYlzYQyRNkRWyvnxLcrleWpLIGOFAnQ3RtkhoUzxhy7GsVlyE07ZHfbIGgt1n0EvJog1dmrQRjE42SB3bIIjB/ayevEioTZ0VIrvRnTdFhNJDZUovARaThctDUXlkwQa1wAIBnZEKbaIlSZINOvtBF12WJBQci3cmo9/oH7tWKpejPBs/P014vU+zmQeb38NZ8zPqv9lMpnMi3jZoeojH/kI//pf/2v+w3/4Dy+4/d/+23+bhapMJpPJfE/MFmeZyE2wE+yQs3P4e6pc6G6zp7uPQrFMVHHoPrWJjSKQEXbiIQQ43QJ3qHEmbIsUyAubxcSiuzqOKe1jrb7NTpoSxRrRzrPZNxRdjRQQGfCRCDEKVkoKIqlpTGjm3reLta8omkYxhRyVpTAGS1nYyiJIYXWQgjBMFC2mcgHeoEXa8ZFKY5nR6jw9TAnONrjhnXtwmiHiXIucjFhxVugmPSZ7Y0hlXX2wxnQTlGWDFJirtaPyiQtC0NGgDQSJInZ9tlwXe7bAxO1TaK3pfHkZHaQASN9CC0h9h27Ro1DxmM2KVGQymcyLkn/zQ57v1KlT/ORP/uQLbv/Qhz7EyZMnX5FBfad+67d+i8XFRXzf58477+Thhx/+Xg8pk8lkMq8ibTTaaOaKc8wUZhj3xxkkA04VLnHS22Rr2GXr8iqBChkoDYFNahTNYZPxWFCTEgtwhMAGxqRkryxy9/AYP7H5I7xj813sbh5jbDDLlk44qwesx5rzkSKUV/OMGH31PYdDxUnCr2iWTrQ4N4wYKIVC07cDulbITppyMtBcCsENfCaDGk4iaJYH2DkLfTW4GAxaK1YvrPH1P75AxxKU756F22zun36K484VukqhtUU/hZ2+SxiNApyvBQaLRBhU0SbxbYwAIyAwEPRTwpJL/uZJvL1VorNt4vUBUTcmbUVYRZdwLMelWHF+uceJr62ydr79vTvJmUwm8xr2smeqJiYmeOKJJzhw4MDzbn/iiSeYnJx8xQb27fr0pz/Nhz/8YX7nd36HO++8k49//OP80A/9EGfOnHlNjC+TyWQyr7zjm8f5w7N/yNZwi7X+GiW3RCfpMNXew9KKJlFbFAKXbdNludihPJigk9tmefIE89tvIVF5QgOuMGgNUoJCEcqYiipS15pnihepRhNopbCUD0LQl4ITsWHMVhQFOEaSr+VItgPoJsjAZcdJOKtSdns2cWEIlstG36KVCAQagySIDFEhobJjwJaokkPaiUlEDMbQWE1oX4lJhOSZasLwxiss1baxtus8whZV6WOF47QdjZXzkGUHO06RCKTjwKFxmlsBarWHFpLxsqS8r8z0XTPM7q8C0G9FxOGoMqDREPQSeosVenaf8fkiOyt9uo2Que/tqc5kMpnXpJcdqn7qp36Kn/7pn+bixYu88Y1vBEZ7qn7t136ND3/4w6/4AF+u3/iN3+Cnfuqn+Cf/5J8A8Du/8zt8/vOf5xOf+AQf+chHvsejy2QymcyrYa2/RqITKl6Fpe4SRafI+mCdtCMRWnCmfIVKNEOSCjydp5vb4bG5P6dZX2ECl33b78bWNsKAsA02AhcbW+Rp5oakic1UsEAnv4NXarMnPISrLKRrcSFIeIwe9tgOd3TnyG2BMpKKlowJi+1YcyLf5Nz8GhMlnyOdWyigMZamoQRjlsHzDNv5AUMXLjctrNBhF6BljEaRS10cOSrBPuhGXDkTMFk6SEzKqt0jDIvklcC1PPKhRroK4VhYcrTksHWqiTBQlAJfgFP1yLmSojNasLJ6tsVyN6YmBDlAY0i7EaVEEYcpl57awcvblOpZkYpMJpN5MS87VP3Kr/wKpVKJ//Sf/hO/9Eu/BMDs7Cy/+qu/yr/6V//qFR/gyxHHMcePH782LgApJT/4gz/Igw8++KLPiaKIKIqu/b3b7QKQJAlJkry6A36Znh3Pa21cmb+dsust8932nVxz0/40vvDZDraxsbncukysY3bkGhPpAlbTpS4EY1aVIBWsmyF5CmDXCUst+qpJYVhBp4YcPhEaG8GO2+PTU1+ksnOMUlrB1EJu2DfB7HqdzWaEFabkioqQAeEgz5oMkMIiwkUYyGsJqUWpUmZsospet0Zx2cLOJUzkLKYjQ8UWJK6gG9W4sDVgMOgjjSBQBYRIUF6ThXSWQQJlS1C1HardSfx0gZzrI44E0PYR2zbjk1VKrRDtGLQj0d0YLcBJQeZtIgFojTXmkaQpwXafjaUOq49v0QtTEjRjEtKiTbVooToBCA2WBqFRafq35j0he4/LfLdl19zr00s9X8IYY/7mh724Xq8HQKlU+nZf4hW1trbG3NwcDzzwAHfddde123/xF3+Rv/qrv+Khhx56wXN+9Vd/lY997GMvuP1Tn/oU+Xz+VR1vJpPJZDKZTCaTee0aDod84AMfoNPpUC6Xv+XjXvJMVRAEfOlLX+Ltb3/7tRD17Ndut8tXv/pVfuiHfgjPe30tDfilX/ql5y1b7Ha7LCws8M53vvOvPXDfC0mS8KUvfYl3vOMdOI7zvR5O5m+57HrLfLe91GvOaEOy1CVtR9hVD2fX6L362dv+ZOcLnDh1hnsad1FReYq4KKFwjM1OqnGEYDMxbOUNlYltppsFUsswkXoYI3GUTQoERvNV/yRrDFjoHIZcSn5YI19yOFz3mcbg5F1EyebpweMsXbzEonMM353AJ+VcPGQlsHBTF0dDruiwxxZMKIOlDWBASnJlBx2mdNKErxXPozancWIP27Hx8w7erGasnTAVSZIYhqRcSmM2c31MPmGwf50fvvMtzFycJdgckJvKU58uEF1oM/qe+2sAAQAASURBVDjfYRAmBP2EviXxLJjJ2Viuhcw7OLMFhld6bPcTvDClV3CYu2WKasFGByndx7eIdwKerffn1H3ckoszVyS3v4qzq/y6rQaYvcdlvtuya+716dlVbH+Tlxyq/ut//a/88R//Me9973tfcF+5XOY3f/M3WV5e5l/8i3/x0kf5ChsfH8eyLDY3N593++bmJtPT0y/6HM/zXjQIOo7zmr3gX8tjy/ztk11vme+2v+maiy62CR/dxiSG1BHY1uhX2bO33ZEexg0s/MTDKBvXkgyUwLIEVSwGyhAkIGILszyOLQQekkgYmiol0ikxiqExxKpKbcJFtCzcTgGEpDZZQkUp2rEo7KsTnlzm6FKDxWgSy7Xo5Nv0jWLgdEn7eVxVwhib/EAx4dkUACkEyki0AVJB4sPlYZdBu4ifWAghr/5+ctg1M4YVdUj6XZppSjUv8Uhpezv4ByNO28c5dO8ckw2foi3IKYG7q0pn/xhXLvQJOyG90ODszjMeK7QtqR4ZJ1kfYAuLYiWHZSSJ71K7YYK5N88hpGBwfBNPWFg5Dx2lSGVwPBe1FZIONWE7wbZs3L3V78p18WrJ3uMy323ZNff68lLP1Usuqf4Hf/AH/PzP//y3vP/nf/7n+W//7b+91Jd7Vbiuy6233sqXv/zla7dprfnyl7/8vOWAmUwmk3n9ShohaTuiHTbZ2ljn3KWTxM0AkxicmQI1u8JiYS+O8vEY9W/yhCDShs1EcznSAOxRmjEl2dIR67HmdJxwQsVcig0rCWx4QywcVFuCNEjbQkhBZztgqMHxbXrnWnTbEVvpBLK8F9suUO3m8fo5ksgikQGB6BObmDGp8dE8O+2jMaTA0LO4sK/PQ4efpjzlYXIR0hckoUJrzcLhKqWFEsKRlGyIlKLrh2zNn+Nr+guIjQLijEen0WOrlxC0I9JWRK8ZsR1p1hFsRZq18x06Q4Uy0HxmhzBI8fbXKL5hhtqdM8y+e8+1QAVg1zysnI3EYFty1LdqkAACZ7owCrWt6MVPUiaTyXyfeckzVefOnePGG2/8lvffcMMNnDt37hUZ1Hfiwx/+MD/xEz/Bbbfdxh133MHHP/5xBoPBtWqAmUwmk3l9M1FK0OyhgggwnDh1lvQOyYJTIVnv02lEXF5RpKFhyjbYDoAgMbCdGgSCg76gKAUI6Kkcp03CjtJYxsGgwYAVeFjKx6OEhY3wwCSaoJewKaFY94gbIds9javG0YFAoyhIh21Cniyc4bBapC40KlXE3hBMedTMSgqk0YQaWlLS3ihgTdmszJyk3jjE2NCiYAmkEDRW+lxZ7uFaHtKGaCpk76276TvrdNbX2WMdpGtiQmdIObRJ8jZ2zaOcGrTSxEGKsAQYw1aY4LiCmgYRKljtM/eWeby9L1zC5y5WKL19F+G5JgCy6KB7CfFqHx2kSFdi115fS/4zmUzm1fKSQ1Wapmxvb7Nr164XvX97e5s0TV+xgX273v/+97O9vc1HP/pRNjY2uOmmm/jCF77A1NTU93pomUwmk/k2aW14+HKTlVbAwW6IlAldk5LHRWyVOdvf4cidh9k+1eTEySabA4VAUJBQ0oKO1pSlYNIWuFKQE4IUwGhsEaH8y2xZmnp/Hkc5SGwmjE3OFgTa0FCQhimWsDHa0G/HPN1roYwi0gkTxmVcgBQ23RQ6JsctnWMcoIAjbbAtWpGha4MPSK1paWjkLCb2V6ms2ryx9BbifTvk23UmliW+bzFMIi4+fI5Gx6KQzxEnFofn93HXG/dT2XB4pvkMl9RZEEXsxGJPPs+huSJRY8iWucKg0kZteUghKdR9jIIk1lQcCb2Y9L5VutLgzZZRnQi75uEuVhBSIKTA31/Fv9rHanS4DPHlDmnrG4/NZDKZzMsIVddddx1/8Rd/wa233vqi9//5n/8511133Ss2sO/Ez/7sz/KzP/uz3+thZDKZTOYV8vDlJn/w9cs4jYSNoeb2RCIQdNwBAYbJuIa3t0pvM2A1DHh2dftQGxSGshQ4Amq2RAL2aJIKgyGOe/i9K8i6h633IHEYtySL3uixBiBSNJTEGEjjbyzhQ2iwoaHARIa8rQkUtLGYL45R0pCmElcKGkpzyQZXaSIEbSnJuxas9LEcyYyzgLuzF/IttNulYWJkGpDQZ5B4REGIk/hsr/RZPdviln23cP349TwQPUB+POJye5kpUyNe6bO6tEE72kKFiqETkTdlpJR4JYtqK8QKUiRgDRP6968Tj7cRno1wBEXA+xb7pIQUeHurZPNTmUwm83wvOVR96EMf4sMf/jDXXXcd73nPe55335/8yZ/w7//9v+c3fuM3XvEBZjKZTCaz0gpwGgmLHcOgE3PFsijZPkNbMulVuCGdJjzfJh7EpCJB4gKCHaUxkSEnoSQlZUvQ1QYQJET044h1ZdNlDweiEkXHIlKCvBRYjB5blpCXgoYyGDQCeTVpAUIilIW2UnY0yFiCGO23KhiHktFIe7T0bsISBLYkMhrLlkgNRkFtpkCh7LJxsUuaKLxuxLSEXKIYOAmD2pB21KEQVdEaWr0WJ742+vV999zdrPZXaeoVinqK/IUc8TBgSErR5Bm3DOenTjPZWqQ2bbFn7yLia6tYqFEuNGCGCYMdsPdU8GNF2opwjOb45nHW+mvMFme5depWpHjJ27AzmUzm+85LDlU//dM/zde+9jXe+973cvjwYQ4dOgTA6dOnOXv2LO973/v46Z/+6VdtoJlMJpP52+Wbl5KJuRfvDWi0YW+gGXRTTDMhTRI2BbQLNkfK40wEKe0Ta2w8cYXlWGDbNrFMEFqiZMqmEgglmbQcCpZg3AIpIMKib1yksRj3x5n28lhCgJa00lEhiZIExWjGC0ZZSlz9f2GB7UiwBcPxHdyNOjIcBS411AS5lMCSYAxFCTVLMK4MgRREyuDkbMKCQ226AIBKNa5v0b0Y4kpQWrAiBTu9PMZpIasp6SAicSz6rYjuTsDN+27h5FqXc80ldqe7KAgfnxQ7sUiESyxayKZP3g6I1Dm6OxZF18YoDcpgDKjEkAxTBmdbUPGwtwZc/sJFnm4+TNPvcd/YfQDcPn37d+GqyGQymdenlxyqAH7/93+f9773vXzqU5/i7NmzGGM4dOgQH/vYx3jf+973ao0xk8lkMn8LxZc79B9axyQG4Qj82ya+5eN2LQ1whEUiU5rSYStVbPciOsttykYSao0rBCLWWLaDKyxiPyAhQsmYxIoRvWlKxrBL2ljSMCVdynmLYZrQ1y7S0vQLXaaCOqGAJW1wU0PPpDQVyGsFc0dr/6SU2FXo6Dai5zBuJEVPMFCGhtZ0SQmNR0GMSqeHjMKVssFRkqIjsSou5zcv0Gz2EN08ta5kDwYHQYJgOylQiHz8qEA91RS1Q7rp0LcD4iDl0SttHj1TI04qxAPNHqUJBeicg+UL0vKAw12fG5IJhu0+fhSReh7DWOFrgzSgJEjfQilDmmiiS128RsQRZwFRdriPJ1nrrwEvDMLOrjLxlQ7huTYYg3+gjre38rrtXZXJZDLfrpcVqgDe9773ZQEqk8lkMt+xtBVdK4OerA+IGyEA/+u/Po3vuxx94yxzh2qjst2pwXFsPBnjOIKiJSASyBDyHpQsC2UMDgYVaaSwKNQ90ijANT5TsUfds/CMINSgnYQJI0mkQEoHYxSOkUzHeaSVUBKCzVCwnAJGIgVoNNJIsMCSAm/SsHrkcdYH67z58lvZ5dpYRqItkMKw4nXR0mMqyDOOjREKoy281BAJTVgtEE90OPXMZYwSWKrHopikaEm0EHjAuBK0HYe6ZzGPwLYFXt5l3bZwcjbnWgFxojk8U+b0ahtLejibIUYK/FKJ6xcOMHxqGzcC43qUhId3pE7YjnGudCBSxAZUpMGT2HmbRGus0CIXF4jDlEmmmcnPAC8Mwu5an+BUk3Q7AAzJSh8hd33LPVmZTCbzt9XLDlWZTCaTyXw7np3lSBoh7WHKsBORD1JY7yMcycpyD1xYP9vGKMHauTbFmsdU3mbRkTjDBA10lcERgoIUVC2wxKjghGDUj0oIibANkQrpuS1cp82NnSPkAEsopBFIBYlMEWiUsdkQMWiHSS3ol/tYfR9ppySOxI49DIZUpNhSkuYiclWHCwefZLlwmqhpQZhgmTw9YygJwRgCN6ySOIKnnR4TkUfdSXFMmRRFYiTn+kPUagO7l2eu4uD1IwpyAKLw7M4tpCUQQAEbx4KuJbBSTbngUhnPMW9rXEdyer3LvkQwGShSbSDVrA9SZvQUuYJCiZRqnCNnFagcHmPnVAOlDJYx5AykeYvioTHYHjJcD3AVVNIcWhgONQ4y290Hs5A0Q1Q7RuRtdDshsvuodoTRGmFJ1DAhbUVZIYtMJvN9JwtVmUwmk/mueHaWI2hFtJshjbyDdCR75ouMHayx/SdnYR+MOZKtRDNoxww6MdqV2GWH8brHIEiwxWi/kyMEVUsihcAY0MJg2QLLNXTz23QWllgWFzncmMPBoq0VVWmxY0I2RRNV6OOaAk5nmh0rpqokNpJKr4A0FtPGRhnJDiCMQDgaeyaiLVqkno3oOdxReBsX9DKxD7iSSiSx0YylNhUhMKnBK9a4Eob4ymYgNFs6ZsLOU4oN7a0cU7Fm946Doy180yMVIZGXo285tPM2eXLovKQdxEhtaAPuniKz+6vMXD22K62Ag1sR+TNtmp5N2RIkxjBEUB8fQ3di1DDBHs+xs9Jj58ltasMUDSCgMFtk7L37OP+/LjPcHGBp0KkYHeNEMjzXgcNjmDBFdUJ0wyBtgSzYmEhhAoURCqvgZL2rMpnM96UsVGUymUzmFfHc/TZW1QUjSFujWameIym1QvxEE+VshDLUyy5LsWYp1mw+sEapGdHbB7PCkAoBFky5gool0JHGkoKd8ZjuqiGOLXJSEhuwNLgCNJAKiTYJsUpIikM2rVVmN2eIRYLnxthpAYOhLULWwgAz1eBGbzeVwEFqRSQVKjJUjEVVSBxHoElpJgJhBINBhCdK5BsVRDNPd2NIdaLOMFfiyXZEjpQFfMalg3EkOlLYgST08qx7fco6Jo9DKGzi1FBySsxMxzjbCUncxvEUJmiwYZVYlyVMzWfKqdOoWCx3DLcLm2Y/opa7umdJGKz8RRy9hjHTOJc9yo0QK9XUfIt80SJ/ZJorT5xGrwa43RBrc0hukCAAbUY7xKJujLQlhUM1Ni938DYGOMKgDDiJxl3ro1ON8Gysio9TsNGDFHI21piPBeh+gre/mvWuymQy35eyUJXJZDKZ75jRhv4Dqwyf3BkVKZAQd2KiICGJNcOcRSwlJWNIEk0YKRqbQzxrFLxEmGLr0WvZlmTKNeQEFKTAE7CTanSqKS/keGqjwRQFCnI0Y5WaUd+pUKaUpKBY6nFl0GZtfYveZI+nCxcY60+yR9coyBxlBEfsMp7xcKfzVB2LxlpAvlTAbMQ4kUFY0FaKsnRYsG2ESNiQipwpkhd5klBQsepoyoz7e7i82OPJK4+z6Hjs3j6Eq8t4iWYoJVHVYbMECwenOH6xw6FEIHoGR2lEoFF2Ds+Hgi5hG5vIzlNRORqhpH/ZJV5ImTlaZWMYoLcjJoD5lYD4coen8uf4zJnPkOgERzh8qPy/UVodBUw31thPNThX2uB49AiLaoqm6HFDew+uckCI0SwfEF8tvjG7v4oxuwke3sA+30bGCilAbg0ZfH0Nd7aIVXFRnRijDZYrERUPkxrsqkfucD0rUpHJZL4vvaxQdfnyZb70pS8RxzFvfetbOXbs2Ks1rkwmk8m8TjwbqLpfXcZEatR3NzJowAIcY/D7BsOoPLkx0FCGQTtmwQJfgCsh0qNUlXcE+asP7irwHShJ6LVipm9ZYN4bsqAl8moQGBjNQMSc9ZeZj6bQgxBlpcT5ISW3xHplmT+nx9s7t5OPd9NLBbNJjV22hbPp0W1sY4zi8naPvcUSwtKksUfJ2OSEwEjYaznEbp8gKhEORj9EtScoeR7eqsdUoc3N0ThjYYWm30bmfabiAm5OUZVXmN1wWDaGZNzlxkIZVgY4qWG726MjHKy9Au+MxVhqg12hhEXRDOnECfmKxz1vWmAqSCFoY0/nqYeatBWxptdIdMLB2kHOts4SRkNKVgHLtTCJJmwN2HxohZ7skDLG5KqF3eshrCrm6hE0CNyZ/KjPV2tIZ+cKzXqb2WoRuy2xaz6qn5BsDim+cQ53rc/wyW2ElKT9GH9XBdWPQQiMMRhtsmCVyWS+77zkUPWVr3yF97znPQRBMHqibfOJT3yCH//xH3/VBpfJZDKZ1774cofBQxuYYTqaIuHZfk5gmVF/J8uY0YduwBHgYbASReHqB3shBM/2lvW0xjESIQ020FPQSjVtlRK0Yoq+RCSabgJ1qUlkirJCJmWB0ApYd7YJyy1ywzJVr0qutMl5c5KnbMFCK8dUfw+kHmXXxV0NKZHD9cBEgkQKnJzPdqwoaYEQsKMMZQvyliBUFhgYdwS7HYHrSaxeTL1TZCB24yBIcyH5qiIebJGsrOMwyVGVMk6PuRsOcWS6xP0nWrQ2hhhjaG4PuMAKN1sV5nQOgcQw6mt1JQ3ouy3SpS67EES+DTsR2hh0kDA7O4MjHb6+/nVSndIuD5iyS+gwRRtDYiLcFkzKCg8Xn2IhHDKZXE/RK6NSBcLBztk4OwGdP7vIcDDADkLsXEBbp4xThVaEdCTOVB4hBTLnYJW8a1UbVT8eFatIDINWiBDir63+Z7Rm+OijJKurOHNz5G+7DSGzxsKZTOb17SW/i/3Kr/wK73jHO1hdXaXRaPBTP/VT/OIv/uKrObZMJpPJvA6krQjz7Oac57oaqEbEtaBltMFRhpolcIUgJwQWo75PAJYRSDEKWq6EjURzOjJsxoqtKz1iY2G0ZMaSVKRNSdpomdIPJOfMkCSsMdM4wJ7OjRzeuIsbt9/A0Z230ogcHqDPWlDkUgKDNAWtaZoYGQksI7gsU8LYQvg+GwZCY6jaEiMEtqkijQWAz+gXaCdJEalCasm2IxHCQZU9nqieYWlwAkOZoltm1isxrSRztjM6NFojBFSn8igU9b5DTfkYBIFO0WiE0ERek3yuT/+hddLtABModDdGD1OCkw3s5RQpJO1hG2+zxh9tPsbJmR3cfRUGYwk7+S4bEx08XJy8x9m5FYbJaUzcxCJFuhL/cB3dS0ibIfRj/NhmbJhDhhCkQyITIYo29tSoSbFd8xCOIFkfIBwxCstXS+ObxIxK4P81ho8+SvvTn6b35b+k/elPM3z00e/0EsxkMpnvuZc8U3XixAkeeOABZmZGtYZ+/dd/nf/yX/4LjUaDsbGxV22AmUwmk3lts2seVsVDdSJgtOxvlK9Gi8sAjBj9yQgYSM3QKCxjEWmDYwlswDejaS5hDILRUjKtIbmazYyCtUtNCjMWrqOpGMBIkAZXOsRC4o7lmVoroI1i6qjP1rkAc97lTmsPO6rNutBcTg0lYZDDmJKnqOQMfT1A47DPn6CnUgaWxTAHS/2YvC1Qro2s5yDqUFMJReHgYCiplEiCsSSTsY1wPYKFiKfql9jVOgAdh3asKdkuKoHt5S6NxzeZiBU1C1QnpC4tcqqCry0EAkdKYhNxOX+OCweXeFf1MGbN4MwUSTaH6DBFFl2Gm10uPX6WS7OXsLfKTG8cQSjJfe464p1jjNsFBvdvUuvm6Vl9hoWYeN9+zP4byW/5KFWCtIQJFaNGXAqZpAg8vMjDkzkMhkjHtIdt3MdPEZ3YxJ6dpXD7QZJ2yEWW2BleZCGtUls3SEdi1zy00RzfPM5af43Z4iy3Tt2KvDoVmayuoqMY/9AhwjNnSFZXv5uXayaTybwqXnKo6na7jI+PX/t7Pp8nl8vR6XSyUJXJZDLfx9zFCqW3L7DxqVNXP5SPaGOIzGjGSQqIAaVgPdW0EkPRu9pjyozC1rUlYFJiNBgh0MLgXO1DpawUlOFC/xJa+kyoSVIpyVkWrlOnmVgMVlIAbOOgV/PYSYqVuCgSxsI6BSkpIkFptk3KRu0ynclzJAUbJ7iTyUizNb1JbUJzwJ+m1J1FxYqxuSL1mQIPf/YxZpXExmBEwDCJOG3O08kHzIV1qhNTjN2xQPHrU5w1FjfkHAqJIZURO06M2NxgoeuT8yxMqgkNCCSpyrGZaISjiY3mZGmTL97yCO/a98Ps944w3N4gWR+AFKP9SsKgjEKjOVg7yKVzTXRqSKodcoNpNjeavO3vvosTQGezQZzLcdP8DPPleW6+GnCeW61RDWP6XzuDDDVaatCMGh0LyBkf1U0JHn4cE55Gei7V97+f07sEnznzhyQqYbE6zQ9W3sqBPUdwFys8uvnoNwpoyNHs3O3TtwPgzM0hPZfwzBmk5+LMzX13L9hMJpN5FbysQhVf/OIXqVS+USpVa82Xv/xlTpw4ce229773va/c6DKZTCbzmiekoKENV3oJuyRIMZqWCgys5x0sW1KLFa1YU/MkXirJS2imBm1gygZbfGPtoGYUxNAaLaBmQ10btpVBOSkXik/RsmGsdzs1WcB26/SdHO0oRqWGqT1l0khRm85TrHtcOb2FwkUbgZUKhAYtNAKBdsucnl/hDdN38db4Ni4tnaFpTnK5ssOa7fIPbv4HYODMmYsUL1UYLyi8YUoUahAwVEMC9xIHL11BJX2c/q2EfzzDwf4biUXIkpviiAiVt1mjy6ybR+ITKY1vIDZgq1HJiIq0GGjDRa3Y8GzeHv193iLeglGGQEMUK7zFMu5OgI4Upm6zMt6gF/fI1W2crkVxMI3rOkxN17Esixtvu/OvPW/e3ioeoyWZurtK/6snkGoWIwvXlnMKBBKBSYPnzS6t1eWoSEb9IGfFWS4sbHFs710ArPVHBTQOVA/w0OoJPnfiBGq4lzsW6+Rvuw3geXuqMplM5vXuZYWqn/iJn3jBbf/sn/2za38WQqCU+s5HlclkMpnXlaWTTTZCzZgvyMvRUr9AQwhYQiAci1KiMZFmUgomPIvEGM6GhsQYZj0JYvT7I0UjsUBIhFDYhQC3HLEVb7JcOc3Tk/ciBTQKq+wTu/nB2t9n0HSYnsizcrpJOEioTuY5dOc0RhuCbkxnOyDNKTxPEjVjLGGhNdD0qbfmOVCcZ/fGELdjUe8cYSswPFE4xZ9u/SXxZZdyZ5JZO+K6pIhjSfycpKf6bE+tM5PkmGgfZLtSpdWfJ70coq08i4cX2Gxtc1aeJSx2cJSPU9yDFzkwSNAobKUJjKGZGFIBAbBpRZi+ReN0xIkrpzgoC0S9BA2sDVP23DhBrehQqbrcnXfZM1hn6vAU/SuGrc0mU9N13nb7HS/r/AkpqPydOwDD8HiADiTm6p44g8ESDtg5wjOnr80uzRYFjnQ42zqLIx1mi7PXXm+2OIsjHR5aPcFKKyHWDn+wfQWAN+wdo3DHHdcKVnQ+97msYEUmk3nde8mhSl8tdZvJZDKZjNGG1XMtlk42QWmGF9rkJCzHBk+MSlIYRyIjxWCgWC5YTM0Xsbsx+WFCnBo8pZmwDU9FKWfsFrtzCVDEaBB6tDPLExJXSi4dO8FZ7ymWOksYpdAIThTOMygr3jotsfqSaJhQncoztVhm13VjzO6vAqPAsPRMg83LXdJE0+ynJColcoYMrS65oMzW+iqtwQxh6pJrV7A7IRW5j9gkFGIbLynhVANibVj3G4wJj6eK55if3s11F2/GVBtUlMWqJ9BTPuvb0Fjtk88VmcxP0tCa6uY8F1YTGlaLiutgUkGkYZAYWgbyFZfQHlC2hozrCpv+FgxjEukS2oKqGe3BGnQi1g+usTZYZ1bM8nf3/d3RfqXZv+aEvQRCSpzpvTi7dzCJIr7QATOaqcIWOPtvxKvPXgtAt16dyXruvqlnPfvnz504Qawd7pi5jTMbfVZawbXrp/O/Hqb/la9jVBnhJPQfjim/6yj+3lpWkj2TybzuvGLNf7XW/Nmf/Rnvec97XqmXzGQymcxr1Nr5No/+2SW21rpUUs2iZVFxJUobLkeGsmWYx2BSQwhcaSusXWXu3Fuh/8gGqRnt21FSEcuAXm6VRVG/+uoGISQpo4IVolzAWSwx0Z4gVSmtqEU36mJJCykkn2j8/3jnrv+NO4pvojKeY2ZvhfWLHU4/tEF5zGd2f5XZ/VXWzrfp7ATc//gjNC93SETCwGqT1yWiVo5uP8btu2jtYGxBMbEY0qdT3sLfKRAFHnbOoej4NNwOVtll7+oUpA6yMkFBCYqWTTtfoTatyJddNnYatDYGuJ0pwhj6bode6nDF6ZNLyriOg7ZheneJg3dM025eJnqmh9sVjLWnUAUXx3cotCNINTlHoq6sc396H5drGy/Yr/RSPHcvlV3zcBcr10KMXfOQrsQIkGUX3Y9HG9+UwQQF/BvehrenipAC8dd8Xykkt0/fTjrYwx+du8jmU00qBZu5skv/oYdYf7iBWTc4HMTy8hgD0Ypi9Q/O0F0sM3nnDHMHs3CVyWReP77jUHX+/Hk+8YlP8MlPfpLt7W2SJHklxpXJZDKZ17BuI6TZ6TKgywR5MJKeBUUtmHQME47EF4JYG3wp8NE01/qslh2kgVSlRFpz3tqknQ8oOiGpuLoiQhi0MSgMsTCsGMHg/gKiVqDn9aj7dXLS50B/F1ObY/QCzYPWcTp7Oty1eCPmwn6euXcNlWikLdhZ7uHmHcpjPkfeMMOn9WOcsq6wu3sU3RXYS2P08z7LWlASgtB36YcWviOJVUixN0bsDmnNKcqlfXhdF7tSxq+N0dvpE0hJLvGgbLHrjgPUXJvymE9nJ2D5gVXCaoexXo1YG0QqcVKPRAuwNFppyuM5bnvXIvOH6/QeESyfV2z7MaXExp+tM/uGWZr3rhBuDEnLLkncIj9wObh31PB3rb/2ss5dfLlD/6F1TGIQjqAI1/pKuYsViozK5FsVl8Hj2xBeXZa5PqD1pxepvWcv/r7aS/peC4nkpthmmBjysc3EyQtc+Mpf0tGHqVp5lHQoGIk2o9lNNUjZOtlkqRkhpGDu4Ev7PplMJvO99m2FqiAI+OxnP8vv/u7vcv/99/PmN7+Zj370o/zIj/zIKz2+TCaTybwGlcd8lJsgmw6RlmgX6hqkhNQIlIZEGlwpUMYQGkDDyfNdXBnTdddRQZEtK6Gfa9HLbXBdOAnAQPYJpU2KQODSXYVSvIvDuXn0tMOg0uQHw5vZ2y2hU0UurLDOELUFj2w9wp6ZGJIy4/NFlk+3OPvIFoWKi+WM9uscqB/gjFxG9n3qQY1yMk6llCPAJ/EspC0p5g0Tuyc4fT4k6SYoX1EyKcFyGw+Her9CE2g4axRSj1AknNctFtwC99x517UZlpzvYzVy7FgbeE4RLy6gjEGmDqIgkUJQnRrNrq2ebbFxsonckZRSF2VgbX3IzuPbqE5MNVQ4aYgWDknN49yze5kKM4Tn24TnmiAE/oHqtdmkF5O2omt9pZL1AWkrwrt63zcXr5DPNNCRGm2SU6AaIeG5Nt6eyktq4NtrRVRdm/17q+ys9OksrxFGDnHRw1IJSB9jDFIKtDakCBKTMlzbYfuRIbP735jts8pkMq8LLytUPfLII/zu7/4u/+N//A/27dvHBz/4QR544AF++7d/m6NHj75aY8xkMpnMa8zM3gpHry+wFuwQBoZNT7NPldEKfDmq4GcMKGFop5odZRizBcKCzShmlR6p32WjcImVyhluVgeYFDOskzCwAgb2kFXR57r+YXLaQioL0xfsXrqFfq6JL3LkRZk1p8WYcdgrCxjtEa17hJUewhl9iNdKIy3J+HyRnZU+3UbIj935Y0Qnc7S3Uwp+CdZzxNsC/JS5m2pM7C5fm2m6sGoRV5uMDWYpd2N0qpDTZfTOkH6QctK/yK7hPEZXWR7YdO9dpxF+nsXDk+iiZmvXGUzYxPFDCKsknQVkmseKHdQAlEjZvtLj859/gJ0rQ2i5lAeSkicJhGB7M8BsjPYhVY1hdjqP8j32zNzI/MI8M/kZZs7OsPHIeexhgrQl8XKPyg+Ia7NP38yueQhb0DvXIlGGsJ+Q1+YFIcyu+aP+Vc/t4aw0aStk8MgjdD7zGXQUIz0XgMIdLyyOUR7zsRzJzkofy5FUZ8fon0/Y6jdQdgHblRSkxNeaUGlWkoRGnDJrxTj3nWZj6SSle26kcPvtWbjKZDKvaS85VN1www10u10+8IEP8MADD3DdddcB8JGPfORVG1wmk8lkXpuSpS6HenXqY5p+OyUWEhnLUYlwowm0wRaCRIErBHVgZ2VAZTzH9C15Hlw6xSqXGRvMcsfSu3mjmcfLuUCCpz0sAbHQJCgGSiAEaGmwtI2b5lgur7ErKDOuckgEWtts2QOqEuYqFerXz9FthMTDhI2L3Wsf6stjPra0edcN93CivUprY0DfjahN5UEILEfQ2hjQ3hiQK7n4rkd+a5wgTRiUU6SRhJtdjI6R7S436v2kkYWncqwXGvQCwfalS3wx/iMAJrslbkz34Zs6jpVjzVYMomdzikYg6XVD1p7aHPWHyivi7jzNwKD16H8ApZpPox0RdGMqXp78WpVqOI1bdll5Yot6JyQ1At+W6CB93uzTN3MXKwTLfVY2hkQC1p66iKWfYM+Rqec16UUYZM5G9xJQV5OVEESXuyTLXVRSoTc3T3tph/GnNzh42wuD2bPFQrqNkPKYz8zeMsNpiX9ik42wSjMs0Oz0iLd3cM2QfuQz4brMuT5OUCVejmh/9ssIIV40tGUymcxrxUsOVWfOnOH9738/b3/727NZqUwmk/k+l7YiSA2zRxfonW8TK43YCvCERksYKEOoBV1tKEtBThpQYDmCnfwqqUq4fvvNzHUOMi18pnwL7+qWqoLOsW33OVe4yMDZoJgewxcFDIqB3SGyhiSBy9PWFsPSKjfaB5jsVph1PcqTFRb2HMHbW2GtfJ7V3hqlwiQLejeV8dy1D/nPfr3yTIOty11c3yYOU1ZOtwl6McZAbTrP7PQU3UZAT3ZpDS3O5wcUk5BxZTE3KOMZl4v2OjI11HtVuv6Q6ekxzsQ9AN5i3YKrLS75K+yJ5lHVHir2cJSDYdT5WGsDSlBKKwyDAG2n2MZDGklpzKO1NmTQjbBsiV90CYcJzY0BUgpSO6FmK0quhz80JMME8g5W5VtFqtESv55r0cw5RNUuyxdW6F1a4xH5V8A3ik+odoxV9XGmC4RnWxhlEJbA9GOU9Encm1hfDehIj/ZOleL59gv2QD27L+q57X2Ld97JoTtBP7jO4IltxvSQ7a0AzwHT3mQ6FVjuboJhl9xYFZ24JKur3/lFm8lkMq+ilxyqLl68yCc/+Un++T//5wRBwD/8h/+QD37wg6MGjZlMJpP5vmLXPIQjSDaG5GoeaMNgJ6QvNUIrujKmgktZWmgMgTZgabacVbYfbLB3eAvV3jS2sck5gsQIBmqUqrbsFtt2ixVvk3unnuDAxAWuC+5ge7jDxdJTpEZRice4WBgyYVtc15oj9G3GnDrTh3bj76ny6OajfObMZ0h0giMd3nfofRyevI2HLjdZaQXM13Lcsb9+rSpgtxHS2hiw9EwDx7MAiIKUre4WjukwLmJkr8bZoMX1nkeFMpYGx9jsTaZZ9VssF5e4WL/CM2kDBJTcEltOkwl/FzeLY7gll/IU7ESKVEEjvdqA2EpRWjE0Q5wwj1Q2GoNKNb1mhONbaG0oVn0QkEYaL2cziAOCbkgfGLoxCzJPznOJBTQud3C3g2vVD795Bqk85o+KeJwfIocu0/Ys59QKa/21a/2jwlOb6F4dqGBVPFQ3wkQapEDmHaRVw/PyTC14dE2ZbiN8Xnj6mzy7NLCTFLCsBrnmRUJRo5cbJ69TCoUx4sShM/S58L8uUDj/h8y+523MHRrLqgJmMpnXnJccqubm5vjlX/5lfvmXf5m//Mu/5BOf+AR33303aZryyU9+kn/6T/8pBw8efDXHmslkMpnXCHexQt4YLlw+zabbxF4pU7dsUh2TxLBEFwRUbY9I2azlBmxWLtF015no7gEroSImrwYu0BiMHC0xk/5oeVzb73N0/AhSKs6bB1nuLbOrvIvz7fMU8vCmubuZvVDCa7lcqm2gAzDRMsfkPGv9NRKdcLD2jQp5atjkDx66Qpxo3KtFK96wd+zaTMrq2RZbl7u0N4cYA4Wqh2WFTKkiTuJgbEnDW8UoF4TBAIlIiWTC6cp5Hly4n5gEN3WZyk/xtvm3UXbLzC1Os5ddECrCyx2W9Q6xY5BWylaSEuV7aDvBm5X4F/KoCIwNWhviYUqu5KC1IYlSolaKtAQISCKFSGxsG1pDw6CUcPMtC2ydabPx6BaFinetOMdzZ5B0qjFrfWq9mGHo0JHQXU4p5qaYPTrL8NFHaX/60+goRngzpLvfDJGPbVuIWGO0QQ8DZM6C+hhdY11bWvlyXFsauFPH3XbJn91i/eQa/f4mtqxgTR1hqFxUcT+NKGX16W022w8ifuJNWVXATCbzmvNtVf+75557uOeee+h0OvzBH/wBn/jEJ/iP//E/cuzYMZ566qlXeoyZTCaTeY0RctR89zP2/0uSJhTlJDe5N6P7CYNE8HTxAq722Zo8D4e69OM+lzqXmO7spSrnKEQVKk5KSRqGOuUJO0DUXWaA+u3zrCR99rb3UNrM0/Da9KcVq/1VlrpL5OwcP3LgR/jxoz/Ol4PPo9c7HE330ZZdNt0mx4DZ4iyOdDh7tULeTGGGr517iJX4LAfGdtNuzF1rRKuN5vjmcZZZoXM0QZSLlN0St96+m9ZKyM7WKstRi9lwjLoqsmL3mUBS10WMMHS8mFCMk9uao105SyFfILc9hhXWue3oG5m5qcLahTZLJ57Bv5LSDkel23NGkuYi8ocSzIUc/koNV7gMTUwaaYQAaUm8vM1wMyAejkqb265kak+ZRiul34oZVrq43RKWdGmsDtDKvKA4x7MzSNpovv6Fr+M8GmFHkgVj409MsNyKmD23QI4K/eZ5dFLBO1hn9cwOw62IqmMT+RbjrkZ21zFJC2vYYPbo24in9lybEXu519DcwRpzB2sYPcPq2UPI2ZPULz5DaXWbnjJspZKSbeE4Du0wj9WO6ewELytU6TSl9alPEZ09i3fwINUf+zHCJ54gWF0FKTFav6xxZzKZzIv5jvpUVSoVfuZnfoaf+Zmf4YknnuATn/jEKzWuTCaTybwGGW2uLZe7EKyzMdygn/bZTB7hdPU0E+ki1e4MrvZRMqXlbNNsr9BP+hgMq+XzaDQ39Y4yGeaRgCM1g4mIUI8DksNvvoN9S0d4/H/eTzAISJyE0/4Sk7vuIVIR+yr7SHXKv7v/3+HZHv4CFAOfYT7m7v33AHDr1K0ArPXXmC3Ooo3mZO+LtESHhxsnWLDvYb62D4Djm8f5zJnPsDncZL2/zuzsLJP5SXZVfY45R/CXDIV+g+6gx2V5kScKp3hYKO6MbqAQVhgMqzQ3yyzKW4hTEO2I2bUbCJ08j1y6xOL1Y5w6fYVoZ5uD/SoFLVHSMNSa2A7o73QoJFPYnoPtWvgFhzQZ9bCSNoSDZFRO8apUaZrOFhM3F1FfFzjDOrYvOXjjLJO7y8TDhPWLnavFOQRr4jJnzz/MbHEWpRVLTy+zO6zTthImkgJyS8LQpRhIwu4m3UKJnHeA6Pw5QqtM7Do4eQcGMWHUw1m9D3ehgOr2SBs7MLXnO76u1s63eea+NVRSxpp+Iwd291CPtyg5DgoItCZyqxij6Xz9MQb2yrcs4z66TjXDRx6hd++99B94gO1tQeDVyN9/jvE/+p+IKEKPj8Pf/1GCxx/HfcMbvuOfIZPJfH/7jpv/Puumm27iN3/zN1+pl8tkMpnMa9DquRaX/vwKVphiK4d+WXGxcBGN5kzucc7NP8VUd5FSVKfnNdkpLYMymGfrcgvDeuUCR/U4QzPDJW+Tm4eHObpTo+MNgBLJUpeTj5+Glk3fWIxFBXKrDfbduMjf2//3+P2Tv89/f+a/o7TCkhZvX3g7+8b3MVucvRampJDXCi4AfO785yjl4I0LxzjXOsfNM4Y7Futoo7l/9X6udK/gSpdu2GOsZdEKJJ9fOUt65yI3veU65joR9/cf4kTrCRIVcmK4wTPmAjd33sb08AjdQkAlyFOKxplI6tTCKVzbp705ZNmRDMOATa+H36zgG8NQG3a0xk48Cs0FvDTPwsFJdpb72K41WvKnoFB1GXQigm567WfRSrMWrHCqeoa33PX3yHencFyL8fkSpZqHqbkMujECGExt84Xw/yVZHu0tm+8epNqeQiibemqhHI32XKzEUC3YEKTspDaT1XnI1ZjYW+bCVpH1QYojUko7TyNWniZZMfT23snl5QrpxjJe3ua2H15k/lD92jifG8C/1d4uGC1H3HxwDWelS3GyQCcxRNctsvPUNoNQMdTQUIAYvWbn7BI7T/0h/vU3UHzT3RhjSNfXcebmyN1yC8Fjj9H72n0sP3KRXkeRJJN0xvdgpIPUCXrtPmrdS6idHQB2/tv/H1sIcrfeSvDYY39j761MJpN5MS85VN1zzz1/42OEEHz5y1/+jgaUyWQymdcmow0bD67hrLVQMmRcaw7rvVwsPINEotFooVivXGCdCwDY2NjCJiF5zgvBltskEQl7ollc4+K5Pu3KNjVKHD/7MMvtLguUqdoWKjX0tWa2OAvAudY5lFbsruzmSvsKes3noLmDsvZhHKKlNnEz4CJLXK5sMFucYVdrkhvX99L0eyyOlXnz4gGSyx3OXTpFd3uLJk1yO2Pctf6jjA3miOyA4WbA/93+HG++aw//6Ja3s3WqS3erRzfuooxCGMG6WKIudlMdziBEjlx+H0flHpyOh2DUq8vL2eTJ4TfKbOqUcSHJW5JJS7IiDX7RgS2blTMtBODlHbTW2K6F41kYA0KC0aODZ9DU2tPIBx12prqM1WDnyoDlDZ+8LtJL+iRWSN7PISptEvGNvWUsF2lFFudkSNE4eNMOhdkq+qEtdloRBUdgDxJ6xqFXnmLP4d0cOyroNkLkyUfwN5Zx7noDwdmztKZuoBdaOEYR9GKWTjafF6pWz7W490snGIYBeT/Hm82x593/rLX717AudqgECfpiCzsvKNUXsBarbDzVJ8UCYWNLkGik6xI9eZZ4aZnB/fcjS0WsUhnpuYSnTxM++SRrKzFXzF5U2SK2CgijqLXP0aoeYH3ydnrFOSw5CqrDhx5m5fHHETkfPQwQjoPwPEpvextTv/QRpP2K/ftzJpP5W+wlv1N89atfZffu3bz73e/GcZxXc0yZTCaTeQ1aOrPD5dNXmEt8usYmb8d4wqD51ntSUlKUUi+4/UTuPBiYjvaw2+9xfXue/c0JGnPwaPAEV/I9Bu71FFOXrjtg4sjctVmoA7UDPLD2AFc6V5jsLjLZP8ilzW0sR2LWB+TWejT7DbaHy5ybu8BKd4W3tG7gJnOErtuhV4Oj/b10vrKEbnS421xHcWyc5nKJWn8aW7sMTRdJn3i4zBeWnuDobJmCU6DklOhEfYwZxZuV4jmkkOwyR9g7dSd79u7Baw9JOja2cilUPMzeArrvUJAxpcspe1IHyWhFXxoWaK0IpEhBWBQqHuMLBS6d2iLVmsSzKMU2FU8yiA0NA0ILVNOh2J5huCVoOQ20FxFNX6a2uZswHdKf3cRvVxhrejgT39hbNl+ap+EkDGxFkEp218YY9pNRqfuBQUjIKYPn26SWpNeKOHLXDHPAwJqmfdJD9fv4exZxF3Zhlkbn0xj45jmopy6dYrm9Qljp4LcrPHXJYv7Q3aPHP2cWKz7TxEoT4qSDJXJUug2qHZ99P3CMxs4J+u2IWEtcV+IP21SuPIQaDrF8n3hlBatWw52ZZXD6NMNTp0lbLXqVG1EFKA6WaFUPooVFq3qA0KuTWjla9SP4qkUJ6BR2MbZ9CtpXfxYAIej86Z/i7t7F2D/+x9/+fzSZTOb7xksOVb/2a7/G7/3e7/HZz36WD37wg3zoQx/i2LFjr+bYMplMJvMaoY3m03/xecaDOlO2oioselbM2cqpv/6JRjDd3UspqjPwO6yWzl2tnCc5kV+hVfdwE8XR1ixap4DDdZcUcXGF4JY9FPUcB6bneNvtd1xrSvtjh38MgLOts5TieWTHJqp28dolgo0BfmJolYfInuBA9zDhFclQRTRkRCFnsb2+zBOrDzK+liMwQ+qqxHjSI1B5lJ0gEws3yRPaIeQKOLZirb/GfGmeVNvEygAWhhRLWISTTbb9kxyYrfJMf5WYGD1TZyzaS+wZTi6vIrTPfuHxQ/YufOOxpVOqxqGITUsZtAzRsUXUVVw6OSQII4SykEswZQtsR6Itg6NhKwEtBUJLkBoM5ESetJUQWgNSEsYGs3RFh0K9yPsOve/a3rLpqb0c37lCFKR4OZtizaezOaQ6mWdrqUfXsmgmikKQMjmZf15Fv/xttwGQrK5iz85S7k7jba+jtaY2nWfhyGgW6tmS7OrE13H7FQrWDF3Rpe+1rt5veOory5x9ZBMhBfkgYTLWOMJH2GA1L9L4v7+Cd/AgN970BuKJ/SShwvEsvEYO+88k4VYe6fuYMMREEb2vfhU9GEAymhH19Qoyt0i/MIcXdSh3LzHIzwBgJQPi0uKoTxjQrB6ivn0a8ewSVQBjMGFI50//lHRrG4Sg+Ka7yd9+e7YkMJPJvKiXHKp+4Rd+gV/4hV/gwQcfvFZO/dChQ3zoQx/iAx/4AOVy+dUcZyaTyWS+h45vHud8+xxa34LSCRV8TjvnAfiB9p1sOU1O5M9jhHne82a6+7h15Z14aZ7ECbHmLDrjm6TKUHDzFHMpclkzlCEb6XnK3IJ/dptj4RozH8xx67ve/YKx2NLmx4/+OI9sPMLn1v6CNPZpXmiyUJ1nuzBg2Bni9DTaMqwOG+TlONpOqYYuUthckavYTUNR7UehwNgEVh/hlyiaIika7cR057Zg93mmisVr+7UOFt7CVu/PEXJAkPZ4b+fN7N+cI6hrhtMQq5ggdnice8G7H6UESrS5M76ZN27eTUW75IVgSjgExjCwUmRigZLoVJGmAbELw8KQcm+cHCARDISg4kqmKy5pCt1GgNKjY63yEVuz67hRnmKuyIZZp2eHmHLC3kOHuGF4gKOdXdjSw9lfRr5bXtvnZIzhmWYIQuDlbBzfouT7LByps+u6OkYbTj24fm1PVOGOO4BR+fmNp1bwCg5aafbcMH6tIt/w0Udp/Y//we7lC8gkz/L+3QxuL7N46DBGG5748hKPff4CSayZ9Cy0lLQFpInG668xc+oLhDolPnMGd8/TzPzMz1C4+46rZ3+O/pSg8Tu/Q9rt4U1MYKQgevwJSL+x76zaGS0/DfPj+FGLaucC7eIiq7NvIvKqIARBfpIy0Cvtol3ZR61z/vkXWhQRnTlLsryCcF3Cp59iXIhrxyCTyWSe62UvFL7rrru46667+M//+T/z2c9+lt/6rd/i3/ybf8Pa2loWrDKZTOZvqbX+GlfKJxnrzROkOS7aW/SKS/xw602U4goYm3rpEf5q4mvwnGA13z5EJZhEWTH5oMze/g2Ubw441zoHAg5UD7C2fpGBGbArnqENOOOL7Gt5TA0K33I8zxaYOOs9znWHbZI2bE0NeKxwlqmxCrWwyOTMHOPMoZ4p0wgMQ9Vkc3yZ86UVhBHMdOtYsaTldrm//gj5/Bxz+h0UnSK33XYrm5Vp1of7rwUqKSQ/uv/9bO7U6aWbvLVT452tWVwsTF9wvr3D5bELPGMdZ6wzRTmq0nU7rJa3KAcuY0mRgBTXFnRiwXoKzVQggTFLUJy8TOqtshNUafemiVEERmKEoWAMxrLYfecMu6cLLJ1s0GtFFKsewUyORphHnyxTVhVKqoY80GU8P0bl+Djb60v4OQvhSIpwrS8XjGaNAJ68eJKg26NeKnPDvv3MHaixdr7NiXtXUYl+Qb+rbiNEp4aFwzV2Vvq4eedaEYpkdZV0c4t8oKlGIJdjDt20j1unbh295pcuEAaaMUswC1gGhGOzlCh2dJ5ceQ9jZgshBGm3R7K6eu2cH988zlp9nfkf+wH2Dgu48/M0P/0ZwjQdrUG8SmBGIWl4BeF5GK2uBa3Ar9OsHWZQngbqWDok9OvQeZELLY4xnoeVz6OeM5ZMJpP5Zt/27svHHnuMv/qrv+LUqVMcO3Ys22eVyWQyf4vNFmcZTOxwXHzxWmW/o3qc0qBCFOeZMAVuaN7Kcr4xWuInwcJCIhBCYAkLAaQ65t7Ve5nMT1JxK1zpXuFM/jyxinlXdIwyM0wkY+RKM0h/hkc2Hrm2dO3ZYAOjmbOnd56mGTX5mvkCi/OLjNePMf7YJotByKW0Qy6/nzcfuYszuTNsbDRJCwlziwu8K6nx1aWv8NnBX1AMPBpulwvlFeaKhsdLX8SRDoG5QmlYYjY/zeHLinOf/1MCv87CLUf4mTe8k9V2yLGnmhS7ffpphB9L9u3U+ZHm23iLvIte7NCUXZZkG0sairjURA5f5DAGNm1NYDlYYUxda3ZNnSPZfR9GBAgt2Fk7RLh5K620xooRlF3J7A3jzL5pDmlL5g8/t8refh783AWWO028OYfysEK9M0v3Ykij1SQNFBys4seatBXhPee8CilYr1zgq/nPkfijCoETFY95eTvdRohK9Iv2uyqP+ViOvFq2/fmNf525OUyasJNU2Zi5C+P4qPMVNi506TZCwn4CWOQsiRSCrkqpxJBPhgRJxFDnqDZ2QAichXl2xCTrD6yxceFrrD/83ym0hpwby8MPvZ9bbruN/te+Bq5Hy5sj9Gr4YZNq/zKyVES6LqrZBDNa7FfrnKfWgVzYZNV9C6NQleKHzRe/8I3BDAYoy8KenED1erT/6I+y6oCZTOYFXlaoWltb45Of/CSf/OQn6Xa7/PiP/zgPPfQQR48efbXGl8lkMpnXgFunbuXfv/n/5N989RdZ1xfAwPhQgrGZMEVCe0igDdN6gaazgu/4CCNoTaxgq1sIBwl9t01rYgVlFHsre/nfD/7vfPz4x0lMwjOFC8yNTXI3kN89ia1LrHiGz5z5DIkefdgHrpVJX+uvUXSKvGHqTqLHHue2VZcDdp+1h1aIDIz7Bwg6AX907qtcmHsUZoa4lsv7yu9DG80fX/xjlvwlUj/FFS4LxQWmC9McrB3kobWvc3rlcXaZOTrNrxOc0jQ4ipY9Vlc0N/3I9dx16zy9ABqXeuRiiUDg47Bbj7OQGtpa0bBsLGxCuYdizhvtkaJA2AxBS1SiqNbzFDoBKtciljFubzd26Qp2YYdtb5Pdkx61sEBlb4UjP7IfaUuMNqyea/HUpVP0vRZ1b4z+ZY9hN6bfauAXHRzPQiWa/FQe51wbdbpBWsvR6kYsPWc5n5CCtf4aif5GhcC1/hrw1wen2f1VjDEMz7bJS6gLCM41CZ44jeo36O2/m81VSWhXKLopvdjjyjMNdh2uUbcEtiVx0GgjqZJCkpIMurhhi1zURNg2VqVCu7KflYebBIMdwnZIqXUrud4y5ZUWev0zDMcPU3jTm7hy/1lW5agghUAxCPfjFTyc9fOUdZNOZT+hXx8Frs4Fqt2L6HWHBgeZWX/w2izWi7KsUTGMffsInngCEydIzwXIlgJmMplrXnKo+uEf/mG+8pWv8M53vpNf//Vf593vfjd2VmY0k8lkvi9IIXnrwlu598e+xi98+f/gicZDrJaGnNEDDuxMkdgVOrqPLkUcqB3gbQtvoxk2ebr0NNu1U7R2Bmzbq6QTXfzUZ7Y4yx0zd4AYLeuSSDac0WyBrMxjOzab3kWS9IUf9mE0c+ZaLqWTy/zA09PM+jN4vSZOVOTK+B68vkXQPEtLTtHdCZnbXaKf9K+9hkBQ9sv04h4ICHXIIB1wtnWWhdYEd21cz6Q7jtoqE+k2lCuU+6sMB2PXZmwKb5hl+f4V/DhForCwRuOXUELi6QrCyzF9ZIxD5Qmmz5bpbQ0JHcUOPYKuRynV5KTBjsZwtIsuraKUi9XZzS5TY88wT9kBfaXHl37tEfqeje0KtjY69FSL1I05N7bOIgcZmyuzeamLVppBJ0IIQdQOqCqDpQ1pI+DKfaucVSHKSVh8e4l77rqT6cI0vbjHV5e/SskpUdie4NT2OqW6x3VvmqXXiiiP+czsrbB6tkV3JyAOUvxBQmF9gJ+z6H+1T9rtojZXCZVkK5kmnCgSRA6BsLCUw9blLrvqHofHPIZdjTbQjiMmgmV8EzDeXIfeBaqDK1j1GpX3vIf1R7fprzVJjEto1QkmxmnWrkfqiLnu4/S+di+TP/9ziLtXECfbVKItdqwZNkszSAuUdZBS+QiJU0JLG6lHhSxqnfNUuxdpANXuxecXqfgmIudj1WqjpYBxgn/oEOGZM9lSwEwm8zwvORV94QtfYGZmhqWlJT72sY/xsY997EUf99hjj71ig8tkMpnMa4vvePxf7/o/r/392dLYnZ2AMVmhNzbOXOkbTXiPbx5nrb9GJ+rwVys79GKficIEb5x9IwDzxXlO7pxEozmVuwjAE4Utpib387gWdIeGM+YMrnQp7kxyans0y3LLvlsASB+/j8lSFb86Tip3yAcB45cvM6hUsPpl6hWLk36bs61Vdpd3X+t1VXJLrPRWSHXKWG6Milvh+vHr2VPZQ76ncTZjLvmrLLh56rk6IlKEzhR2/hsV8aQtUYfrDB/ZpHi1RoLGIBB4UmIDu3MFxos2O8MdXFcRFRXHW2ts9hW7rTrzjsA2FjT3EWtD29tgI8zRbc2xp2CRsz1iBc4wxk40G6G++n000s6xKywjdQ67HBOkMY5nMbOvSjRMqE0XqJ/ZQRpDaoGIFW4Y0zY9RGLx0CMnqOy1Ec8phl5sTLJ9PiW2RiXqj71ljiN3jarmrZxu8uj/uky/FRIHir0lmzGlGZQ1lTDFi0KcpEni7UPHCaWiJPYLaGWYO1BluL7NlS81qAoXihJ/GFO1Aly3iCBPfSKHZB1ZncUMh6NldvYManKRxPURGJTlkggLRIl1cSuTzzxJ6bHHmHnPPWypE0SDeayBIe4NSKOY2KsS1a/DjzvUuufp52e/sX9KvLAR8QtIiUlSSBK8gwcJn3yS8MwZpOfizM39zc/PZDLfN15yqPp3/+7fvZrjyGQymczrkJBiVPjgYI2jzL7g/meX62mjOTJ25Hn7owB+9OCP8sTWE+wETSxKAPx250lKzQJFd5zEvpPddcONYi/piSKXku1rRRNuP3g7zbmE4doyuruK5VeRuw8jly/g1x2SQRNpnaO4W3L9xBu5e+7ua9/3n93wz/h/zv0/nGqeoupWmSpMcffc3dw+fTtDmqwtnWKmPYmTd8jlDKUgpi8UK/v6rBbPMWNuQwrJkb+zjydaEcnpHSoIBAKJQUmDzFtYLlx8/CSlIMemGeA6Lqvjp2h4NvubBSzj01WGkuXQ297HstqF5UhqOUPVK+KEFm6cYjDUbBizoKE0AsGkcdhtWYikSF66pPNFlpoR0TDBdi0KFZdQgWMMOjEIDJFWKJOSw8dq5bl0ahN3XlFyS9w6dSvrjw0ZhgH7Ds2/YB/V8qkmrY0hxhjiIGWQt8gHKSaK6VuagQwoiTGC2NA3knbHpTBm4+VtgvUdustNYnI4vkQISYwkEnmsXJlIh5RLE+h9Cwy7lxGP3UuxfZZ82mBK1tmavI1Q5sGMAq0wCjyfwK6TrK4yd9vtiB+5nm4jJBoknPyTJxgYF89EmCTBCJt+bgap4tH+Kcv6RmELy/rWF7gxWJUK5fe8m9oHPkBw+DDJ6uq1PVWZTCbzrCxUZTKZTOZVJ4W8FrCe647pO/jH1/1jfv/E/yQMRh9u07BKN024fXGK0+sH2eVOMxtbXEq2X1A0IX/zEZJNgx6MIQs5+uV51oIxlAInl2ducYY7brr5eUUuAO6YuYPbpm+7NpP23KAnhaDsldA6xSQambcJ9g/ZubzCUrTCl8+tI6Tg9unbR+Hl0DiXBilD1cffTqlYNjlhYWJNaBLKcYnxpEbX7mFFoEpdvj5/P3sGk+wOy5RsUAbW/R02xtZ5w8ydzBRmWD/fptUPqWpDRxkcIcgJQGsEkoIjkQL6FtSUhw5HS9u00kwt1rB9i52cjYwDZASpgIsqQqYOiUnRIbQfh73+BI4cNQgulqZQ7YSnz5wj7+co1eevHTPDaHJHSgnGsN4cMLANptLFWDmalS2OFMdRXUF+Jk8S+8wfqbH76Bibf/EAQdxhR7hgIhzHI1AGy5IsSInnuUSuS8N4DIZV0vrtlOwcsyuPMr7xIPlcQtebZ1MeJLbySKPxzZCil+LMzjJ89BEKq6tU5+bIve1WkqUlzj6yAWGIo5qMWW1EZxl3sIO2JGvjt+Lp3ugHe5Hm1Nf4PuX/j73/DrIsPc87wd937PUmva/M8l3VvrqrLRqeBgRBkSJBiSORGu1MiJJmd2M1GmkjdmNjdlYxWq1iYrURO9KKClISRZGEGYqEJUEADTSqXXVVV3X5zDLpb9rr3fHf/vFlZmVWVxsQDXQX+vwiMjLvvcfdc29GnOc87/u8v/AZev7W30JoWtxDFRMT85bETVExMTExMe8LUSQ5PVfF7jzH08U819w5AHqtQ0SmybWVBpapMVZMkgu0u4Ym2PsLFD5znKDqYhRtKitt9KWQHrNN3U9z8L5D3Df0ZgcN3lrohTUPzTawJ/O4N2rIKCJY7eKLgPxAL360sKe/K9ebQLdN6l6OqD9k0NJhswMBGJ7BcLsHE50URaQVMdozzH29R9k0AuYDScqU1COPjYEac4dOMzpq8nj6b3H19SX8jsQwNSxNEMoIJ/AhUul5rh8RmQlyUqfT8rm16bDhRpi2RhhIph7sw8nZzFSb4ER0ZUjdNzEBpMD0E2zUNunMbvDQww+RNtO0BtpMRxewVnrQQ53+sslo9CRCE0wc62F9rkF3o44e1UiHddq+RcXyaec6tIerHBl/iPBSlsiPKJgak/er+VXFxgD10+eoWH2sRzq4oepi8kNCU2BHEU2tjmybjFJnUwoMR6Ih6SbTyIODPPcrn2BtLWLhWg28kJHhLKP3f4Lu1as0v/lNhGGiD/STunaNA/kMmWMBjQ0fY7FE0Vui275JWQywOvgMUjPR9BDrHb6jwrIQQtA5cyZO+4uJiXlb3rWoKhaLiLvUH+fzeQ4fPsw//sf/mE9/+tPv6cHFxMTExPz0cnquwn9+dR7PjzCNET697z6oXua3nzuErhks1x3GiklOTvbsdP1sD60dOVgAVPmhvb+wExOeCyTWQC8tv4hlauT6ku94HDKSeHP1HWEm8iaVoIp7YwXbTjB4aJzV6hzfb55lMdhgwB7c6c0Cdo6lPV0lVWphuD5hINFSJiKQWL4kNEOaUmIYAb4ZMto8RE80QA2NahdcXbIilyh3yry0/CIicOnXpgjIMxMFZLHp0sXprmPLBIGmU5EmkSfoyacJENRdgdDUOXE6Pu26R64vSWUlQTXqgqmheRrbnV+Jdp4wijjb/j7BDPzcxC+TywhCQnr9QZpOh4XTDUp9NVXieaiI+AXB6rdeRKtfYORwH8vT81zP5Ll+n+TIkXE+cugEV9urlEttekfSDO/PIyNJNXeQ9P0L9F5doyl6kULgS5tQ6JSDCAR4cpOBSkBNS6FpDkbQoJrvx0+kKBzYT/app8gCB7k9s+rll08x/Gffxt5soufzhM0mwXIJY2CAnG0x8fnP071m0Py2izaQQ49yRJqJEXRpp0ewAMlb91YJXce9NUtQUiI6+eijVP/wD3FnZrAPH6b4G7+BFod2xcTE8EOIqn/1r/7VXZ+v1WqcPXuWz372s3z5y1/mF3/xF9+rY4uJiYmJ+SlmqdrF8yOODue4ttIgmzShCk9M9d519uHuobVvxbbAuVN8vR3eXJ3WqytIXyJMwdLBOs8XfkCqY9FJefT1jHE+PM+GuYkfBjzU/9BOqSDc7itrNz26VQdRtIlqHtIN0TWBtHR8JH7UpWG1qCVbDEeHySf6iVwDx/MxsJioHCMzbGDfCjlUG8eQEisVMBN0WI5aYIf09ZURm0WEXqRop0maJgE2TV2ocsVIlf8FTsTchU3stEnogxYJImfbZdGQWgiRwBceK1aIV+/wtSuX+eyx49idDE2nQ1hsYQdjO6WW2++zUBugNtvEu17GoMXSkGCzmKO+ucJI8wDBbJbQj1idbdA3ribqXj5VIsweInmkxrjdhMo61zYKdMigh3WCKIEVdJiI5rDHHsRdfYNc8zoSjZY9QFhP8MZ3FjCTBvm+JMuZ63zp+peYujqH6VfpT6ew6nVEwkbLZm8n9JVKEBXQMsewDY9eTCphglUtgzBUT1U9N0VveRpRKCDbbXBddZp0Hb2nh+TRozjT03hLSzT+4ls0vv41kNB+8SUAen/zN9/xOxYTE/PTz7sWVb/1W7/1tq8//PDD/PN//s9jURUTExMT864YKyaxTG2nzG+kkGCj9M7rvR07wRk/xDpB1UX6EnM4jb/SprFRZa64yuH9h5mrLlCtdQlkwBPDTzBTnSFrZff0Z21jFG2EKQi7Pp1iQMt2sYtJsn1jXF+4ykZ7gcQBm4rZ4ajoQyxkqXptDEvHsCEpMxjLeQ7Xh8jLHlZknazZJChuUA19stU+lvReUjmdcUymkjaGboAfMjhRwD3WQ7PqgpRsLDTpNHzcrooltDMGyaxFbbVNGIAW6URaSC7ncrI2gpv2KckeUtEhPnJfivl6A9sfo+CBduU12vrQTvnbdkCDv7zMVeZYzM3txN6vrVYo+OnbvW+bXaprHWprHXpHM7iyQP6Rw1jrc9z48xWEhFDPo5uSsf0jTN28RXXhWwS1a0QE1HoOs9F7gmjFxv32IinTJ6k5BOML+JMehcnDtC5tUCgH2JaFOTBI5Hm0X3kFY3AAc2SE9rk3kF0PWVvAyo9RDFwqRGQ668BR3FQvorPL0RRiu3kM2W7TvXqVqNWi/pWv0r10CbpdRDJJWKvROvXiTr9VTEzMh5v3zLP+7Gc/yz/7Z//snReMiYmJiYkBTk72AMqxGismeWQ0y59f+ckfx7YY8lfaCFOQG+jBbJtMV6Zp+S1s3abpNZmuTGPp1p7Sv91Yk3kywPXZq3y7/iJzhVVM3eTzRz7PwH29fG/6OzuDjCcPDWBHA1z6wTJOyyeIwMro5O0CgW7Qlm36/BRNrU4j2yJTHiDSIkSksz6wzlSjFyJBNYwoWqC7ARO9eQrjWUqrHSrLbWQkcVo+uqmRylr4bohmaERRhJQwmrIZS+9no9VP2Da42tfDaDGJntRobi4QTrcZn1shudCmdkUVWN4Z1NCb7MPUlpmpzmBqJoNDPQSbt3vfvG7A+lyDTsOj26xQGFSR9A25j9yYS7Lj0nBMRo7288m/fYT6F1ssnP42G/1HsFNpmql+Qt3C1kLaXRetvoIbReRW1xiPmpzeDw8d7GVf16Gm76PdMkmbPr1+meTDD6ukwrkrwD603CiEPsnyTVKZKcJcAQNI0AHbRtZqEEVKUEUqul4CWipFsLqKt7QE3S5EEbLVAl0n2Nigc+ZMHGARExPz3okq13WxrHdq+YyJiYmJiVFomuDJ/b07j33ff1+OY1sMbfdU3b/vMJ/f0Hhx+UUubFzYmbPV19vHrxz6lT2lf7vZ7u+6Ga0zt7i6Z2jxLx5QVRylZol9tUG0qwHr5kUGf6ZA7abk/MYl1oo3SRpJnMoBWp5GRuhcylzEEsNMGD2UBmex6hlczaFFQARkBfhBhLvWwdjsQk+ClK5R1KCtQSJjEAifpt9ClzpRKNB01UNkhZJU22JkdAKt6zM5XsRJzfFvL/wOM60Znljv0LOhkzvxSXqXWzvDbjtnzlD7wheIXI9By+Rv/MwjLB3MMZIZ4dH+R1ntaeyUX9Y3u1gJg/H7eiiXWgxO5nZKMu3BPgw/IrM1D8t54xxLry+y4T5MOZ1Ft7tIzcYwE4SZIlojwIsM0jmLvnbIJ8z7Wdh3gIn9NwnLDZbCKdrtEOwkw91pjq9voqXTiHCToF5F+gZRt4oTShJmHhyXiCEK7gqyWr0dsb71W8tm0SyTsNFQj9NpQtfdKQ/UMhlEMhEPAY6JiQHeQ1H1u7/7uzz88MPv1eZiYmJiYmJ+ItwZdgFqvlapVeLc+jlafouW12KxuYgmtLuW/u1mJDOCqd12umbrs5xdO8uJwRM82DnE/MXLVNfXyUnBzPAbXJya54x9hrSZpurUyPXNkXQydBNdlrO3eCb6RfqyvXSrXZqijSGSNHWLddPCcEPafsRI0kB0A9ykQV4X7JvM0TTblN1Nqt0aaBKRD8h1xiAAzRB0wgjXi0isdMkPJin3b/L1W99gvjGPF3qs56ApHaqXz9M/eGxn2K2/vEzkejt9Swe6GU4c/KWd939n+eW2Y1UYSDFxvBehibv2vs38+zVuuWO0zRyhFCTtFaJiP2PjffQ/dID2zCz+uWWS7Qb9dpPisc/wyMGTtCunufD6KbquSaBZ+FGCZbGP3GtnmervxRgcIGzcwC/NUc0doDT0DFI30XQwAdls3BZUoJwq0wRNI+o6hJsbgHKvhK4jTRNhb31b/CAeAhwTEwP8EKLqH/2jf3TX5+v1Oq+//jozMzO88MIL79mBxcTExMTEvJ+MZEbwQ5+6Uydn5zA0Y0+U+lux7WS9uPwiFzcvMlufZbml3Ixj1QnCuovlm2TCJIdKIyyl13bSdSNCKj1LdAOPCB8dm+EDvXxk6H6++brPTGUWPdiHU8+w0g3QJGiGRi6Q5HRBvVxi0arTOA5mapD186v4BJjSRHQkeiEgEaboOh6bUUSmX6MQ2Gzmm3yt9Wesd9dpeS26QZcLYxBIwUf1PI9/4vM7vVTm6CiabeFMT6PZ1tuKircKDrlb71s30UMYlEk1Z3FSU/hRgXwQMTZmsv/pYXh6mM791puG76Yee4yBlZC5Fxv4VZf+yCOdytDqHOLmjZDe5/46Nn9CUKngJHqIhE4u2KRp9qsdBwG7JBVaIoH94ANEzRbJw4fxGw0S+6dAaHQvXcK9dg0ZqcTC5IMPxkOAY2JigB9CVJ07d+6uz+dyOT796U/zJ3/yJ0xNTb1nBxYTExMTE/N+cmLwBJ/Z/xm+MfsNDM1gMDX4lv1Uu9mef1VqlZhrzHGocIjTq6f52s2vYdmfIYOFCAJaZhepw6PmQ3iOT6JuUEk0WCvWWOlUaPtdHul9in/yzN/FMkyKrSPol/McHcqyKCr0GwmODmVI5WzspM700mucq/yAdbNC7YbgxNLPk60PQAhds8167gZjJ9KMZNKcfu0GZjnPpVbIeHEMfbCBF3mMZcaYrkwjkYTAxXFJLdPgkXHB6I06jbJDtniQwuc/T1Aq7RE3d+OHCQ7pO3GM+XNreH5AmiaZ8nWKa8usbdpUaHPiFz95194loWns/8UnqZRfZOXlOqPJFEIzCBNH2Kg5rPxghdHZFjkgRQdNBjTMfrRIhXiInoOYRpaoUyEoXyfIJun92MfwLl0ibLUwkgkyzz1H+uRJqn/yJ9Q8D6OQJ6jVSRw/FodUxMTEAD+EqHr++ed/nMcRExMTExPzgUITGr9x329wpOcIpVaJkczIW/ZT3Y3tMsDTq6cptUpIJH+Q/DK/deiXSJ9PYvkWDeEyu9HiY52nKZgJElqSVr/gBf00AE+PPI0uBO3Tp5k6N81kReNqdIBu7yy1/RJz6jAPDp5AExp/fOp3uFotMx4eYGotz1Q3jYvJBiG2n8LQdG5UrlO6Wqfu1CmMR3REk/WhNjLbZqVUom8jy3OdR1jS15jJLICAIAqYm16nejNF6Efopsb9zx1m9Ikn3tPzPXKwgPfgMPVXNomW3yDyPbz8MbqdJq+/eInwgUf29OBtI6OIzpkz9L/yn7Cqg+j9D7Ap06StBMKPaG3WabYkWd8n17jMqOPgZAYx3TIVDhAd/iyRH2J6NSINnGQdI58n9eu//iZXzBobwxoaJHJcNNPAm52lffp0PBg4JibmveupiomJiYmJ+Wlj23X6q7AtwL5642s0HJ+svI/V5gKzzXmOB6PIQGIEINbrVLBgfIxjFKjPVam1JW6qxZeaX0J/Y5qhvzzPgOPyGR/O9lU41X+D+S6sTJ8HVA/Yvs59dEs9jPoZHowGSZEgMjSQgjVCpsQhhi5o9HppaqHPueoKq0cXWTGvkWlkGK/280z5IbJenq7n8e3sGU4NvISMUtRXk2T86HZU+tbsqndie0jvblGqCW1HCO0WLf5Cg7xRxO4boqsVcbQ0hoxIAEkzYLHSYcwXdGZqpDToO9KDvT+/E5zhzc2RdDZw+o6TNm0iCY3IxNcSJJ0ystNBRBHFzjxUZyj3HgHASQ1S9iJ67TxGshcj6WONjd3VFdsWV60fnMK5eGHPYOA4ATAm5sNNLKpiYmJiYmJ+DGwLsiulBqday5Qb15DCobvexo08Soky/U6eHq1ATTZgo0tNc5ivOqTDUdJJjenmea5XLzLguCSOHqVneprJcIMLKbEnXRBgIBhlVtbQrA56V6McefQKm6QAiWS0YnE8yqBrglHNJAz62Wil6S6HZOQoAw2NnJfH9VL0+DmeLH+MZO1BpN3Lgm2Tsn3YikrP9SZ23qeMJKUbtT19U0JTPWJn187yxekvqih5DLTz1zjQzRA2m3TPn0d6Ppq9lRys70MGEnt/P063jh7qNFprWKl++rou/a/MsNHMkHBC6pqApRZ9jNP6wSnc2Tm0XI71dkCt3SbScrhRQM2Hntochc4CaJqKSncckBI3UQQg0EzSlk4nsnEKUxy6fxJvcRHgTQ6U0DTSJ0/iLy/jzc7eHjIcJwDGxHzoiUVVTExMTEzMj5FUdIiB6GP05Nvcqt9iJdnkoY7OoFvAEz4XouvUhCSlj9NtBZQ7GgiTjBtxXO5jU3qU/avob7xCRwto9A5hCGNnNtR2n1cnWUcYAt0vIhFkjQjfcOn4OmZkMRpYmJpGCJgIRkWC/uWDmE4S33QQFiTNPAWZINQlNCxGghyaZdC2QoLjFlOH+/YETgCUbtS49MLyTmkgqARAgFKrhB/5HC4epvnqKwRnv07T6idYX0eYJuknn9wRJcmHDiNMgfTzGMUQtxWQMkaIPJd9boXkrA6WiW3qNDQNv+vTOXcV5+IFwnKZDW2Q0sizdDWdruOh45NprzNSOgWdtkr025o/BWA7VVrAvBeRDiO6UYjuCgb//M9pn3oRc3iI/n/wD0jfpczxhwnriIn5wBJFsPAS1BagMAETT6ubDzF/JWJRFRMTExMT82NkvCdNv3UMrxHRr2e5mbrBeaeEbYTcEiVWGgkMzWLB8Sgakmw2RbYmGTMAUaCt2czen2S98y1qRYvNvioPDzxM1sru6fOaPDLAi6UXWdzostkdZKwxhgizNGSCZMoilAASIVSCuCfhcHuMFDpV2iyJGhuDglRVY6PsUvEEiAgvijBCk95CgqNPDFG6UePaq6tke2yQMPPaGq2qy9iRAuXl9p7SwO2+spnqDPdVPVKRQeLIEVrVKjLw94iS3fPC0k+NUC61qL18hc7sVfRiHpwARwSYgUZChpjJFLJTQqQzpJ96iuXrHjKZZkDbYCOISHfXGVo9TSFcA0DYtkr58zwA8o1ZykAlCCkHGkJKRkIXWakQNBqElQrNH5y6q6jaLgO8s+cqJuaeYuElOPN7ELhgbI0JmHz2/T2me5hYVMXExMTExPwYOTnZA8BStUu22Uvl0hgt2eVGtMKKFtBrZVm15+hp95E0EgSaz1DSBN1gMaEz5EFnsMjVgSEOFw+zUp0ha2X5pV2zoaJIEnSnGBt7lvWBC2Q6PQTrWbSWTWrdAkdnXgakDA3bEDgyJIwExy2DCMFAlEFzdG4sdjCkRxQKLMtGeAIjkohMlyha4+U/hfX5Jqat47shAGEgaVUclqZrZIr2ntLAbcFXapUYc2pkrz5P8/nn0bJZMp/+NOWwSDfRg8wdJAV75oWNHigwfXOatYtdetseRt7ESvajJROkhtP0nhzCudokauXpLK2QspIUewcwGWZYRPSEyyTnFpDdLgDSdffMoxJbQeqpzgb4IQidTHNBvRgEyG6X7mun7xpEsV0GGBNzT1NbUIJq8H5YvQg3vr3XtYLYyfohiEVVTExMTEzMjxFNEzupdVdfXiHUCxw4MkZwNaDkz9MMG+RaAzTMMtXeBcykhieTPOEc54EwhZ036N0/wisd800lf9ucnqvwR6cX8fxB8u1PMuQZFCwDTwY4aZ8WbcLIZ95uo+kQdm1G+peRyTJ0e0iUD5L3E5jtBGlNo4XPBj4yGdDq2SDsbZO9OILlBRDo2Ed6aFVdEDB5fx9L01WKQymOPDG0pzRwd9BHa/MVyuJ5IqCmDTLfmqTqZ7E8g7VTpZ349W1KN2rMbuRo9D9CW/gspSQH7hvgqUdHsCbzeHN1uhfrYI4R9k2idxoMez7YFgidxCMfRWvcxHn9HJGpI10HItDv+HzS3TWiUEeLAlJORT0pBMgIb2WV6h//sVouFlExH3TuLOcbfxIWX1GPc+MgJNSXbgukwoRyqNYugdeExTOw8AqEPjzwqzB4HM78B2iWbj938u/FwuotiEVVTExMTEzMT4hcbwLd1NhcarGvOMFy4SLXq1fYpx1kTVuh0bfKxyY+xkx5hgO1/RwIhkkOpTnw8BCfL2tvGe2+VO3i+RFHh3OsvrFJx5ccmMpz/cw6HaeLkdtkvJslNAS2mSDIL+EMfg+kjx6ZJIDi2mEGTTDQCYQkNDxu9lW5sP87fKr0UQ44adKmJPJhc6GJnVMBE+XlFpmizZEnhvaIItgbYqFdWSeZzuIefY65OZ3mTRcPGJzKAbwpUbBRdkgYGsuDBZx6QC5n0pO2d14Pqi5Rs03H7RIUDhJZPfiaTQedjBSs3nDJ738Srs9Qyo6CkyLVLVOo3UTbNe53eOVlPKuHlNYlL8pgWRCGIATCMAjW1uMgipgPNtti6sa3Yeks2DkwbeU+Lb2m3CinAU4NUCEy7P84HPgEjJ6AjWtgJKG2CG4duhW48CXoPQsr55WgChy4+GUYekAJstjBehOxqIqJiYmJifkJse3ibCfl9WQ+wZeul3GiNaTXJkuWmeoMmcog1bU013UXvekjhtM8fvito93HikksU+PaSoN8UhB0Wpx7qQFdnZCIvmoe2zZYym1yIMzTtjYJ8LCb44TZJXy7TFHXiIDNMCKnQTqQzNtNllolmt0NpBylLkL6EzbDwyn6PjoGEppV903hFdvsDrGQjQJj5hCt+U1cbQp0g9CJWJtt0D+R3VM2CEqA9ucS0IBk1uCobTCw4dCqrZABjKKNnssgakOEUUTUXico7CMR6ei6i2clCaaOUx4/zqp4lFDYCAmjwQb9TgWjeQOA3oSJYZuYAxNYB3+e7tkX8FevoidsItdFBj7G8DDt06f39FDFc6liPjBs90Zt3oD2Buz/GLgNWL9yu7zv4pegtaFcWLepHKvls+pxIrcluqpKeBk2NFegva6e9ztgJKBegld/F77//4L1a5DqhX41miDuxYpFVUxMTExMzE+M7RK3bUdmWD6G0ASlVomh9BACwUp7BdPtg83Mu54Ltbtvq74yTWWtjOElEVLD72nR6Np0LZ2eyCZt24QUaUodP7uEjAyanQKDCDQhyQuIgjZG6xbHUjoVDrNcWKPt1xk2UhQLGXo/Pk5FQqPy5hj13TTKDuHOfCuJHPsovd0Kt65ncB2BlTQwbI2BydybRNluAZqtdEmUu5jDGfyVNkHVJfXIAIVfO0n9977BeqlMPYjIOA2S2Rzkc3hDRfoeG2PutYdxGv2IKCKfzFGwhiF0Ef4BAMzRJzCsIlqmn7DiYgw+ri40vVX0nh6yn/kF1tYi1r/zEomwQb99CojLAWM+AGw7VBe+AI0SDByD2e/B4quQKKhlGitQuaXcp6ADUQhI9XjhFbBSkBtR4qswpvoOO2WIAtAMyAyo9b22cqtmvq5ekyE4FehWoWcydquIRVVMTExMTMz7xlsNF16OqlxaWmbzLnOh7oqQiOQNlqovs7bqkNIGSQx6sJwmaEsWsxXMAxYPDR2g1xskWgy52VhmxDWwOgOYlSN0iaj4AUEEjbBCNwjRyxkecJ+gml3mVO4yDw45VEYc2GwRXM4S+vJNMeq72V3uqJs6/Y8fR0YSa+0GjtdFSsj1JNl3vPdNomxbgI5EEfVvnsZZbRJWkxj9vRhFG6EJkod6Gf3cQTq/86ckpUaifpXE2P1EB+6n78QYo4eLXBEJhBBEukGfZZDWQ1pSw7DSakeGjW/amFaKSBMYfgK9MIJs1Mh99hfonvwsV//4NK7Xj5mbgvYbZOJywJgPAtsOVaME9UUliNL94DsQdGH5HOiGEklhqMTQDhKkr8r9NuogNOhUoTgOibxyplpr4HdBN9kpGwwctS6ossDWOpz/IyW6fvafq/19SPnwvvOYmJiYmJgPKHeWCd6ttG43Z1bP8i9f/V8ptRfod8c4EiXxWgm0tEtyWNAZXCb96IMcO/gEF8++BgseI84Jqs0ma75H0qgTdJNs+F0SoYbwaqBpdBINeruH6A8GaXYrPJ87TzK4ROHGPg61HuXQgfG3ddL2uE1Fm3x1hunXNkiJLL0P9lFeaTM49WaXajedM2doffdLSD8PZo5V/QTlU5KemzX6DJ366QZWdpj9A2t0XrmE7q+QqF8nf9Di1VtThP4GqY5HKtNPjy4xNZ0eK0ngNgDwNAtpFBBo+FGKyOiQPTqBN7eJns3SrLpIK0nebFNvQNvTcG/N3jUVMCbmJ8K2Q3X+C7B+FcwMeF1orIJhQRhAsgjtTdVbFQZK7ES6cpjuhozArcJaCwwTNBNCD4QOaBB5W3Pe5B0rhreFVXESnvqHP973/gEmFlUxMTExMTEfMO4sE3wrtoMgXnp1Ea+aws+ZlNJrJEYusd84QCdZpzbUwTIsRrMjnF07yxdbX2awL0+Pk0UeNHhevsihucNMLh0l75qU/TauVUZaBaxoDCMwkUZAvl4ge7GfQbvNxWQTt+68o5O2+320T5+m9sUvojlZQg7QRZLRNXoqV2i/VqaWP7SnP2vbufKXl5GuhzU5ztKixuJNl6pZpn6hjExomDJJaB6hvNogBSQPHyZstZg+P83zlescKV2hvxFRCI9ialO4QZdkIgubV4HHqZbn8QpJMt4mnWSRTFjFnruIZpqYeoVc9RRWuo/u8H0YlQ1SK2t4516nNnMe5D+86xyrmJh3TRjAq78Dt56HZB4e+Vsw+dztUroogvlTcPO7Ss8c+LhK67v0J6p/qrlyu6TPqanSVaFBd1P93XaUkPKcd3EwQrlXQQhaBJF/e5tGUrlWRkq5YPIOgeV34ObzsaiKiYmJiYmJuffYDoIQq1mOdB8BrcNSdo5OX5e/9szH0cTexMCv3vwqvvRJHejhQnWGj459lP+h/n+kemsBDAs9THJFhMzpowwlTPo8AxC0hYb0Q9Itn+w3XqLvI1P07fs44VKE5/hc+v4Sc5c22Xe8l9FDxTeV8kUyYubKKfzyHOahwxSWN2lXLbLuOon5GW5eHGJp2EXkCm8qJzRHRxGJYbwVE9voYRSdpObiezoyMNDGsrAC0dgxLHGLoNlET9ispXtIXp+haGrMTY4wUpomaSfVHftGjWDjGvA4sn6Tsj3ARlugN+oUpySZRz6GZdZIdX9AqrWKDIbYNE6guStkNl/D3pfGXV/Hv/B9iEVVzI/Ca/8OTv0v4NTV4+Wz8Iv/H9j3rHKjZv4CLn4R2hXlGl35U1WO59RRJXlC9T5FwZbQ0cFMgm6BlbktsqqzbxZCb2LrNRlB6LBT8hdFELrqb7+zte3UVplhB4gATZUDfoiJRVVMTExMTMw9ynYQxNjEMPVrHgPdRwlyI/z1/Z/i8aHH0cTe0rSRzAimdnve1Vh2jIONCRakx2zQoohOkTQJL8OUpqFrAiuVYM3zqWhdjOIBvGaSI+UhvFaS8mJdDQEWAjulszHfRPyCeFN/1dm1s7zgX+JwUMG/UsLPHsSKTMp+jt59j9Ca3yRodxg+NsbmUotzVzd5udlmrJjk8UdPkJoPcG91EJ0ArRkRtiPcKISEhiw7kLToefYJiqmJnYS+np4puos1ardgoLxElI1wG5fBFdAu0wnVTKpitIGx9jJdPUcibJDxI6LDv4BfnaFTnSPV16VYuoW48Qa+n8GvObSNBFYSzPRblFLFxLxb1q8ooQJKzNRL8Prvw43vQuksbFyH1urt5auzyi2ysuDVUUJIbA22lkCkxI0MlKMUehDJrbK/LRG2I6y2RNO2KHuT4NpeT9v1WqREmpQ7q4OmygUH738PT8y9RyyqYmJiYmJi7lG2gyCS7YiDg8McPHCI/cd6OTnZgybenMa3Pd9qt3vldxrYWoZeLcSLQoIwwQFDo08TuKZGMmWjaYJaW9B2CmAWsespfByCINq6lpNEkcTt+Lxx6wpntM2d7W+7ZYv7swynn6Z6CdJGkdGRftYudqnNr5GxfWrpFJtLLTarDrPlBu5VwTlL0Hh4mANdSeQIkn6IwCGdEiQbt8iMHEEMDpEcSjPyzAiaMb7zXk9GEn7tZ6iMFxhsV5h68BAyirj1n/4IOdPGTinhl37gfqzLlwnrs5j9/UhHo/H1r2OkdbRyHQ628d00USRIHR6gc8XBzEWI+/p5rXiYxK2yOt93ST+MiXlHBo4pByrqgqarsr+VN2D59a0UvrsI96CrRFMUgZ3Z6pmyVYqfU1cOklvbWni3gNr6W7e3nCcBRG8hqHYT7d2GpkNuGLyWmlVFpMTb3Asw+4Jy2T6EvYaxqIqJiYmJiblHuVugxd2izbe5W9qgNZnHeqifRstlueZSNCIGDA0LjZQPmq7B0VH0eojlBGAY+Jj4bqCqibaus0Jf0ux2mF64TMW8QaY8yILrMJweRogeCtVJLqZb6A8bHF/voyFzJCbG6e3NYvT3UQz6aVYc2i2XTMunR2h0TFhcu0G1WaEQRSSiiIJ7k/2ZdbSUTeHJZ0ifPLLn/eweODxWtLn/UB9ByUPTNVJPnOShp56k8gd/QPUv/kKtYBpkPvpRnIsXwTTB9xCGSeLhJ3BeqeMHZcy+BNpagFu3MKfuo/3gJN9MTHJ1fRyzNg/Ak/t736uPNebDwHbYhJmBqY+qIb2aDlYahAGRo3qYwuAuK0vQLNCkKvXDUf+HqaKKQF+7xI6DtY3Qt9wlAXpiS6xtO1zvhm1Xy4Spj6geq8XTyuUSW6V/m9fhhX8JH9U+lHOrYlEVExMTExNzj/JuAy3ecRsfGYXBFO50mf6bVcyqCwkLretj9SfgSJHkzTq1tQ6BHyGjCKEJNE2iWzq6JtBNHddqY67neLB2hO5ymg3a1MwSAsFUzyOEKZfxkxn6e8eplNr0Hp4kM5Lh8ovLbLZKtNY9oraGLgX4EZqtobkhrgeyc4s1mQe/QW9/AmN4GBltH8vtu+KlGzUufX8Jb6NCVNlkvHyavlQbPWGrZYWgdepFgrpK/4tcD5FMkHriJCDQe3twLlzAmZlBGzyC+fHjpMYS8KiDHxQxx8Z4WQxx5eo6R4dzXFtpsFTt/mgfZMyHj+049MBViX0f+T+pKPNuFc7+B+jWwEyBbKvyvR23aIvIV2LGqUOqTzlWPYeUi7V5fUtEbblcQgeE2k+iCH0HobEA9ZV33wclNNVLlSxC8SAURpTOmvkL8DrqdSsD3fqWe/XhIxZVMTExMTExH3KkkKwUbhIeL5Eye3FuXsMzNrCCfjL552iVHfbd30siY1C6XsdzAwxbBwGpnEWuN4HnhORGI5w3IsRGC1PLYhgdpLAI0BgqFgFBejXF8vU5gnaHbilF++Awm60Kc8Y1kt4QemRjWiZEGkXNIFdM4jXWacgCuiFI+R38hTLScQlKJYSm7RnE2yg7eBsVkitXqTV1GuUuQ8ezhK0W7RdfpHvhIu2r1wgjdZHqTk/D8hLG8Ah6wibz7DMkjx3DW1oiarXw/DTSH+fSsSmW6i5jxSQjUmKZGtdWGlimxlgx+T59cjH3LLUFJagG71fOUiIPD/4NePXfKIcqCkFEyrmyc1Cf37XyVjjFdomv14bCPrASsPQq+O2tUAoADQxbLR+6QABuAw58Cqpzym1CqOWDzh2zrHahJ2DgiOr5mvu+EmaP/hakeuHKV7YCLAyVYFiY+LGdtg8ysaiKiYmJiYn5kBLJiLNrZzm1dIqXVl6i7bUZoc4vDVikIhMHA2/RZ/Dm43Qi2Gz6OG0fGUIYqIuv2loXtxOg6xoZkgwYWVLM0UjbdF0L6Uega9Q3HIrDKYKVFZyFRXJRjUa5gJ8BFwe9msHOCbrtNsnQYjSVZKio0//YJIvmJpvXG+RknZ6UhbCPY+4bZ2VuntXT6wwVqgzvz7Nyq05ttY3bcnD9NEZWI7FWpzuzTGJqEhA0N6s0zRTG1h36wHExXBf70GG8pSXqX/0q+c99DnNkhPqXvkTkepQ9yZ8OPcalvv1kkyb/3ccO8l89sY+lapexYpKTkz3v34cYc29SmFBiZ+2S+l2YUO7VpT+BwFNiKnCV2PHaquwuCm8LKRkqoWNYyskSGtx4Xg0BDl32BEv4LmiBcrdaZXBbKoq9/ygU90FrQwmtty0FjGBzRvVjDR5XyzeW4ef+BRz9HNz8jnKuDnwCJp7+8Z67DyixqIqJiYmJifmQ8lrpND84/Ye4zVWKQR2RXuPhoIuIItqLKczhHorhOhk3xO4EVNo+miaxemYwU2X8Ti/djUM4LZ9ExgQBmRFBz8Y1Cu0WFetBmplxpGHiuyGprEV/s0o5CmilRxDVTQrzp5l6cJyF3Br11C36l9scvzXCYGKYRDkifF0jyuxHO9QHjoumS4xWldaqTlk/RreRZfOFZTYXm6zeatCqOTihRUaXTATX6RkUJB94msxHPgJSsvDSaxjuBubWFVB7bBJrdobWqVMIJEJKal/4AsbICJHrkThyhMoLZ/CCZbr5STZbbU7dLPPff+owV0oN/stchSulBr/55D4M48PXnB/zV2RbeFTmwWtA5ZYKeehWlTjp1pWAigIlmBCq/8pOK6FlJIBIDd41k1C5Cd2KCoy424DencCLAALUMN/S65CfUPHo3doud+suSFR/lwQWXoH8KOTHVCDF/o+onw85saiKiYmJiYn5kLI8/Qrj3jwi4XNAOHRsyLgSw4Dufo9kW8fs9lLxQwoSUrqG2TdDcuIFQs3HDdRlRHfjCF43oOW1uOF2MMYfpCcxRyZ3HlHdwF87jJRp1mbrjBzrY799kcaGj7UxR84J6A1K5H7mEZYOHmDCuUnfooeWSRCu38AlhUiFBIc1urc0OrbNwL4MzpxDAoPs/WOUl9uUS21aNRe3E+BLA6c4TnqqQP8DnyH12GMITUNGEfytv8vM1/6SVKuGDqQq6xCGSKGa9o3xcaJ2GxBopolz7iUyYY2e9CBiq2QQKfn9l+d46XtzPOAKWlqZr653+PRoi6BUwhwd3dlnTMxd0XaFObz2u7BxbasXSagQC82ARE65SJoJ0lVia+pZGHkEFl+D1QtKXPXsh9riVn/UXYTRdk8VqNRAGQGWer65okr37irGdhF21TZkoFIJhYCZb8PaZbCzymmTAhqL6u+Jpz90CYCxqIqJiYmJifmQkgocWlpI1c/Rm6qAEJQcm4zwMDdNwsUhIusgPSkNzRRkTYGZrxJYATTGIbWImSrjGoKkLbG7s4x18uQGs4Rj64TREhlzlloTZHkSuhp+/2EO/sanqX/lK3hBQOqJJ3BnZjjQzXDi4C/RrpymeunbhJVNojCFcFo0200WOx45M0cmsNDMPNmhPqQfUV5uo5savSNpystt3LZPIm1iplJEx46SemxoJw0w15vg0V/8JOFDj1J+8UUCJCYSoesk7rsP78YNvOvXsacmyTz7DKxdwn9lBmMcPpG8Skvbx0L/ozx3qJ/TP1jgVxyNcXRCLyI8V6L20mtIZwXNtgD29HrFxNyV6hysX4PavOqlsjJKTCVyKg2wvakG8Yqt+PPKLDzyt9W69SVVhleZU6V9d3WaNDDTEDhK5BhppZ2C7u35WGyVD8p3mrsmb/dcdTZh+muq1DA/rkSgo8JfSOThuX/yoXOvYlEVExMTExPzIWX/0Alm2hcY1B0EBTTbZWJCJ+2lMMMTXEv1YFouI2s+upVGE5AxhqinUnj6EtI3aegD9A8HRPMlkk2boWQKz6zgCWjVxtFT81jJJdreEK1mA7cbkPq4inWvfeELuDMzaLaFOaoyDFOPPYaUkvpXThGtt8mMmZhXz6GnJmhbPdyIHNqdBA880sPUUJpmxSXXm2B4fx6AmdfW0HSNdEEFaJRu1Lj0wjKhH6EbGldKDepZnX3dOqVcjtSjJ1g5t4jX6Sezv5eR44Nkn3uW1GOPwfkbdHIaMhpgv7PKrxwOiR4+xMnJHmqvrZKKBK2tC9mcHyJ9i8SRIzjT0/jLy+/Phxpzb+E2lUPlt9Vjr6FS9oYegNwoFKdg/iUlgIQO7Q24/h1YOQfVeXCqWyWC+l1EkYD0AOz/mJohJSMVaNGtbIVbTKhhwp0KyHeZAghKyNlZlTpYm4Nkj3LaOptqKHFtDs79R5h85kPlVsWiKiYmJiYm5kPKweOfQAhBvTZPLj9O72gW1yuRTIxx3YEr018imq5yqzPGaH8vhxgjEz6At6HTiZbpJMZY0CZJt66TqV5GH3iIlPSJnF4IkiTy67hdHWNdQGTQ9RyuXJmlfzzLyGOPAeAvL++UywEITSPzxBMIIah94QsECxsMJFtc7HFxgya13hZ20E/TMrjvSM9O2MaZuRLD9w3z1NhBmhWXbNEmX51h+rUNvEaWgfvHuH69ytXXG9jtmzhLV8h+5kk2GiYro89BvkhzoI+Bz95P+qgKnmgtdNn8/jobmLjpCYYmihyY7AEkT2SaLMo2RmQT6iYdTVMXxtPze0RiTMzbYuWUiNqtadwarF1RwRFP/P2tUru/UMN2/Q5Mf10N+PXbt52ju7lMdhb6DoHfguywcqfSfeA11fDedJ8qGTQS4Lahu/nujnm7x8ttqNLE6qwSaaEPTk25ZisXVPDGh2heVSyqYmJiYmJiPqRous6hBz71puejSHLq2h9ydX2JvChgygLLC5DuLXLgwf1YnQLXrm3QnPMoiohuNERCu8aIu0rS7iVRP46r2wSJBTaXNRqd+9AMGyl82lWX9rUKy9erdKI+UscOMnqo+Kahxaldoqs4MsJGGDJ/uokdjNGX6SFbtFmeqfLGrSu81HyBVu8apm7y+SOf5/Ejj9M+fZraF7+I5mQJOcA64Bgp0o1Fnrj1A9quhwT8vgkMaz8DW71Zzaq7cwztG3XWwimW+p4glDrlKwmSN2oUatfpfO/LhNUErj4MY4e5lUwxPP4EIwNdrLHbIjEm5m3p2aeCIpzK7eeiUAVWNEuqRylZVAmB22V7tQUVX75TvvcWCA0y/crFsjKqxLBTVqV6k1u9WZ0azHwDNq7fZQMad+3RShTUz+jjKsZ98YwShlEIBJAZUvu+8AW1/IekvyoWVTExMTExMTF7OD1X4dwtwWbQYdmapzvUZUiOcOS+PkY/MsoIsOBFrDoVekYzdNaTLBlPYHlNJiyTVE8Gs3kcs32YoL6KQCPSJJFM0x9ZaDdr1Js+EbA2V0cINcR4NzKSrJR8yueqpC+tc/JTD3Lk5x6nWXGwNuZpnTrFzXKBVa1D2htl+JEhriffoNQqAUqMRa7H6JE+5LWb+I5PJpfGXjlLp9WhNj5FHshP9NAIe7l+vYoTSkzX41AQcWahirfWxDf7iPQE2c4KjhylUXZIrSyz4WTZ6B+D0gbJRpuMTJJK9JF48CDJg4W7nlcZRXTOnNnjzsVhFh9yJp6G8Sfh2te2+qJC0E3VRxX6qkTPqcPVr6ko9O2ZUtHbJPUBoKllFl9RvVp6QkW0p/vBTMDgg/Dwb8DN78OFP1L7eNMmtLvvp1tWIu/BzyvRt3pRDSrWTXXMXlsNBF46oxy3B34VTv69n3phFYuqmJiYmJiYmD0sVbskg4NMpI5yo7POUvY6rUSJZ4aOIzSBAB65r49LGy5hOySdTzL42AkixyO72KHHMnAzIWI4jXNLcPNGG4mGaZiM9xfQ/RDHFJjSo7HR5tR3GxxZnMJKGuT7kowcLDD79dNc+NpVglYXEfp0zvwe+547ykB/P93z51ntDuFoo/QeTFN2DVZX1hiPGkzM36RdOY05MoJmW7gz0/R2mrAwh5bO0Nus4poRY16FBWDkvgGW3SxXzzXpWoI3SpssvhJxbrFKf24/DxkVJBHtnkmSA73kehNU1oeYp0VYh1zuEFJPsWoLki2P69fLPPgWoqpz5gy1L3yByPXiMIsYhabBib8DrTUVi+401EDddJ8SI9uia/YHcOPbtxP+wjuH9OqgCeVmyVC5UUjwnK3BvIEKlehsqrLAtQtw/g/hxndVwqC8y9DftxoEDErgrV1QvV+hr3q7kj2q/E9Gal/NVSXKXvs9aK7BoU/9VLtWsaiKiYmJiYmJ2cNYMYltGfidYdJyhI9umuy/sc6+8z+g/blDpB5/nJEt4bCdqrf92JurE1RdckUbazJP+EqSur9CImPitHyy41nMlTZ6rU3Tdai4ARuzTRqzET19OTJFG4DaYpnQDci0l2kmhmg1Qprf+kuMwfsQdpHe8VHWbtUJpx0m+tMkoiz7z0mK2i1qF5fJ/9qvUfj1X8dbWmbu3BXcW7ewBybo1fowBifRx3tYQGL0HMS8tEkqodE3laP52hmCyzX6EwWyjz3GG1LwoKbRU5zAGB5GRhEB/fT1e1iWQ6mhkzZCnorK1CKDFfp48C3O67Z7FodZxOyw7QSNPQZjJyAzAHZBlQXuCBANeg8qYfUmMbVNCCIBuqZ6sXRTOV+RVAOCg62y1kRR9VRd+BJc/N9Un1YYcNcyv7c/cDWoePBBJf4uflmVLFpp1b+1cuF2eaJbV8Lqxrfh4Cfh0Kd/KsVVLKpiYmJiYmLuIWQkd4SLsSVc7uxH+lE5OamCGk4tHaFx/nU+8vwtCuttUsZFNpf/DX1CkD55ktHDRe6MY7D3F7B3Pc71JckUbUI/IlO0SR0p0nesl+uvrHBzroJrZWhTxahbdA2NpG/QKDsUxnvRXp2nlRhCi3wSXhVj8jgicz8CQXK1xVTtKi3NJVfvMMgAvpYhceQI3WvXaL/4IsbEPi6+NMvaskO6qZG+uk52+GHs7CRYBeAm7dfXGK35PF4PmXvlIgcuPU+PCUM+nAc2Dh5DGGnWbrXQOnXCpSbDYURRs4kCm0S6hQhfhrCLFiUx1nWWZ/oZ3p+j+/rZPaV+5ugomm3hTE/HYRYxioWX4PV/r0SPYSvBcbdwh0ZJDex9O/GT7lfhEX5XuUZuA4ykcrB8F5BQn38PDlqoZELNUCl/RgJGH1W/S+egtsTemVcSvDqsXVSR8De/C5/+v6s+rNrCT81cq1hUxcTExMTE3EN4c3Var64gfYkwBRmUkHkv0TTBk/t7OTn1c7xx6xoyKGHnLGw9SdhovrXDEkXqInHXhdLdHC2hCTKZHuZ+8DrmtX5oGfjCZ7Wyjj4oyPVOMPz4Sdz5eTZfOIvVWafAOt3hIaL+qySSQ1gzPRQTPqMjddxb8wjXRrMLONPTyHYL5+JF1l++wXq0n0jrpZ0ukExbuH0j9N83Rme1CcBqfRVzMEmf7xN0lskn2hx4/BnWzl8mmfFgpI/ay+u0qg522iRyQM+a5A8X8Ffb3J/tkpSXueY9TmUpj14KufTCMs7VayRe+PKeUr/UWyQexnyIqS0oQTV4P6xd2hoAvMXu/yciNW9KM5S7dDf8tiq9kxLa6yo4QtMAW/0OvffmmK2cKvmrzcPqJTBMQFfR7f1HlHC668wsVBLh+hX4yv9B9WEli0qgwT2fFBiLqpiYmJiYmHuIoOoifYk5nMZfaRNU3T3O0HuJJjQOH3uWzYELeLNzRHQwBwcwR0eJIsnpuQpL1S5jxSQnJ3vQFl6CM793+647ICafvaujdWLwBPIZybf0U1TWW/QlLVaDEhP7VU9VFEW0x3thKo1tHyRxbD/l7EXCoEwnWqLQOI61UMe9OY9mGWSeeQqGH8RfXsa9NYt36xbtpo0UOunOCp3UEJ4fYAz04q92qIU1AFbdNYLrHl3Zpd55HWv9FtUzvfQOHmXf4eOsWCYNQyORNnHaPk7OwkyaJLyI5GCKzL4CdilLz1Kbuj5A31iazVbI5tmr9M3OkTx8mKCphKg4eTLuoYrZS2FC/a+sXVK/CxO3X9v9/+TUIT+mBFN5BgIXKaGzYeK3dMxMSGrYQWT6IFpXw35BrSd0QL610PlhMDPQM6GCKLpVVTqo6UosLb4KZlI5ZAj2ulW7iAKoL4JuK1dNiL1i8h4lFlUxMTExMTH3EEbRRpgCf6WNMAVG8cclqRSpxx6j97d/m/aLLwKCzLPPkHrsMV6dq/CfX53H8yMsU5XtPNlYQPounvU0weomxswa1oQE5JtS7zRN4+TIScTTgi9Of5FXO39OGAaclPtonl1h8cw13NOnCbttFoMynaemyIz1kVzN0iyfx+Eqlt7F2n+czLNPk/rMbyEMdVnTPn2aWmmZRGcTLTlIJz2EFgWkUm0mPr6PsOpQXlZlUMuZDZa7ZRK1kOJSEzLjaOljaIWDBLUcdnkOq9Ugkglk0mQ9XSftlRmMsvQMDdKuDNMN/muy/WV08qwtCqLKdbTFc4TlMu2XX8YZ6eUacxRXX+PE4Ak08S7KnO7i+t3r5VExd2HiafV79+e8zW4Xa/Wi6rvqOwzLZ+Hyn9KZa1K7kSQKBZougQ7pkV2Capu7zbC6G4miGujbWFLiLbxjO2hboqmtSg1ba+ppr6WWJ1LuVeTzloLqzuNqratt5sfe3TF+gIlFVUxMTExMzD2ENZknA3t6qn6cCE0j8+STZJ58cs/zS9Uunh9xdDjHtZUGS9Uu9E7gORO05pNIuR8xP0Bmrk6wOfOWqXcnBk8wXZnmG7e+waHmOMkbARvaTZKrEiM1RZRySc29iPfyNPJnoCNLCL9LpncMrb+N/dQnSH/ul5FRRPv0afzlZYzhYQqf/zzi+y+gvThNO0yTSYQc+dt/k+TBAt0bNazXEjDqUSzlqXYShA2Bl9xPva/GqG6j5QT+chlj/Rb5VotqEBEVehgLM+SjBKHm0CqtoQsNYRg4xV4StQto03PkOjVytRmsg4dozV5ns7pM93/7CpXvfRvtZ3+dR3/ub71zlPpul0K31EV1Ih8LrJ82NO2ty952u1hmAg5+Si37wOdBaPjXvkwUShKFAKdm4Ld0NcfqTbyNa7SNbsMjfxvyI/Dy/1cJJ7ai22WAShfUVDqhU4PWqirfs9LqO+q1Vd/XuxFT20RbARmhBysXob50T3+/Y1EVExMTExNzDyE08aYwiPeDsWISy9S4ttLAMjXGikmi8aeYT3YQwkUMJMkbRYKqe9fUu92BG8OdIgOpAU54D6FFgrblkokkWqeCHoXkgojWq8ukEuNw9CBiqYy+0kKzbVWKGASs/fP/J+3vfx+RSmEf2E/xb/5Nhv/xf0/+Y2fe1MN0/XqZRsOAUehze8l5Fsu6T9OEtBzAMnWcNQtCDz8qkEgVSLQ9sm6WomYiNdAkCClBjwhlhLdWI3PzMvLmSyRSObTkOH4tzcXxR1nWOwyXN3lgOcJcfJXl7y5h3z+KkctijY3dfV7Vbpdi9vsqrc2wVC/Lh2Tuz4eet3KxdANGHsGcOI1WWsKpgaZLzMy7dKR22C22hOrJstJw5DOqvK++BOvXwGuo72IUbEW1C/W49zA8/t9AcRK+8t+pVME9aCoa3murbd+VSO3nu/8MkgXo2Q8f+6cw9dwP+V7ef2JRFRMTExMTE/NDs50QuLun6vRchReaRR4MfcxVGOsPyRVtCN+cerc7cGM8KDBZGGJG3uJh/QBaNyLyHXQjSejUiNplapkWiTfq9HQnSBx+CK1/FSsZIC//KaX/37+gde4mMogQtpKbb9fDtCJD7La6AOwJ0nhopMwEFREwaOrohoYWAlJgahn6hEaU8ClLDUNqWBGYmrogjUKBlNFWiVUXJ22SsobRRp/kfH+erx8cwHEqWEFAYsnn0fUW/npE9z9/E71fxx4eAracu90lf05dOVRrl5SQCroqorpbUfHVQw/c8439Me/AO7hYqfv2Q7eGv7qBmfZJ9f8QQRRCBzTlQmkGCA3mfqBK+gxbzc5auwzlW0pMSQkIVdondLWNblkJqv0fgcI+qM0BKp/wbMKmZJiM9O3jxPos2luKKrai331ouaqU8OZ3Y1EVExMTExMT8+FgOyFwN0vVLjctGD6Yo7HaIhhPcXgyjzX55tS7zrmNncCN4orkU/mPcnNslQHPp3b2NbJzq1itNmG3TFi9TsoDp7FIBShuLlA82IZSmfpra3TWNaQboSWTRJ6H7HTeMq48khHtyncJFy7BY5+GSNINIwxNJ6frJDQNsXUHXyDQNANdCIZsSBVm6CQ2EW4/euUgQmhAQITEtQL83CBe8wZi8EGiwhilgsA3bQ6Wmtzq72Ul6xPdmEbLjYJIIWSXyPVupynuKfmzYfykKvlz6nDhy9BYVFHZuvlT0dgf8yMw8TRCRqR1Xc1/ct5GtNy1JE9XARNhdDvNLwpvpxDe/C5UFyBytwYO79qGDJWw6pTh2/+jElU9+2H+FMiQswmbL2Yz+JqOGdVAC3kcDSW3hBJwd+vzkqEaKlxdUDcY7jEnNhZVMTExMTExH2DumrL3Hs+leq8YKyaxLJ1TjoPVb3HicM/WDC3xJsfIKNpgCNZvVGiHEXq+l2ffkKz+4C9xr3wfs7ZOS49wkgdojj1OPVWl2F6i1O/x6Poy6YKJwCYKIuyBDJ25FpEfIEwTY3wcGUVEQUD39df3iLmz62fZWPwe+1C9XboQ9Js6zVBS9gN6dR1QaWlSCoQAkDg9M7T2f5XA7OD7KXr5LHr1CAKBHkHBKyDyn6CbPoJm9WJFGcaliaUb3Bodx2xuMtz0laCKAmS7TGgXMUcPgDWMjCTiznjtRB4e/o3bA2IvflkJquzI3pS4mA8fmrYlTiLIDoHb3Op9epdID0LYKfvLDato8+0UQgmELqQHVZiEMLbcqi0xJCOV3Ld2CRrLSqAZKs2vZOj4QnAYmxkzScnQlRsmAyXG3jaFMIL5F2HuRSXW7iFiURUTExMTE/MB5vTdUvbucIh+0ryV0NsuCVwsd2i6AQuVDsBdhaA1mWeh1OD0Gw0MP2LiyiZJv4GhTxKM+2idb1HNjbEx8BTVgk3D9vCWTpOYm+cmCYxQZ8zqIAwNIXzs/hQUBkFLoCWT1L/0JdyZGZw33iByXKJ2i8QDD1Ddr1ErmtyX6scFWt0VEsYALddlpb5BOtkhGhgnHSbRInUxKISg3XsJL72KCG18q06n7zKp2rHtM4JXuIWf2ETvFEnUMiAFD1dVk/8yAX2Xz3B8foEgWUSGLUgmsQ5+AnN8P0Etx7mXFmm0k9zvCHpXLyHMXfHamqZ6qIYeuHtKXMyHizCA1/4dXP0KdGsw/KCaDRWGSmgh1Uyr8C4u05vYSu0bOQEnfhNuPq9ardL96ru2XR4YBXdsZ+tvY2sGllNTxwWMBBJTCmZMML0OIxHKBUMo8RX5b//+Whvw+n+EyWfuKbcqFlUxMTExMTEfYO6asvc+c6fQi6REE2JHZI32JPmj0wtvKwSFJriV1DiX1fkZaSMW29TTBoWaSRaLcnGc+ZGH8BJpOsYCIhrkjck8YTdFSZ4kafRxMlvnlz7dpRcNc/9RPK9A63vf2wnEcGdmVOJgNkv30iWCRpOhmzmMhw0uHgwYA9qFNrRKhPU5evwbWBuLbNbH6NCDmzpEoXgfWdNCbjlW22x1mCClpFOcoTH+PSLhIyIDTdNJVo6gCcEjtZAH2j7hepNgcwZpWViHTmKOPIY5PIWWE9RuLrB5dZ7lgXU29QkeHp7g4JH79wqnt+uvifnp5K1i9V/7dyqhz2ur+HSnpgImtJwqn9M0GDimnm9vqLlR0baLpam5UHvK7zQojCrBU99ySytzkOkH81HQTKjOKWcq8lDffJRrKiMlgoQGqV5or3PCcQFJyegyEkpOuB7YWymB7ySoAAhh9gU4/W/vqUCWWFTFxMTExMR8gNmTsmdoNLo+Xz679L6WAt4p9H5wfZOlamdHRI0VU+9KCI4UEjQcjwtrbZ7xIBXpJDJJcskerjz+66wt+4ytlkkxjJQh2bCO1hRYtuSr6cOca1v4D0/yT3/+PgDkq6epeJLuq+dJphKMHjqMe+EN3JkZBJA8fBir1eQT1n7mnoigDPTPYllV+uZnySyvI/yQ7EqZSv4wNw59gqgN96dC7PVjhPkbRHoXu5vH3LyfSEoEECQ3iYSP3RrHzSziJzZJiaPqmIBISsJOhcA0CU78PMbUc5jJLFFH4l5dh/YmY2vPM+mXqfWlWeJxDsYCKuYuw7SZfBbWrygHqu8orF1UX7L8CBgp5RblhlRMeeAo9yqKAF3FsiOUEAocVI+TeopU/5sTJ+uLqrQwmYdgSJUBhrranpmEwjg0VrbcsEiFqGg6GoLHHUdtWLfU/jQDwtbtfe6w1Wu1Xcq4/ZzfvucCWWJRFRMTExMT8wFmd8peo+vz+kIFP5DvayngnXHqSLlHRCHlm+LWd0eob8/XEhLyLUnQjNhs1zGDLn44R09ug5GeKVrzM/S5t5jLmhxYmGdw5Rpg8qT8NsOtCr/3yF9jq+kJgEu9Uzw/9RTJ8jrdngF+dvJpJlMH0fou402/SNBsoidsDh97lvuNG3yjDA/bHlFjljoampmmnhRYlRabQ88QGWl6TQ1b0xDlw5j6L1DRNmg1stQrE+Q1hxHbwuj2oUkTN7OIJk1Mpw8AiSQIA9rrl4kaSxi9h2knj9AKM/SMF0ldXcbrlHGZQW/eoFSboBjUyDvfo11UPWACfvghwPHg4HuHt/qsogiufxs2b6hY8tqiejzxtHKhbn4XNq8pxymRBwQkckp8bcyoOVJ+97ZQ2XZao0A5TLoN4dbNjtCHy38KD/7q7cTJwFOJk1Kqv4ceUCWGnU3wHbAzgKbWtTNKpPmO+n+Mgq0yv0iJPE1At8rdyxC3jk9Ganuaodazs/dcIEssqmJiYmJiYj7A7E7Z+/LZJfxAvu+lgHfGqctIslTr7oio5w71IzSxp+dqd4S6MAUZYLnukA/hIXx6Oxt0Ih23sQmVZQaOVHijbwAx+wZT6zXGasvogcATYIcOzy2dI2lpPDPxLO3TDVKPPcZS3WVu5DD3TdzP4MU59L+cxuvPYE48Q/LBB8Bb2QmsCGYdWKxS/uqrBGtNoq5EaAaycIjO/qPki1MIQ6PfEKQ1QTMUJNcG0aIhVtsOA9RwgpCSGGS8coicgCCxien0kaweRkpJhKTpNyiX58mPPkWubz9CS+F7Ee7FEm67QnvlAqEzy4htMC43kRcdNPMamzf/NX1/7++RFud3BVQMqw/gne7cv5XDEfPB460+q/lTcPM7UL4JG1fBykDprFr+8f9WLbfdU5UZhKXXoDavnKmdiP+tIlXDVmJHbLlCYaCcLEDFqodQuQGLr91OnCydg2vfgOUz6rt37HMw9pj6Lnarao5VcRKqs+qxjNT+tAQQAAakelTUutdU7+OdgjSEcfscJIv3XCBLLKpiYmJiYmLuEe42cPf94M449SiSbxJRd5YlBlV3J0LdX2njlR3qrofW9hnxJcnsCEQBbgU2O20ifZCHsgL2PchI6SyJSBBGIQYhEkEi9Hl67izB18pUps8DMNYzxYFbcySWXUY0E5m2aDhz5IHk0UOkTzy5czxy5HG49C266x5aS5VI1Yr7aY1/kv7iMCk9wbBuYAiwBSRFiGw26To3sUYPMkwf+AJb6ERSktg8hKYdUe0qEiIR0E7plJ2ASu8D6OkCOSOi2iohElly1Vv4y6+Rrs+yMLQf65Mf5eDCFertq5DTCWbnaH3zi6THL6gyrKQSsu/qzv2dKYL30N3+Dx13+6yiCF7/fajOb/U/RZAZAiurXp94WjlHzTUlvBZeVa6SUK4xZkJtY9ulCr0tp3IrxCIMt7aroSwsoQR76CpB9eDfgI1rSuCkx9Q2m+vgVJSTlN8HS6/CwstbM6y2XCZC1TOV7ldO07HPwSf+b6o36vn/WfV2vR3pXkj1qd/5CRg6BuNPvv06HyBiURUTExMTE3OPcLeBux8E7jaz6k6Moo0wBf5KG2EKrrsef3lllbEwpCtC/O4m/ZqGbYRcH30MORuRWVuBtk5nsY3dbILQEDJi6544pufTkH1sNnpJXlnjANdoXrvGphjBll1IHkMGNkFlEz1/fOdYZBRR/dKXIJ9H+tFOZLlj92CaCazAoRF49KZyhGi0mhsUdEGntUKmWyYpn8Q20gQCEuHW0UiIIp9IenhBg5Z/g5oxSM2xSDtrdFM5PM0kkR5Hc9rIRhmRGMMxh0m1VzFGHqK05qBFM+itDqbbxV9eRO4zEMke1a9ipd/dnfvChLog3o7Hvofu9n/ouNtntfASlN5QJXViKzo9dJVY2n79zO+pcrvtsjpN30rYkxD4kB1UCYBuQwmh1srtkAjdVn1SwlCCK/KUmNJtNRPtu/+TSgH0u0rEpftg6QxUrkN7Ux3PnWmAAtAs9YedheGH4OCnlJh77H8H09+AxbOgG1vi6i1KAdNFJdKcinLf4p6qmJiYmJiYmPeadyNePqhYk3kysNNTdavSpOUEdFI6dZFiKExDa5O69DEDHVHeIL15k4bRi2Pmt7YiiYSGFAIzCjD7DjHecxSpZ2ifXsWd+Q5p16c20IOTLOB0GiRlm3CzS/faNcyJx6j98R/ROvUi3fl5+Ad/H3R9qzRKkPBrNH0HP1OkoJtY0kOYJqZpEjkNcKokhx4jFCkSEUQyRBMC33fwhYZwKlQ3XuG13iscX2xhbz6ASDxEOzmI1qlSdVzc5DEs1yUz8AiaJkhGAc1Ohc6rV1i5cYsR18NqNhHJBEEzoFPOkO4TSlA98KvvLkp9e5k4fv2Dz90+qwt/rErn3Dp062Cm1HDdsceVc3Pxi8rdSuTB7wBi68aAVAJKM2HfU/Dob8LZ/wALp9V8qGROifPtQb/HPqsEUH0ZciMqHOLCl1RqoNuGoeNKRGUHYWNaDfuV4VsP7jXSqiTw4CeVoJp4Wh3Xa/9O9WPJEDyVDPhmNEj0wPCjUL5xT7qssaiKiYmJiYmJ+ZG4awiFJvbOsyokeEjevuwYKybJJk3ONds4huBn9RZ2OE9w3yhjax3KUUAzMYDmOSScCgBCSkLTom0mkEFINjNCUjcRlTmi9CBCJClsXsROD5EsnsQywEoNI4SDe6VFefG/UP/ifyBsNtnu7hCJBMKySRx/lnRukOr6CmHDxc6NoCHQSRKlC/h2EvJpjJ4B1tpdUh7oUqiWFSHQZIiHJJk/wsNtj4I5T9pskU6tUl1dR0uCnp3E8FyKukS3ErR9j1ToYqETvH6JkdxriIdcZEXD7/ah943gFyfh4VHwGuoCeOGldw6e+GHj1+Ngi/eP7c9q+zO48MfQqam4dK+llpFSDffddm523K3LKlhi4mHV+xQ4qnwucJQYkwJO/B1Ag9nvKaEkNJXcJyNYPANuTe1Dt1UJYeipbUeeilFPFNQ+2ptKiAn97qIKAX2H4f5fUd/T1YtQmVfbf/lfQ3MV5ei+1eDfSIVrZAagsXhPuqyxqIqJiYmJiYn5kbhbCIW9v7BnntUBV1LEotc2EabgoceH+d9/4hA/uL4JUjLVDen/s+/RvdkgSPVjjSTwmjMYq1cp1G+CEAhdR5uYwDSTaKUliBpoROjpQQh9ZKeCkBG5sIMRNBEhkMojNJ/IC3HOXSPsdDBHRwlWVgCwx8fJfeSX0HL3E25W6EtvIDsVpAiRrkcU5tTFpJnCt3Iku01GyytII08oBB080kYWTzO4mS1QFL0UewYZGmjS+s53MWdeJl9vEA49QCY5CHaOpGkjEdhaAkdI/EaF/L4m4UNpInS8Tpv0i0uEmxnMB38LvNffHFYx8fSPJoTCQPW63Pre1vygSKXHmQn1+j1ScvVTw+7Aim4dnCqgga7dDpXwXfV5P/g31OPr31bhFWZClds5dSXEQg/cJvLM79FJPIsvfh6zf5BU+7uI+pLqeWpvwPrl2wJJGGBYqp8pcJU7phlKaJXOq9eCzlsIqq31i1OwfBYaJdUHmB+HTkUJsh1B9TaDiJ0qzPwFPPc/KGF1j7mssaiKiYmJiYmJ+ZG4M4QiqLrYwEKlw2rNoZAykTUXxwLMNt5CB2FUeeqvP8nTB1T8eOuVOguyD5E9gKEZmFXIjPZirtSItK3yJk0j21Ng5JOfICyX8VdW6F4+TRCYyE6FsHwddB1hheg9efB1NNMEo59upUJ7YxndcWB1lchWIR/lX/87DA7fT+flm/hLV5FRGul0wDJByyJ1A00KNEJML0I2y9Cu4slNDD2BZibppBNEyQwDmoFmaORPHCG69gpRMITRP4JX9EkWx0FEBLKDiDTCTgVNMwgba/SVr2Ic66eTa5Ks9tJNXSAodvBvlUjkW/CDL989rOKHSfjbdkLKN+H6t2Dl4lafzdaFrpWGo59V/S73UMnVTw27Ayumv6mco1RRzYECKF9XjlFu/LaYPvBJOPgJqC9Bfkw5Uxe+CKUzkCzSuXKL2swykZ5CS+Xg5/8u6dSfqeWD7t40PhmASKjSwdxW0ESnqhwrp65mYnXWVdpg5G9FrW+ljwpdibHVC0pEpfpUX1eiqMI2onArRGPL6dKtrTlZdxFYm9Mqgv3h3/hxn/H3nFhUxcTExMTExOxhd9neaD6BFFCqOW+Z7HdnCIVRVBf5LSdgud5lvtwhQuNnXQdnuYwMfcKNBex9BumTJwEIVlYIEr1gZbE3rpOSY3QamyA19O25PVFEVK+h53Ikjx2j+qd/SrByGRxn51j0vj5EyiGqvoFITYBexA8knhdwa+AAveVZjFyOhf4JEsD3rq1j9niMuF1klMbzPLxrp9B79mEPPIxEAz2FFmkkBRiJfiI0hOfieG2WhWTeEgxnkuwbSFJermJ9+wy5ZoRWPEHKSmI4dVxAi0JSbgdN6phBF+nUSM6+gD5wDD/qw2EJr6+O1ixgVDoEGy1W/h//guKhkFRfEbE7rOKHSfiLIuVKXfii6o3x23csoKltrV2GvoP3VMnVTw27AysSeeUa1peVyEn3Kddo9FEQEs78+9ti+rG/u1eArF+C2e9C6Sz+siSqJ0mM9eKsr+OvHYcHfx5O/b+3giZ2I9X27vuc6qG69nWozaoyRCGgchOG7oeTvw0z31DH5jVVH1R2SMW5V2dVv5ZTVd/T2px6rBlKnAlTCaYwuC2y9iDUjYN7VNTHoiomJiYmJiZmD7vL9ppugESSs823HDh8ZwiFNamCJbK2wWg+SSFtUW+5tBvz9HolrLFevLkV/OXlnW2Yo6Po+hsYnTp6sh8vCgjrK4TlMloQqNmlYYi/ukbtz/6MYHGRsNHcI6jQdYSmoZkmzaVLaL0GCaMHv7aIoRc44vrUR06yOjKMtXwegMOvf4/VkQL7j/eydukHsHgN1q8h0oMYfhPRuInoexANE2lraCGARKQHIBUwYej0+SGNcpn1ckjOC0kbFug2mgGgI7p1NM0gqi8TtFaQuoXwOzvumj5wjMTmfgh9/EwLbW4FcWMFkDRfX6AzrdHzsEnv/QnE6KO3Y6Z1C2a/v3UhW99x84DbzlRlHlbOwZWvKBdB+nf5xKW6iD/4idsBAzE/WXYHVuTGlXg6+/uqFyp0gUiJl/rSXjFdmb+9XmFCiZn8OCR7MDcuo62BUzPRdBczG6lo9G6du7pEVka5X9VbymFyW2q/0lAiKDsIJ/8bJYwuflk9zg6r+PP1K2obRkKJpkQRcqNg51TZ39ol9bdT3foO3mX/ZloFdOTH3vPT+5MgFlUxMTExMTExe1iqdvH8iKPDOb5zdQ2QnJzsfcuBw0IT2PsL2Hc8P96bYiifYLXu4EtY6c+wb62ENzePZluYo6M7y6Yee4z+y9eY/+YMIWm0+jLW4nm0yNu5/BJSIrtdnHPnwfO4G5HTpdEzSK1UwW5XCMKIjJYDzcBKDlLQJFktz7rVRwMwQ5/hmfPcHB1nujbPUHMVM5kj094gDH3CgxFh3+sY9RSp1jEkFiLRi0CQcFpYZj9pzac3gjAK0TQdXVjIoI0wEmyX1kWRi9y4TLh+Fbn9joTEOyTpjrxIIqWTXD1Ewnfxpi8Qyi03UErCVkT1vEdybJB0exMWX1EX4asXb/daLd4RP73do1NfhpU33rrcShjqYv3Jvw9P/oM4oOL94m7hItVFaCwp96ZbASv35gh2r7G3DHTscZXkF7ik7huHtIPf0TB7h0k9+Qyc+/3b0ep72JqHdfb3VE9WYxnYDpUIVFnoygU487vqOLJDt4VdfUG9Hu76n3SaKnjCLkC3DMjbyYNC31t6uN3PlSxuOVh7nfB7hVhUxcTExMTExOxh95DhXNJEIv9KA4dPTvZwpdTgK28sY2oa30sMs+9jn+FQ1MQcHSX12GN7ll9f6jJrDBJ6AXpqnOHsBD31GwgEeu8htFQPkVuh23ODIB9iBRMk1segU1WOTz4PhkF44zqOnUKjBvOnSEQaemEM0gPofpVMcgDZ008DGLMlhfkZVmdnyXUaNHI9SN+nHdSRB28RjF0mDNuIZIJo3iHVfAChAVoCyy6AEERuGy2RRdMSqlzK1EFoRK11gtocYW0B6dZhYxptS9gIwD0oaX9EIvJ13OBbRI2bWFe7hOUb3BZA6gJTBuBbByB0lCsx+awqE9t9cbu7bGq7PBB2XeyKvdu1s/Dob8GRn4sT/94PogjmT8GN76ieOSmgMKYiyfc9Cz37dgQSuRH1+M4I9urcXufKyqmSwNoCIj9GWoq9oQ83v/NmUQOqxA+phv5uf2/2vK6pqPWLX1bR/ruF3XYy4G6CNtRLcPAo9Eyq57yuOpboLuLeSMLUR5Q4ayz+aOf1fSIWVTExMTExMTF72D1k+G49Ve8WTRPkkiaD2QRHh3NcW2kwO3qEsCelYtbnqjw+UcB5/SzNF07Rvtal1xpDd5dZ0xJ4iR6og9Z7CHPqOYRm0B2YpzO2iNRNgoSOMdNPcuUBAMLqDbAsTDvJ/H0nWe2dYP/CZR5avUaq4aAnimj5EcyBXoae6OdmuEHfiYfwz5wlaVokCLkxfJC14hDHHjpC/kAJd2OadGmAoKeNn2ggNJ1w/RoiP46WLICUW7/V/B4ZeYjAJaov4t34NkH5OhJw7CTe+BQ9qwsgJcIwCId9yGkk/SGcdJVosIqYDsDUEEIivUiVgQFG1sDUNsDou93z9HZDfrdfa2+oi2hNVxfxZgr6D0PPFDzyt2HyI7GYer9YeAle+JewelmVb24HhiyfhY9ptwVUdU5Fqm+X+m2Xf9YW1PO6dfs70LPv7QNLDn4SXvk3qkRvN5oJrXVV8pfIKZG1o32EGtqb7FGuqJ3dEW4UJuDUv9reCDvuloyUcMuNqbLSyizU3tjq5dr1fdMsVTKomypExbBgYwbmTt1zQj8WVTExMTExMTF7eC+HDI8UEjRcn+9cXSWbNGk6/k6/lmlA6eUzjH7/e+TqOQqZx0kk8kSpPG5tZWc+lZbqQWgGUWMZ/0AbaRmYGxnCSQ0/0ySVeQRj6hkAwsoNjPVVnjJPszGYJ//4/Qzbx/A3NyGwsA8cIPXofWjjafjzb2L29dFdWSEZBAyhoR2b5OG/+V9hLbfYvLCJnU/i9XvoQQJ9PUBKELkRiHy8zgYicNFTA0StVYTQEGaSqFvDu/FtwvKMOibdJEokyWWSWPv3g+ehFYtoEwJGG4QmJJOH6Tv8SZL354haLURrEefVb+NXWui9IxQ+9xlSU9m9bsXbDfnd/nvmL+HWd5WDphnw6N+BJ3/7nrpY/amltqD6m0L3tnPktVXJ5rYbuS2Qdpf4rV5UM6sCV6UEjp9UDpXXUAIM7i5Ioki5YaleaJbueC1QwjuRg+O/Buf+o3KmhKGeR6hBw71HlAC88W0lunLjsP/jsHRaCTIp1LII1SMlI5U0Wb55e+4WqO3qlgqw8Lei2jeuKXFVL6m5Wx/9JzD13Ht91n9sxKIqJiYmJiYm5j0nCCJ+/5V5XphZZ3ajrQrPpODcYpW5jQ5HhrIsdS8xfeNb2PUyfs8oIx2HYPMaWm6UwdY8Zv0mUgjCbgUjCtByo5jNAJEP8Ae7GBSx9GNomX6EEIip58CyCGs3MTfWGPvml9DSaZqDA+Q++Un0XA5z1MDenycIVbmSlslgjIxgFPKYtToPHRqCU19n8bVNCvYEnX0fwXZWSDbSJMtZpLFGWJ0l9FuEg8fQEMj6LMHcCxBGqkSxUyEoX0cYJlGxSJBIk8xkSOuQ+6W/RvLoUfxSCWNkGO+QxHFLJBNjFAqPI8SuC+F/+H99+5P8dkN+t1+beBoWPh0P9v2gEUVKnHQrSlRsI0MlNHa7jncmPa5f2fs4kVfLbwsvzYQrf6b66AaOweP/rXKaFl6C1/+9CpFob6hYdBkpJzORVetJCZe/pKLTkSpUInTUMt06EKjI9/JNdWzLZ+Ej/xg++k/h5vPK7Wqr2XOEHiy+Ct3qVl8V7JSgarrq7dpOIfR3OWduU/WSvfC/qO+rfm/IlXvjKGNiYmJiYmLeV3bHrL9VtPruZf+nr1/hK+dLdL0AP5IUUxalepdqxyWSsHnDpTC4TqvXprdwHFMWwEqQkhEi7GLpgrD3EJG7gmwtEsy9gEj2oN8qkyx24OAgdvI4dqUAGYkwXIRpI+wCBAEyDAmdLlGrhb+8jHvlKnpvL+bgIH3/4O9jnzgBgDUygjk4QLC2DoFP+/sv0D33OonkOOkxHSHH6CaPkNm4SLR5laBbIdycwTp6BG/oMEzPIstLyOYCBD5+Zwkvk8M4dIShX/1l9Eya1vPfI3HkCM70NEYuR/qJJ35in9vbCq+Y94cogjO/A2f+I3Rqu8rsNNW7NPbkXtdxd5mnbqtyueYqdMqQHdkbsT9wHC78sRJNug03vqu28eTfv71MbgiqN7dElVAlh4EDeEpYtVfuEne+Vc7XrYDbVuuAEobNZXjqH6qf83+o4tilVG5Tqlcdy/Z7kyGw5XzdNTCDrbRDYOEVNQrgqX/4o5/znwCxqIqJiYmJiYl5R07PVfjPr8yrJL9I8rmHRvg7T0/eVVi9eqvMn19coen46JpASnD9EF0T2JrkM2zQml8kndQxR+5HrvShOQLpGhB4CCOJlh1CS/fhr76KdFaQTkkN95WSxKZAq1iYU3m0hI/0u4TpXgI7B0lIDGTw5s4RNepI1wUpka0WQRAQNRq0X3xxR1TJrRjyoFKhGwmYuYnuOmjhItHcC9jGI+i//JuMaHmqv3OZoBFh73uE3t/+bYSmsfo//jl+ZRmh62iZDNlnniHzzNM7QRydM2fQbAtnevpNiYcxH1KWXoWz/1HNcZIhqg9JKPemMA6P/dbeaHwZKeEkgUy/SnrUTZWkN/74bQFm2DD3ghIxga/CH5wavP6foLkG6QFVclddUL/NNLh1tZ0oRKX81d8cOgHqGAJP9VW1N1U8v26qHr3NrR6o8Se3RNYqeB21nY1rSoD1HVIiMOhCGKpSxXcidJQojEVVTExMTExMzE8SGUm8ufqeeVHiLdykH3a79ZkKY0ttHC/gtNPl9OxlHryc5L5HjpB67DEkYsfJemF6nQOu5DFpUgojLuFzojbP4xsz9LWrjG/Mo4cB6fke2id+hkRLJ2quEuk9aDJAhi5RYxm9bz96dghvYwZhGGAaEIRqCHD5OgEgUj1IO0OQGUD0TFEpHKR/6n5s28Y5+5dIofo79N6DiEw/MmpTqjr84HwJE6j/yX8huDWLW67Q0UySnoeMQqJOE63bpMfMUDRX8JZKZD7xCbRMBmtsbCe5MPdrv0r19/8TkeehD/ST/5VfJvv0bZdhezl/efmuiYcxH0LqS6rsTYhdgRFS9RkNPbTXpVp4Cc7+h63kvKUtwSPhyGdh44oq/dN2hVpc+AI0Sqrkzm2oZeuL8PrvQ7oX9n8M0KC1orYVBUqsCVS53u5Y9DtJ5NT8qjAEM6lmSvkdmP5zuPUC5IZVFHujBE5DHZfMgZ2HviNQn4PSG+BV3/25qs6++2XfZ2JRFRMTExMT81OCN1en9eoK0pcIU5AB7P2F92S7k4sdui3JsCd5oFPn4PoK/lqX6sx5AC72TPH8F75F33qTx/V+CnYRRxN0oojjnVU+NvMdhpvriEYDfB9hmoilFsn0NEb+AazEAKLbJmyW0HoPoPfsA0MQNtdUWp5pIjUNwu3+E6mCIMrqPn9w8JOkE0WSjQWi4DBaqhc/lwc0zPwk5thToJtIXWO1VGb5S3/C5DP7qdTaDBw6RGt2HiNw6WbyJNo1LC3EKiawEytUf/d/xW9H4PvkPvsLpB57DLHlJKSOHaOzb4Kw0UTPZdF0fc+5E5pG+uTJH/kziPkpIj+mItIby3ufDx1YfUPNIdsu2dwu2Qtd5QgJDZAw/TUYuO9279XuMk+nBZvXlDsFqlzQ3XKQbn0PCvsg3a9K9LqVLbfsLjHnexDKNdu8DnYGfEeFaiCVsAoDKJ1TYSgyUM4WgFyFzqYqXQzdvf1j7wY98cMt/z4Si6qYmJiYmJifEoKqi/Ql5nAaf6VNUHXfNJD3r7rdHtvALVr0r4UcNbIY+X3o2TQyWKF9bpPl1grHzp1ntHiEtB2g6R4zZpeAHOOui1dv0NBMskKoWU1SQiQR7jLNdZ+cKzF0C+G1Ccs3gQA0h6g2h95/BGHmEE4V372lbqxHEZqmgWkSRhGysYYoTFHQMojNCl61ghFkcfwK7cFD9Bd60VslQqOP0dIGmfIVKs/sp50usZReoHU8RXQdWlaGgZRgbFzS98yjeLeu0ZpdJYpswlqdxte/TuLIEdKDPtQW8C8soaUzpE48hjM9jV8qvdPpjPmwM/YEHPwZNUw3cPa+1t1K8NsWSNv9VKU31P9Mz34lxpIFFWu+29WCvYmQTh2ufQNWL6gSv0RBCat2FcLu1iBeQLOVoHs7hLHlZrnQ2ep5CrYH9e4KmYjucLr2iKgf0jXXDJh86odb530kFlUxMTExMTE/JRhFG2EK/JU2whQYxfdCUqntaqbGIwmbdcsnlCZhUGcwzCCsfdRrKQY2qtj5wyRSRayggw4ciwQrYpVEuoQwQsJmBy+IsAEZhBiDR+i5/5exvRBx9SxGYRKhGRAF+LMvEFauow/ehzn+DEgNKQNadgpt+QKapiELeaRpI8ub6OtX8EMPY+AYenEKfasnS8y9QKdbxenpI6EbeL5H2ClTzatzEz3p0rRh43CBWvkkq5dNTuyLeHJyBl1uQl8G6BLW6uj5PFLTqf77f029OYvdb2ENZ9GCLM70NMIyCZtNav/lv+yU+ok4aS/mTpZehatfUf1FewYyS/BbKv1um/EnYeUNWL0EHU0JKisN931ubwBJFKlSwdqCijnPjym36PDPqH1sTisR5bvq6t9r79r3tigSKuUPeUdQhbgdUrGbN4VZvBPv5Ibt3qWlBlP/7P/8Q+7j/SMWVTExMTExMT8lWJN5MrCnp+qvyp60v0KChx4fxqo6uKUGwUIDPXM/gRsgEzabg0USdY9MWiOhW+hWnkBINHyGtDmsRBP/wXFmlhwKa1WG22WM/iMUDnyK0BskJSGayiO7FcLNabTcKCLVA2WJMHMgBVFjCZEfx9IzeMkUXWHQSRYprs5jhAGhAK08g5bqQc8OETWW1XaSRZyeaywfGCBxuYx1vYuxOUOvPsQmYFmgOb0kWAW9SuXZv8nAE/vQ9WmoLZA6OUZuYJbGN76BMEzCTpvOzWmIPFozBsnxFsZYBrI5tESSzuvnIAjQbAsgLv2LeTPn/kCFVETBXV7UYe3y7eG3i6/AzJ8r10k3lWt19DMqJn2bKFIpeRe+pIRaFKiSv9w4NBZVP5Zhq9Q+fUs0AWSHVHhEFIGVUsvIUIm6cLdgklvJfT+EKPpRMNPw8f8LPH1vBFRsE4uqmJiYmJiYnxKEJrD3F96Tkr/Tc5WdIb2WqcET+zh5YpB2j8HVjkPZsymkkhztRBSXO3hGkk07IhW06BdZhFbC1CSmmCTtj2H5FQz3DJ7TwIwCUkYS3cohIkAIhJlGhi5abhSiANlRd8VlpwKhj5YbReoCL2hRT+XJVdfJNCtoW70bUtPVDfZOBbZmWskooDtUIng2QPJdnPEOxkqCdqNIWlPred4mwp2n30kxeOkqT4W/z9DKJO2nnyI1OIZoLNHzkSkSR/8RfqlE7RvfxJudVYEZQUBrxkFbuopILkIUoRUK5D79adyZGfzl5bc6vTEfRqKtPqPyLWRulM5cG78JZiYg1e+jMlUiKN+A135XDfldvwK1JVUKp9nKbVq5BK/9OzjxX6vlXv99qM2rviahKQNKT6i+qdqCCp/QDBAS0FTyn26B31XhE1ZGhVqELthZJWq6deWaIZTQei8EldC39NzbOFxWDj7+f4aTf+9H399PmFhUxcTExMTExOxhO+3v6IZHbjDNV8sN/uT1Jb5xcYU3FqqsNV3absCv9OU43AZbdslYAbopaHeTRNJFAxAmUVcnVS4jjBzF0MZ36kQA7U2EDEDoSBlC5BPWFgk3rhB1KsjydQAVo85Wyp9bx3ZWKBomeuDtCCoALQqJUmmicAN/8UVEoocwbLHyGBhui+SMRzToI0yHyvAxUv1LAASRji5CcuUsqXNVAu8VNs6+Svlrf8bgw0l6H+9BWAnSj/4myFWa5WvgurcrmQKIWl1wfJCSsNOh/eqrWEODcXx6zF6WXlW/25t0Sg61+V4iaaBtdsEOSQ8Gyj2afE5Fo1/8shp8215TYRVRBESw+JLa1qv/Fhore/uhJEowIWFjRsW0i4Qq90sWVWLf8Amwk0pUDdynXKnzf6AcLiut0vrsMtQW1XPhneWAb4MwtwYKb/VNbqcaIpV75rffel0jqYYI3yMR6ncSi6qYmJiYmJgPITvlfZUO+x3JpKbzb95Y4qvlOg9rJr8sLMa7Ifaaw+eQtDZqvBj43CIgAIIwornaJhnWscNpTGuIIb1KO2wSVdr4oUSniJk5jp6fQkYBwkwCavRnVL6ON/cDrH0fQWgaUbeGP3+KsDyDCkHfiwRCKWmbSdKd27N0tpfTADOTJvOpT+IvLaPnslx48BdZXnyBw963iIZ8NB8S6xGHKtM0j2TU+teAPoG/0SFwI7pSw/A9cLtUzjVIHj1COr8J5/4AOf8SYS0EeUefVKSSATEMtHQae2qS/Oc+F8enx+ylvgSkYepZ/NlXiVJ5Es9+Duf8K/iT4/DEpJpBtX5ZOVK6CYVJKF1AfcN3lQvK8C3ixoUSVdlBGH4YWqvQLqsI9VQP9N8Hj/yNvf1Y5/9QzZEavF/1bvUdgr6DKilwfVoJu/VralhvFGwdS8Rde6SEgPw4FPdBZR7a62qWlYygZ0r1h8ld70NPqtlcdg7u/xV44t5zqLaJRVVMTExMTMyHiG0x9f2ZDS4sVPl4G/xGxIUoYsz1eLq2xFTgYiZ6CQVMJPsY13R8VzAa6Xh6yDlCsrbBfc0qRS8ikqN0Gy0ovYpXuo5I7UNL9UDKItIq4LURqT707Aj2/k8g/Q5Rp0Jw63vI+hJaqoewU9lxpeD26By99xDG1HOgGRhRQLjwItJZ5c74B03T/v/s/XmcXFd95/+/zrlLbb1Vt3pVS2rttrzImySvGIKxQ1gzGQg4bMNMZoYkEJZJhsyQgEkIWSbbJDyYmV8A852YJZkEQoAkgDF4l+VF8qp97X2trr3ucs7vj+puqbVYtiWrpdbn+XgIUffeuvdU6Xa73nXO+RxMpUL50UdRyRQENdobU+zQV7Fu26MkvQm8CUjsV+iGgMCt9yKZ7pgoSFCwWZakcmQmC2hjCBMJ4sAw/ZMn4NIk6bYS5cNV4rABVDBT9ewY1tbnUiWTNL/1rTKXSpyouReOTEGtgLd8OTrIUN29B53txrvhF+C666DriqOV+45sqw//81KQyNS3vygFDR3Q1AtXvqM+7+rIo/VwE+TrQ/uyfSdWDJytMDjyLHgJWHtbPXRl++DxL9fLp/uZ+t+VSVi2Gfb8qD4fi5neqFkmqoe6W36j3lt1/x/XhxKmmmHdz0J1ql7d0BrQHlz9Hnjz/zib7/KCkVAlhBBCLEKz4enIZJmWomGp79HUmuRfRiZ56uF+/GLEcuBSnaDRQGQMy4MarZOH8QpDdHdchmroJGFiqoDFoQ24KjfC5QNbWdXUxYbMMqxVGEcTDr5AvPd5vJWvxV26CWyMNXG9R8nPoLwUTutKnI4N2MoktpojBKKJ3cQTRz+WzdYjm40sqqEdtEdYGMRtWkrY2I0zthtFPD9YGYMtl4mGhnE6OqgcOEDTgYO8cdU6Ku7lpHdsw6lWwfMgjml+fIzcVTByqIvuSY073U3SHSRcnSNuNbiTFeIDDpXhkLhSodpuqQ2CrVXq81OOHw2lAK3wVqyQHipxcr1b4Nl/gXV3kN7cC2MJwsHBo4tCH7vWlDH1gLXnRzDweL3nKj8Cpjb/nNqr/4y5CVj1OrjkjfWy68tvPHq+Y3ulTubYMuwty48+Pn67sfDkV+qFLJq663O1wtL8ghta13u28kfgynfBrfro85ddD+0b4KE/h9IYrLjhgqrudzoSqoQQQohFaOv+Cf7yR7vp7q+wrgzDKIyr6dSW96Go4FAFojii5Lk0W03BxExWcoxWJmg7/DC1ZVvo8FIk/UZ8IBEbXje8E2ffIzQs24yvs8T5AWxDF1TL6NY1uL2b0JkOMCGmViIceQadaKyHpaCEk12JCUqg3ZkKf0cDlUHNDf2zWuEsWYJu9kFbyPYRRVVseRylZubcHy+OsZUK0ZHDYCy2UiExOkp6yRJUSwvR9DQ6mcSUSiSqFQCuPHIdmewGdKdDed3zTC3px5g8QahIA4xb4jAiKviYcoKoxMnn2VtAKRLLl0sZdXFys/fFFe9AeR6ZVac5tu/merCZLZW+/ufgkS9AZao+zC7VVl+vykvVe6Y2/6ej13i57TpZ8Dp+uzGg1UzZ9qX1nq09P4aBbTMl2qnPm3LT9RB1svOueV39zyIkoUoIIYRYhO7fPcqqI2XeEDp0aPC0xljwjQKtCLBMzfwxClqyPi+4McVgjHZbRVWncScfJGhMEafXMaCgpZyjoTRJZA2qOIJuW4tq7IE4wJQncdKtoH1QDspLoJRHEFcgEZKOG1B+pj63ys9gqjni6lQ9QPk+xlpUbMBxsA2NtP7s7TTd/gaCwSGikYjxI1NUX3gBUx5CJ5P1Smml4ye9K5y2tfWiFrPDCa0lLhTwly3DS6Ww1SpYS3LTGwBI9VyHozMoLFGrRTVl8Z4rEC61hK0Wd2+R2LUYXUOhOKY2Rv2D7WxVtKSP29aOv24tpcceIxwYkLWqxJk7vveq99p6qGnurQ9BzR852rv0at9nx4ekVbfClg/Vy7k/83f1whduCq78tycOMbwISKgSQgghFqGOXMjPxC7tStU/a6mZgXVKYYEkCh/LvYQkPEVTi+Y1b7+RK97cQ9DfjykW0ZkMh0stDA4kqVQjElEFt5ZDK4Md30VoDSrZiilPYib2oFkLJqgP/QurmKBMAJRTMd6uB1BuY/1b9bBMXJ6kUuzHSzfg+S4W0KkU6WuvIfuOd5DetGleGPG/9S0KBR/nkpsoPfIIJBJEcVyvxKc0TnY1zszCv3Z2rR4gntgNxqCSSVre8maisTFKDz1EODINgHJ8FBqw+LVOtNNM1JdBlUt4kwrHWkwImvr7NjvMDzMTphwH3dyM39eH19UJtRq5b34TUwtkrSpxdp2qR2khaV3vIZudC3auAt55SEKVEEIIsciYyHDLQBUPXR9ONzdBSc3NV7JYxrF8z4m5tM1DRyErczWu37x5Xgi49/Ej7CoNckk6zROBwxXJkJ7mNJQKRNN7UWP1rhtFvfx51L/t6Jyqag5VGSO55kbM4w9gi6W5oX5hMk2upZ3kqpWsvGw1Smkabr7phDA1y1u6FJ3wiQoF/JV9JK+4AlOuUHr4YazN4rZfi0q3oxJNmLGdKD9ZH16Yd1DJJKkrr6T1Pe+h9NhjVJ95hjgqAmCVM9fblJpai60VqNGG2nMAZ2//3Gubm+xlAddBJX1QCretjeZ3/SJeNou3dGk9kNYCkuvXU921S9aqEovf+Rj2FoCEKiGEEGIRscYy/d19JCcDLPMnHx1dWskSYUlHNT5bGOfpwig/7uqhUItOONeqqmU8tkwc2I0bD9F6+SWYAzH2QBEVxHMhTc1cIdx/H2a6HzVT0c/LH2JJaT28+c0U/v4fsGGIVZpcQ5ZCew8Nb/8Fut522ylfz1zpd9XFite8kTVxAb936VwxiPK2bUz/cCdxPkE8NY7SLTity9FuERWlSK69lWh6muRlG1BaEw0NoRsayaxfU3++CtAmgWMtmAh/V0TCu5xoNCK0xwUiR6MzKXB8bBThLlmCKZcxUzlaPvjBucN0wqe6axc64ctaVUJcJCRUCSGEEItIcHCa8rMTHL+EjMViUFgsBSzJ2NKDpivRxqVhgdbiOA3JS0441/LDJRpLVcxUDdO/G92/nYr2INB4Jj5hPSmw9SF3E/VHWimK//IvLPmP/5HkJz5OdfcexkIIuvroXrOSa9704pPWHzs4yT1bDxEEMauDZhK9K1m/pA1QKK3IbNmC276e4qNDhGMT2FINt6sVr1NT/PFObC3A7+rE7+0FjvZ4BXv3wPJeoktccgenaToygIOP8pJgImx5ct5r0+0duA0ZdEsWk8sRT00BR8PkrNmwd+ycKiHE4iehSgghhFgkrLGUXpgkqoZHS5JTX6YzAkpYdhBRBG62EU5cI9AujnJZGZXobE3PO180VYPI0pSuUbOGMHIIikW8RII4imc6werFIXS6PrcqntiDxR4td24ttlym8C//TPvHPkZqwwYaBgZYt3QpyWuuZdvhHP1TFXqzKTb3taL1/JjWP1UhCA03p1K0D+ShkqOYC2gAEqtaAPD7mmkAoqlW3GwCv68ZsHjtzgnhZvbvysywvMvedQuFb36TiR98ndhpRyVajha5OIZKp2h517twGhuJpqcp3ncfcaGI19lB5qabjh6ntcyhEuIiJKFKCCGEWCSqB3IMPX2YdAQOGlBYFCGWGvBTFfIFFfCm1kauHA9JGY+EtZRNSHLZEjb3tc47n5tNgAumkgbrY40GC7paBWvq04va1uLPLM5rTYQFwondoOrlH7AWlUyiXI/SQw8RDQ7OFXEYHMpzT7mVIDT4Xj2GXb+qbV4berMpfE+THy7SA7hdaWzVEk3VSMwco7Qisapl7vHM1pOGm9nQ44chfP/7KK3RmQz+ihVEhQLhwScgDI89Dfge2oLT2EjLz/881hhSGzZIb5QQYo6EKiGEEGKR2HdwJ4VgGquTNJgEGo1FoT1N5CrGtCVtDQ9Vq9SaErw+n6fLiTgcjdEaFKk8vm0mICiCg9OEE1WctEdgAK1wWldjSsPEg8/O9UTpdCtoF5MfQDctRaVbMZMOyvfQmTQmn0cnk7idHYCaV8Qht/8QQVsLl3Q3sXMoT/9U5YTXNBv0pndP0nukTGvVoDxdD3xnib9sGV53F042i5vNQpAnHj1MXAiIazE6kcDt6pybHyW9UUKI40moEkIIIRaJEX8Sxw/IVhuwQIzFcTS+UnhJl5oPKxMuB8bLbHdj9nW3cP30Ia7e9RQdBY/c4AsAuEvWUdw6hA0t4dAE0cgQ0fgu8FqxTiO6bR063YYtT2DLk1gToZuWYk1EVJ6imG6iqXMJXtKDri4yN95Aw2teA9aSGxyYK+LQsmoFflmzcyiP72l6s6kTXpPWiutXtWH7WgkOThNN1eaG+FljT9im9ImzvE7nhHlQ11wDhx+m9MiDlHaNQ/NSGm6+WXqkhBCntKhCVV9fH4cOHZq37fOf/zyf/OQnF6hFQgghxLnTtKadh0Z/zOoDZZbnO2iwaXzPQWc8Btt9HuwfY2IyoBYaijXFVDnk1vwoKxo9uq++nNru3fUS4M4KbGjxujNUdx/AVGvgtWHjAOWlcVdehtIumIjwwP2EB+6fq/ZXqIwy/ZZ3cuXP3kA8PDRvAVxrDCg1F166rrmW+Lg5VadysiF+tf25ufCnPDVvntXLcdKep1WvoWHVa2h42WcTQlyMFlWoAvjsZz/LL//yL889bmxsXMDWCCGEEOfOtV3XwU2KwSsHIZeldawDpRXJtS1MTRRomJhiohJggchYYmN5Nk5zbdXAU8/S1pLBW7oUN5tAeYpwqAhBnmjgceJaibg0gZNuO264Xxtx/6PEpSTWQvKGm7jjtz+C4zontO9k4eXYOVTGWB7dP8HhyTKFSshYoYbSilvWLuH6lW0nFLGIpmpz4S8cKs2bZyWEEOfSogtVjY2NdHV1LXQzhBBCiHNOK82mrk1w3H8GrbGsHipwa6B5BofHMMQWPK14OruSf0r7rKPIlhsvZ+nMnKoGoPTE88RTOwiPPIiplOvrXrWvx7Uxurm3Xno8LqAyGTyt8XqX0vGBXzppoHopHjs4yT2PHmL3SIGBXH1+VUPCZfuRKfTr1QlFLI6GvxLKU2d1npUQQrwciy5U/cEf/AG/+7u/y/Lly7nzzjv52Mc+huue+mXWajVqtdrc43w+D0AYhoTHVv85D8y253xrl1ic5H4T55rcc6+e4OA0PYfzvCHhc2lgyfiKbWFAytN4nkPy8mt4oRrR1dPJVXEMgF6WwewYwaYqeFdcRvWFnVitCaMxTOF5Uh29EBdhaZL09bdT27ePxJYt+Fdf/bL/Da0xVJ56ivyjz5OZUFRS3Zg4wlGKpONSrQb0TxQJlzXNe55amiZ5XTtRrobbkkAtTb/ka8v9Js41uecuTC/130tZa+3pD7sw/Omf/inXXHMNra2tPPzww/zWb/0W/+7f/Tv+9E//9JTP+cxnPsNdd911wvavfe1rpNPpkzxDCCGEEEIIcTEol8vceeedTE9P09TUdMrjzvtQ9clPfpI//MM/fNFjXnjhBS655JITtn/5y1/mP/2n/0SxWCSROPmQgJP1VC1btozx8fEXfeMWQhiG/PCHP+QNb3gDnuctdHPEIif3mzjX5J57dVhjKW0dovzsBEornCaPhk1d+H3NGGN5/OAkD+8bB6W4YXUbm1YcXYB3tgcpGBigtnMX1eeeA8/Da2+n5d/+Aqmrr6by1FOEQ0N43d2krr4apfVpWnSi6e9+l+JPfoq/Zg17H3uan7Ss5V+7NzJdDlBKkfYd1nU18qHXrmHTixSzeDnkfhPnmtxzF6Z8Ps+SJUtOG6rO++F/n/jEJ/jABz7wosesWrXqpNu3bNlCFEUcPHiQ9evXn/SYRCJx0sDled55e8Ofz20Ti4/cb+Jck3vu7KrtzxH3l/CUg40N6eUtpFe3zZUedzyPw9MBQWg4nBvEdb15c5f8668H6gGr/Pjj8xa8VVrP7T8TqaVLqbkO8a5drFrSwOjl6/nhuEusYrRSOK7HVMUwmA/P+r0h95s41+Seu7C81H+r8z5Utbe3097e/oqeu337drTWdHR0nOVWCSGEEBeGaKoGESTWtBAOldApb95aTv1TFYLQsL6rkUf2TfCtpwaA+qK7x1bbezUXvD1+nShUF12P92OBsUKNYi1iRVuafCXk/z3RP1d+/fhqgEIIsVDO+1D1Uj3yyCNs3bqV173udTQ2NvLII4/wsY99jPe85z1ks9mFbp4QQgixIE5XIa83m8L3NI/sm2Bgul5x756t9TUfj6+292o5PrD17p+gszmBtZakq9nQ08zGZc08eXiSMLL4nj6n7RNCiNNZNKEqkUjwjW98g8985jPUajVWrlzJxz72MT7+8Y8vdNOEEEKIBWGs4enUbvK9Y3QGrazuuwS/r3neMbML7s72UN2wuo1dwwX6pyrnvL3Ht+nYRYH/4akBwshySXcTO4fyC9o+IYQ43qIJVddccw2PPvroQjdDCCGEOG88MfIEf7v77whNiOd6vDPzTjbpTfOO0fro+k/3bD3EruECvqfpzaYWoskntGnWbI/azqH8grdPCCGOt2hClRBCCCHmGywOEpqQddl17J7azWBx8JTHnqx36HxyvrdPCHFxk1AlhBBCLFI9DT142mP31G487dHT0HPKY0/WO3Q+Od/bJ4S4uEmoEkIIIRapazuvBeo9Vj0NPXOPZ1ljCQ5OE03VcLMJ/L7meZUBhRBCvDQSqoQQQohFSivNpq5Np9wfHJymuHUIG1qUp2gAEqtaXvL5T7V2lRBCXGwkVAkhhBAXqWiqhg0tXneGcKhENFUjcfqnAfVANfk3f0P+e99DuR5uZ31NyFdrLSshhDifSagSQgghLlJuNgEu1PbmsMZgKiHW2Jc0BLD8+OPkv/s9wqEhnOZ6mfZwYODVbrIQQpyXpI9eCCGEuEh5y5twMh5xoYYNDLVDBYKD0y/pueHAAHgeTksz8fQ0Ngrxli59lVsshBDnJ+mpEkIIIS5S4eE8QX8RExgcT2PywUseAugtXYrX0UEI6FSapje9ifR1153V9hljeezg5Lwy6loKaQghzkMSqoQQQoiLyGzFv3CySm3PFKYcoj1NVAzxfLc+JPAlmA1Qr2aRiscOTnLP1kMEocH36ueWsupCiPORhCohhBDiIjJb8S/OBUQTFawxKBROxiO9cQl+X/OLPv/4in/Nb3sboF6V0uz9UxWC0HBJdxM7h/L0T1XO+JxCCPFqkFAlhBBCXETCiQrR6ARxpYoJE3idjRAZEmtbaLhx6YuGIRNFjHz+Dyj89CcoFDrbQuPPvAN/xXUEA0WU76B9TQPg9zWdcbn13mwK39PsHMrje5rebOoMX70QQrw6JFQJIYQQF5Fo9BBh/xGs9VE6gykp/N42UutbT9u7NPW1r5H/7ncxpRIADq1Uns0T5QbA+iTWtGCrMdFUjWj8cXLf/CamFqATPvDyy61v7msFmDenSgghzkcSqoQQQoiLiK0NYWt70O0bMFNjKA8ymy4/7bA/gNru3VhA+T62UkGnWlGOh/aqmMAjHC7htafQzQme/9FOzN7D+C0tNE9OEhw58rJDldZK5lAJIS4IUlJdCCGEuIj4vUtRvovJVVGuj3IbUZqXNAcqsW4dTjoNSoHropwQnfDAS+G1p0muy9KwpZsdKuKJF/qJh4YIn3uG4pGBud4tIYRYjKSnSgghhLiIpK+7jtqhiNq+EsqtEI6MM/G1b9N02yVkNm9+0XlP2TvvBKC6axc6mcK/5BKcdC9uxwq8ttRcgYr+J/opu0niJZ2MOUm6dYBuaDhXL1EIIc45CVVCCCHERURpTebaDYQjz1HbO4wp5IlHtxHu/Ul934sM0dOuS9v73vei57fGsqpiiBtXUsmOoUv9eB2d+L29Z/ulCCHEeUNClRBCCHGRcZc3MRIN4I48h6pM4EWjRPkU4cDAGZ87ODjN8sMlGjv7qCaasKkJVt3Qd9YXBhZCiPOJhCohhBDiIrPt8BT3VUKumdxDdnKItO/S2NmBt3TpKZ8zu2jw6daiiqZqEFk61rQSDiVIXXY5mWs7X82XI4QQC05ClRBCCHGR6Z+qcLBnLd1vfTdD27aysj1D3y/87Iv2Js0uGmxDi/IUDUBiVcsJx7nZBMpThEMllKdws4lX74UIIcR5QkKVEEIIcZHpzabwfZdHGvvw71jFJVtW0HCS0uXWmLkFfE3UiQ2b8bobCIdKRFM1ThaXvOVN+INFwpEyXmcab3nTWW27MZbHDk7OW7tKv4TKhUII8WqSUCWEEEJcBI4dvrexOQGbl9Ofq84Fk5MN7ys/fnQBX5Xsxuu7FVAv2gMVHs4THClgQ0twpIDfkz9pj9Yr9djBSe7ZeoggNPhevVKhrGUlhFhoEqqEEC+JtYZcbhuVaj+pZC8tLZtQSpa6E+JCcfzwvau3dHP9tUcr8tX2504Y3hcODGBqAcn166nu2oXbOEnysjVzoetkoqkaNrR43ZkX7dF6pfqnKgSh4ZLuJnYO5emfqpzFswshxCsjoUoI8ZLkctsYGPgaxgRo7QOQzW5Z4FYJIV6qU4Wd2R6q0lOjxLmAxOpmwuEy4USFuFAgGhulNDWF29lB8tLOuaITcWTY8fAR8iMlmjozbLy+F8fVr/qcqt5sCt/T7BzK43ua3mzqrJ5fCCFeCQlVQogXZa1hcvIRDh/+a8qVgzQ1XU0cF6lU+8kioUqIC4WbTYCrGN07SclYTKXGVcYSzvRgxbmAeLpKda9FeZryM/soP/I8cW4aHIeJa27kUdVF7/4JNve1suPRfsYf7EdFlvE9U+wArrl5OX5fMw0wbxjhK3GquVOb+1oB5m0XQoiFJqFKCHFS1hoAHn7kdcTxyNz2Wm2ExsYrSSVlIU8hLiR+XzOHB/M8tj3PwTBi1/bDvNlGXFswqLESuZSm0xi8Yo1MwiManwCzDJUYoTrwLEceeZwfLrkO369/dKiOlFCRxbSn0GMV8iMlAJRWJFa1vOwhf8fP6XrKRtzz2GGC0OC5iucH8zSlvLkgdf0qKU4hhDh/SKgSQpzU5NRWAKIoh5r57GIBYyoopWlp2bRwjRNCvGxKK/anNPc6MfkgoGPXTvY9/QCljpX0+UtJjhhqaGoanGqMp2NAYeMENghYMnSIG0r9PKKW0z9VYVVnhvE9U+jRMimlaA0stf25U65fdTrHz/mazrpzc6ce3jvOd3YM0tmYkOIUQojzkswyF0LMsdYwNbWVwcH/x969n5+/b+ZvA+SDihSpEOIC1JtNEcWGLYcO877JMW6eGGPJ9u8zZIbox1DwFPs8qISGmtNKrBVheZKgqYVqqoHc/kNz85g2Xt/Lkpt7aextorMpSXtgKW4dIjg4/YraduycLxtauq2emzsVGoOnFZd0NxGERopTCCHOO9JTJcRF7tiqflFYIDf9BIXCM1SrY0ePOe45FZU+t40UQpwVm/taed+yJbQeGCXbtILKknV4Q9soTU1SbG6nHMfYAAY9zYDnoPyQpaqMTWfxuzpZd9U6rt+yYm5+0zU3L6eUSlB5buKMq/0dX+Bi7do2fkm30D9VIV8JeerwlBSnEEKctyRUCXGRO7aqX7U2QhhOUquNzjtGUQ9WFjAoMr4MuxHiQqS14raeFoY7WgmODLNEuWS71tHVuYZaKkMpH7DHiTjUm+K+UgXWXckvXNZLbv8h1l21jjfe+UYsal4BiY3NZ6fa38kKXFw/M4zQGMuGniYpTiGEOG9JqBLiIlep9mNMQEPDJQTBBGGYw9oAjvuuuR6sHFydoruhZ0HaKoR4ZY6tpLeqZlje20GcdDG1CrlkD8Y2MOCAjWMO2JgHR3O0NSRoSib4XtxFuLKTpr6euUA1b/Hdzcu5ekv3GVf7O77AhTGWrfsn5gUpKU4hhDhfSagSYpEyJqK//28olnbRkFlPb+970PrEH/lkYim2DFNTT1FlBGsjjvZLATgolUZrl6TXQjLRw5K2W87lSxFCnKF5QUjD24xPNplhKtvEE+UKa4bKBIEhUpZcWpHyNDevXUJHQ4J/enoQVyl+8PwQY4UqWMuSvc9xiSqyyzbQv2YJ129aflYX+AV49MAEf/njPRQqIY0pjw//zFpuXL3kLF9FCCHODglVQixS/f1/w+Ejf00QTAERhw7//+hofxPJVBeu20gUFahVxyhPHCKaqBKqaQJ/HOuE1GvY1L8Rdpxm2tquZWnPO6kFw6SSvVL5T4jzmDWG8uOPEw4M4PYs5dm2lXxrxxDDuSo3rG5j9LlxivkKNaAYx+zzLDu1IW1jRrVlb6RYkc3wuvUd9E+W2aQ9OlE8PFzgwUrMZRP7uGbHfaRszFW+T/eV3VizbF459FdaAbDe/npp9SM/PYw6XOSwirFK8dOdoxKqhBDnLQlVQixSxdIugmAKa6sABMEw/QN34+CjUMREgMYSQFJT/3UQUq/vd7SnKo7zFIvPotS76On+hYV5MUKIl6y07XH2fen/o1KqMBUpnl/5M6Qal9Icx4xVxriqaGmMLDs9aA8ta8OYAQ0jLuxUlpaUy1s39rC5rxV/sETTdEytEvGG2GGoIYU6MsUSX+Gs3UDL0CGWx4UTyqE3AIlVLS+r3bNhqrJrkqC/SNNgidtDTYWY7SrimcH8q/J+CSHE2SChSohFKpNZi7XhcVtjYlOZ7YQ65m8DNjrFmQyVygADA98km90ipdSFOM/t2r6L4dFpDjV1szaX56apgKIpYvwG2gqKhAEiQxgalKe53GpWK7Aaepo0l23p5QM39qG1Ym3Cp7E5xZ5mQ8tYhYlSRKGtg7Z8A635IXRrA37v0nnl0F9pBcDg4DSFRwcJB0rEpZCEVvgolmuHZ5XBd+V3jxDi/CWhSohFqiGzHq0TGFOev0NxtJzf8dtOygAhk1MPcPjI3TQ2XEq1Njg3DFBClhDnl5FMK4F2WTu4i7bEMtw4ojaym5buK2jEIa8tPoqSgiFl6dMapz1Fn1GsXZvl8plABeC3JWltSbIpNEy6LnpZmuY1q1k9sZZocABv6VLS111HcDD/iioAHltAY91ojY5cDVuLoRazUisqWjGmLU1Jj1vWyNA/IcT5S0KVEItULRjGdbMEwXGh6vhFp16iKJrm4MEv4rkt+IlWEn4nANnsljNsqRDiTBwbTHqzKbKbN/GTIzmWPv5TVC0mm8nSYbP4AFphrcXxNPHMIgmttTLukRypJU0sX7sagEdnq+61JNm4qRszXaMpm2Dd7FypNe1Hr/vUIL3NCTb0NhKNlvE603jLm15S248toLG/Zvm30zF+JQJH4SlFe3sDy3p8butq4n3Xr3jV3kMhhDhTEqqEWKSSiR7iuHjijlfcsWSJokmMqcxVEaxU+8kioUqIhTAban66a5Tt/TkafZeE7/Duzct53S/+LBO9LTT99FGaiHD9BH5TgqrrEZUDpi00x5ANyrRUixRKRxgeO0Th8iPU1M/y9ceOHC2ZvmUF11/becL1jw1Eq2uWLD5tCY/gSAG/J/+S5lT1T1UIQsMl3U3sHJwm1+TRVokJHYWrFGvXtPGHP7fqVXj3hBDi7JJQJcQiZbHUC08cz8fzWonC3FwRC+BFhv/NZ0xIEE6hnRSpZO/ZaKoQ4hWYDTX7R4sMTVdZ1Z5BlRX9UxXeed0y4uU/ywvVFGF/lag9RSbTwlRCcyDw0BM1/LyhWp6kLapxKDGEN/oUL+wYo5BpIwg76kFnKE//VOWk1z82EPkvTFIjwutrOeWcqtlCFMdWCOzNpvA9zc6hPL7vcGhpivJUFS+ICX3NZKND9phzHN8rt7mvdW6oohBCLCQJVUIsQtYaBgf/DmNOLD7hug20t7+B0cF/IqJGvesqfhlnjwBDKrkcaw3WGplXJcQCmA01bQ0J9o2V2DVcoDHlUazWf+63Hc5xP41cmUji5aFoatwXWfYlFI2E3OJolrV0oEf20jNwBOUnySWXs3KohqlZdg5O4/sOvdnUSa9/bCBanXJI4M6bUzUXgCbLrKpaVuYjwoEieJpD+SoPN2oa1rTwi5uWMTxdozeb4sh4iZ8O5bkknWBnucb6lOaaY655wsLDwPWr2l7tt1oIIU5LQpUQi1Aut41C4TmsNcft8fC8Jjrab6M6dYTJ0k9BxfV5VgpOrGBxcmE4SW76SayNUUrLvCohFkBPS5JCNSA9XOE/GJ/WlM9uX9HoOwAcGi/x40KZQU/REliyDT77HLiku4lHC1X2B5ZMNUJXhgnKh0g0rGVdYQMdmXYux+dgZ5rmda1s7ms96fVnt8/OvVpuXcz00V6orQcnuefRQ2RGKjAV4kQRzUozmTIcrroMjBseHJniP9y8ig/evHLuvPe3eBwOI/wWj9e3puddc95wwRfpRRNCiHNNQpUQi4y1homJ+7E2wnEyxPH0zB6NVh5RVGBw6O9IZlfjVJ4iZvqYoX8vtYqFJQxHKVf2ybwqIRaIsrCyAjfVHLqMQhcNK0NNc63+c/zcUJ6DU2X2xhalYGPNo7s5yc7BaX42ctmkwNMubvsVtKxcRTQZoEvtxLqRVteht72JzIv0AmmtXrSXqH+qwkiuwi2TIX2VAC8oEXtpkoUSvuexrDPLzcWAwp4p7I19KK3mB7WZ4X3Hmjdc0NOn7EUTQohzTUKVEItMLreNfH4HJq4Sx8dW/jMYW8aEVcbG/hXwsITUfw0cO0zQ4cThgM7M30e3WxtSLh8mn3+Wrs63zRWvEEK8+oyx3L9nDJUPSBkoY1EWdGAYPjLN/3uin8MTJVytcBTUIstQrkRrxqdtKuDKAmSVA14a62lUKYWb9InxiQ7kme5I03yasugnmyOljpnf1JtN0Vc23BRCm06gkj6BiSiVJxhxHFb6DVyJQ8t4SO1AjuTq7GmD2ulClxBCLBT5FCTEImGtIZfbxtDwt4lNQGvbTYyM/AvWxtS7omYDkZkZFniyIhZw8vlVLvX1qubvszZgeOBbRGNl1nT/VxIrs/M+VAkhXh2PHZxke38OU61xnfVYhgNYBozhXw9NEYY1DkyUCGNLbCwpzyGI4bEDk9weu0zEDumUpjHWGM9n0gSktMuwo0kp0E0ua/qaX7QNwcFpiluHsKFFeYoGmFfxb3NfK8pLkKWKUvUxxspETBQHWdJ9FWtwcBIOmcBS3VMPVadzutAlhBALRUKVEItELreNgYGvUa0OUakcJAwmcN0MYVjj5RWiOJ7iaADT1MPV7NwrQ2zLTJYeYOSpa+lSt7+kMspCiFdmtneo+tQol4WKHzR4fHU6YBMuCniciL1V6JqEXLFG2tNUopiWtAfGsD5QbEj6RMWIaWtoaU5QDF1KjsaNDJG1jLqwfM3pvyCJpmrY0OJ1Z05a8U9rRVsIDmrmV4Yl0i79vZeywWsmicI1YGML9hUuoCeEEOcJCVVCLBKVaj/GBCSSyygUdhFFpZkeqdkqFK/sQ4tSPsxVCXSpB6zZ81mwoBxFjeGTllEWQpw9s71DS3NVNk5G7ApiHtYx202MW++swgSwd7SIoxWbVzSQeOFZ2kcm6VzSR7fuwi1FoCHoSpP9mT6GhvK88NQQQTmiaC2XXtrOxutPv1yCm02gPDWv4p81hvLjjxMODOAtXUoVW190GEApjrgOB1PtXBk64GgwFp3xSK6VYXxCiAubhCohFolUshetffKF7ViqM4Hq+Op/L5+1MVoncJ0GoriC1kmMibA2mDm/gw4bSdCFe5o5GEKIMxNN1TChIeysoCen6PFLbGjvJl81GGMYzdewWKytf/XhPPs0m194gKSJWJKv4fY2cqi1mb6kz6qeZuJcwKVdzQQ/l6E/V31Zaz/5fc00zLRpdk5V+fFt5L75TUwtAM9juvEOMiSZ/SJmEMuyhiTtZXANKK1IrmkmserFhxoKIcT5TkKVEItES8smAMJD/4dKZYCXFqhmh/MdTzFbnML32nA9F9/LYolIJldQqw4QhtPUgjEcnaEz/SY6192Gf5o5GEKIM+NmE0xFOQ7sPsS0qXIkvZOh2irWtVyFwjKSr2HrHcg0pjyWTUyTtDEj7cvoti6NsWV9PiIuWqaicRrHyzi+w9Vburn+2tP3Th2/+O6m5VkgXw97ZoritnEMm9BLUhzY+zCDwShNjcuIAA+YcqE7mySTdtAZF1OK8HoaZS6mEOKCJ6FKiEXEmIg4rqFmvqmec9LPK4pTBy+H2VDl2Ea6u9+G7zcRRyVqtVGCYAzXa8b1MnR2vJVly94nCwALcQ74fc08umSQpyf3cASX5xNH8MIWLl/axFCugqMVnqMx1nDj6jY2dl9K6eCzrK3EtPesJuGm8dCExhJO1cilXJQDzzw1RJL4tL1Us4vv1oKYQhBxe0OGG4uW1oRLnKsRjTZinRRR2ZLNbuYxG5DEksJSAQ40edy0pg1noIwNLU6Lj9eWPHdvoBBCvEokVAmxSExNbWXnrk9RrR5+ic948TlWOq6HpLA2ipmsEC1RDA5+A2MiXLeB1tabWdJ2Ky0tmyRQCXGOKK14ymp+kNyLsQHEHpflu1k2UKFUrpFyHWJrWdrawLs2L2fTsiv5B2OItxdJpjvxtIOPwgeMtZjhEkMpzYMEPPzdMTobE9yytoP3Xb8C1z3x53p28d3GlMfTA9P0j0f0W4d4TZbmUkgUQ02BZxU62UKgY75CjU40IximrOXfdqdo6G2eN2xQCCEudBKqhFgkJiYfoFodPktnizCqXjHQYJjMP0y1fIQgGEFrjyjKo1Bks7LorxDnijGWRw9M8PTeLNXSZpQ7xZW1Xt4SrWDpeI2mMGY6keDRMGDNkgybV7Tiuprn2teQbhzhUhzUzNwmhcJgqSpFf0Kx24O9A0X2jxZ5uj/PgbEiDUkXlOKWtUu4fmUbWqu5xXd3DecByHSkCEcCouEyTsajXAzwrEYrBWguQbGVgBEMm3Fxc4Y9jw9x0y9eSWKVDPkTQiweEqqEWCTU8RX+zvjzSn1BYKsCAjVEEIwDFmMCQGOPudbsGlmVaj+pZK/0XgnxKnjs4CR/+eM9HJ6oEJtVAHTg4SrFC3FMSyXClkOKbsy+8TJbD0ywa6TIt7cPsBq4Doc+NGYmUIVA0YWq79B2sMCGWPM8McuimMmtQxxMaPb6sOPQJCNdrWRDQ5vr8O+bG3lKufwoWWKna4naPXp6W2hem+XZBw/TtHOaJiyjGFwUm3DZgGYZDspa4oNlgoPTsvyCEGJRkVAlxCKRzd7Ikf6/wZhTLer7cjigPQC0k6QajwABs0lNKQ/fa8dag1J6bo0sYwK09mfaI71YQpxNRybKDOeq82ZCDmOoYWkthfiVAt3TB9iY8igtuYy/3XaI6cce54axEaqppWxvWkGr65JRGqNgAotKu1wxHbEydqngMI5hORoHGK/Bt0xM7+EyjQcDko6DBlJtKd7SmWHjhqXsTSqK1YhdCZcShszKJsK9BdzI0IuDwdJI/cNGBYsCmg0EE1WeYoL+yTKrKgZdCBnRlua1WTbP9IoJIcSFREKVEIuAtYZiaRevdC2qo9yZUKTRug2A2OZRqjZ7JcAlkegiX9hOLreBbHbL3BpZDQ2XUCzupFLtJ4uEKiHOpkItIl+LMMekqh3EaBtwbWGMayePcE1YYd1UiUfHj9AeFHljZFDZNUzFScIgZFK7PK9jupRiyIE1pYhUzZJBYVGsRuOgCLC0Ar8YQgOKBhQqMmilKWkw01X27q3yUMIyWqjR4Ls0jFa5ZSpitVXUUKSsxUPRbTWhshiliBVUNOyq1Pif9x6kMxeii7AEaFaKnbsn4U1w/eolC/U2CyHEKyKhSohFIJfbxsjId89CL5XFmBilIsJw4qRHuG4THR23UyrumgtPyUQPUVRgbPxePLeJZKLnDNshhDheQ9JlZWuapKMYnK7NLr/Nk8RcVZ6gNdNB1c/QXZ0mO/A42lp6uq6k2rKSbifJYFytz4UE8kmHkrIUA0MSaEJRm+lJMtQ/HGige6bXqoIljQJrUNWYXYUq/2JqPG5CXKX55XSaGycNTTj1wcEWNGpu6fHYKgaUYbdjGU1FNDz4EF17D7C6dTUptxNHa5qUZs14QG73FEioEkJcYCRUCbEIVCpHCMNJIH4Zz1Izf46uVaW1jzHVmXLsJ5sT5eB5LRSLO4miApMTD1Iq7cN1sgSlSYIwh/UjrDnzRYeFEPMtb03TnU1RCSMqocFzFCOFAACdbkOFBpMfwEk04aZaySUaWJ5oIY3FdTz6XJ9xa9mnLfubNJGxZMOYpALf1mdRekqjLFgsAbAPQx+KCBjEUF6SpHlFIz8+OM5h5dAQwqqy4eaJmJa5iZz1MIW1KDVTaVAZIgXxyib8wRdY9+S99BYrNBdyNKx4PRk3xaQylAGbq53k1QshxPlNQpUQi0AUFYmiAi9twV9grqiFmiko4VLvpQo5OoTwxDkNSvks7XkXQTDB2Pi/ks8/h+O4EPlEcQFlNeXqNP1776G17QYpViHEWWKMxRpLb0sKrRRNqSoHxktz+59oaOOyWkgm2cBwHJKzUT3kpFtwvQzKUSSUph3LSg9812MiDHmOiAGlWW1huY2JghqgGNGKvJskQnEEwz7HMtGR5M1vWoPWmuHcNMWxEtZaXuMnaZzXSV7v8bKq/jvEKujXOWI/QeOgQ89URGe6g60pA2OHGW8bpLdlJVZpii40NCfO5VsrhBBnhYQqIRYBx82Q8DswJsKYEi8+t0ofs99gbYjjNGGtpf5dtYu1VU4W0LT2aWzcwOTkgwRBvWfMxAZjq1hqWA1YS76ynVxumxSrEOIseezgJF/bdpggNPiu5g0burh/9yi5ckgYGbYri00m6CLFiI1Y2X05b/AamXCTtLoeLrpezzOhaY0sibGAZgxu5OA6iuaoSmMUE47vwU230hjkeaphCUOtHQwpi7+igV9d18PaqRg36/Hh163h/t3jDE5XWT9Qwykdm6oUqJnfMbY+59OzFRLWYXnN4nqdpBrh0rFnGNYuD6dCki2KdDnGNHn82/XZuTMZY3ns4CT9UxV6s6nTLk4shBALRUKVEItAOrUcz2+lFoxhbQJrI5RymB3iZ20AOECMUj7WxtSr+QFY4riIwsFiOHUgc3DdRoZHvkMcV+rnt3H9WiSwxGANoFHKl2IVQpxFs4vuXtLdxM6hPE0pj/9462pqkWF4uspUOeSZSsgOYqyCzpZeJnF5HMNrMGSBFBq3ZkljMLGi5Fu6AWKYrE7T5WbwsytQ2iWtHC5VHs+4EHc38Ktru1lxpEQtLBJ4imu3dMPaNp789h4yUzF2Zu2rWZZ6rjI2ZmL6eQqlFxjrvI6WdDP7E0kuzbQz6m7gR3YNo0vXks34hI2Wt27sYfPKtrnzPHZwknu2HqqHSa/e8339qjaEEOJ8I6FKiEWgpWUTTU0biaICjpOkVDxIJtNHbKqk0ysJauPUgiGCYAxjYuD4ghbxzLpTGkeniU3huP0Kz23FmIhyeT8Kl2Symygqo7VDS/P1TI8/TjUcBDReupFyaT9TU1tlzSohzoLZRXd3DuXxPU13U4IXBvMkPIfulhTNKY9dw3kCA7GxjGAIsKxCcxDLiDZsNPX17OqD6ywbAsW0shSwOH6acnmKhLE4XoYgLqCTWdanfEodDeSGSzTmarSvzhIOl9j5whj7nhjmtrIhMVM5EGYHFltiFNpaCqVRvpP7CX0TJYb8NTiJmGVKMZ5I85OmPl4g5pbuJt7U1kynURir+Ycn++ltTbO5r/WEMNk/VVnAfwUhhDg1CVVCLAJKaZa03UqtOkC1NozjJkApMumV9PS8C6U0Bw/+H4JgAmsjmKvJNdsrNVuswmKJ0DpBHDv1PTpDW9uteG4z5coBsi03UCztpLn5KtKZVSQTPVgsrttAtTYIFoJwjFJ5H9VqPyBrVglxpjb3tQL1taoKtYivP3aY+/eOE4QxsbG0pD1ia/G0IuNpJtM+380FdFrNEIY7jEcFTRlLJ4opDGUs2zE841u6/TSHTJFNsUuTn8HJrKOmNbViSPmJEZ7JJIiMhn1gXM2Dz+e4sRyTmiloo7BziwobLMWZOZrThSGuqixlxI6wVWueViHXtKR5rlRjexzh+Q6vzWS4bipiMlflwHSFXc0OP232eG5wmqf7p9k3VmSiFNDVnKA3m1rIfwYhhDglCVVCLBItLZuAeiXAKCqinRRxXGJi4gFQllowShxXOFoh8NhgNRuuXKyFpsarsPiUitDR8RYuWf+bDA5+g2JpN1O5R0j4nbS1vYZsdgtTU1sZGPgatdow1oYkkytw3UYaGy6VNauEOEu0VnPD3u7ZeohHD0xSrEZ4WhEamCyHGAOeA72tGVpSHo/nKzyLJYgNGxyHTbGlaSb4RFgGlaW/1WPYsfQbi+t2s69sKVpDH5oQy/U1RYSiYmOeSRmmEpbDUY2hyQq3487VELUoYiw7MWSAESxJ18FpW02qeSl7wgK72zppb/LoXd7AyKBhmeMTxZaW0GJDmMi4qAnLJekE26fLfG3rIUq1mFpUn9/5c1d0z4VLIYQ430ioEmKRUEqTzW6Z6xWamtrKgQN/Sbm8H2MDwrDIiSXXT5w/pZRFawfXrX94MXGOwcFvMJ1/Cq09rA1pbr7maIir9lOrDROF04RhjjAskkx0UCzuRGufVLL31XzZQlxUZofDtaZ9JooBYVz/GY5n6spYoBLE+I5GK40GNjsuNaV5kAhvZj2qvRjGtWUotvWpkMCAsayxihSKIpYlKBrRFLAQGgYxfPXwKOUg5ldtAm/eLKq6VWgCLKO+xtWaPYlGlsWNVKNmUBGRMRRqMYVaRBRZXFcx7WtUZGnLReRdxc5yjdBY4tiilcLVUKxF7BrO89jBSSlWIYQ4L0moEmKRqlSOUKkOEUbhTEXAF1sYuP7BTGsfR/ugPIypF7IwJqBY2oU1Aa3ZGykWd+J6jXPzpFLJXqwNCcMcnpfFcRtpbLqSTGY1qWTvXPgSQpy53mwK39UoBQlPE8WGY5eFi40liAxT5QBjLRtizVu0R4N2KOiYexOGn1RqKMCN4ao89GqHQ1HET4mZwqELzXI0V+JgqfdulbEciiPyYYwCetDEMDObqt5LFQM1wEPTlHRoa0igcxWm44icB5d1N6GVpjntsrQ5RUva4/BkmYdrFUinaHA8thGww4a4WlONIiZLEdaC0ortR3KUg5jnB+uFOqQaoBDifCKhSohFKgwLhOEkxuRfwtEKpTw8r5l0agVtbTcznXsWqAethsx68vmnKBRfII6KlEr75opQtLRsorPjrQyPfAetPRJ+J0vabpV5VEK8Cq5bnuW7Tw8yOFWiFta/DLlqJggNY3jaxuTKNWqRxdGw3HFIohlNarJlS0tgcBQopbjcaH7OuCSM4ko8DLCdGIi5CoeemcXBK1h+RMgTJgJgIw5tKAwQzwSqUQxJFBWgSSuGjOFRHeJ4MODC7ljRpzRdLUku7WqmGhqGc1UKtYinB/I8VJvEUYqxYn3h39hYfEfhOprGhEM1sixrTTM8XeU7OwbobExKNUAhxHlFQpUQi1QQjvLi61Udy+J5S+jqfCttba+hufla0qltHDgwSnf3O2hr28T09KVMTNxPPr+Dcmk//dUB8vnnCcIxsNDZ+WZct5F0arn0TgnxKjDG8tVHDvLtJwcoBkcD1dvw8FEEMz/v28P6MN8ohsPEVLVDZ01R0Zair3GrEMSWTbj0ojmAIYOiCw3EaAX9KcX37GE64oB18RJWxc38Ah7fImQzLhbYj6EFxbPEfJ+Q9+KTQTFhYn4SxuzKVynVIlYuaaShYmlMurx783I2r2hlQ08T33pqAIDmtMfTR6bBsYQz86diC56GpOeQ9l0cxzBdDgmNxdNaqgEKIc47EqqEWKQUGk6Y8cD8nDWzWymfluZraW29mUq1H2sNdmaiRbG4i1pwmGLxeaamthFF07S0XE+teoSBwguYuL7YcDq9ipUrPyw9VEK8Sh47OMnXth6iGBydG9mFxkexD8Nq9FwwmvUMMc0JwwrXYW8t5rnY4LkOG2LYgKYNTSuKIxhGMFyFw1KrKXvDbM3cy5rSpdxUWIaDYhM+S9GsRNM6s4j4oLL8QIc8GccYAjrRjGDYHVtUVVEJDc8P5WlMutQiU58j5WquX9WGsZa//PEe9o0W63OtqhHxMb+fotiSSrusaEuxvK2By7qbKAUxTx6enCstL9UAhRDnCwlVQixSra03Mzj0TeLja1PMsoBSKJUkk15JU9NGBge/gTEBUVTAGBf4eY70/18cHVMLRmYWDbZMTPyIRKITa2McJw1AGOWl0p8Qr6L+qQqlYP4P9PDMelSrZwpEDGPm7Xc9zS4fno9CNriKW2uacUfRhKUEPE7EKjTPY1DA+/FpRFEqNlN221lR6SVlXKoYmnDZiENl5rpZFHu0YZensHF96OBGYCkaIsMOYpQCR8Hq9gwZ3+Gnu8fon6rQm01hIku+HJGvhtQiQy2a37NugNgaXK2ZKgesWJJhc18rl3Q38sCecbAWayzGWJlXJYRYcBKqhFikstktaN0ITL3IUYqmpo2s7PsVarUh4riG6zUynX8WaKgfYkOMtVgbo5QPRBgTkfA7MSamWj0EWBKJTqn0J8SrqDebIuM7c6vKAeyY6ZWanVO145heKkdBxnfoaEyytBDzM9V6SAli2OkaosjSqDRDyrKdkLeqgHWxRxlDS9zAtdWVhF6BZLCcRjQW6AQi1NyaVBOzRW6AK3H4+ZmhiKECR4U8o2JSvotCUQwidhzJsX+0iO9pFIrB6QpBZCgH8QmDlSMDY4WAZwemaU773PfCCP1TFfKVkCMTJUbyNR7ZP8FbNy7lAzf2SbASQiwoCVVCLFJKaVKppdRqh4/bcezfhiAYQ2uXVGoZcVykUHgGa2rY2Y84ygXKgMXaKgCOk8BiaO+4jSAYR6Fobb1Z5lIJ8Sra3NfKnVtW8DePHmQoV6UWW7SC7bZeXGKWAq5WDp1WM1axqKxlMw5rXM14oyY3VWU6ivlHDDcoF2NgtZ9jTZzGR+EoS2AVcbWbnc4kt2JomBlOnERTxPDCzHyqXGyYjiNcregxGl9p9tqYVVbTqx2msj43rW3nip5m9o8X2TdWmpsPFcQGYyzWglaK2M6PVRqIrWW4EDBeChkv1ljZlmGyHFAOYlxHUwsN39kxyIaeJilYIYRYUBKqhFjEViz/T+Tzz2JM4ZTHhOEk5cpherp/gaamjQThNL7fRqk0DEBr62uYnPwh4FAvy+7S2HgVrpvC85pZsfyD5+S1CHGx01rx/hv6ODhR4h+eHIA4njcHadZGHN5i6z1GsYVS1aGnEpPEsNrCLq0YsvX1qdqNYgma9rAVpQOmVECzdVEYOuJWJuNmptFkYCZWKXw0TRgmtWXIGDbi0G00aaBm63O7QgVOa4Lfe9ulbFnVhtaKR/dP0J87NDcfavPKVkbyVQ6O1+dlHm+2N84BImPJlUMG3QpT5ZAoNFyBQ5/nEhYjjoyXJFQJIRaUhCohFrG2tptYsuR1jI5+5xRHKKwNiKMSSmna2l4zU91vD9Z4ABSLO4njPEqBtQ5KaaJokoS/imSih6mprVSq/SQTPQBUa4Nz61PNrmUlhDg7Hjs0yb0vjMzNrdIKsKB1vWKetccVr7AaNV4hpzTFjEtvZNjrWJ4zhltxWIKiSYU0WA3Gp6QDjHHxrcP1aPrQ7CCmAU0ayzSWMpYRB36gIrSBt8wO+cPyLDEVBaPK0tPdzA1rlsy1fXNffUHx2TlV1y3PgoKvbT3EVClgshyd9DXP9sFprRgv1ogNXK1d3mxcEqFCFS3uUPnVfNuFEOK0JFQJsYgppVna807Gx36IsSeWHlYqidYN1GqjWGtoadlEU9NGwiiP63QyNQW1Wj/GVJn93rheFbC+fs3AwDcJgjEct4FabRBjanhelkSiC0AqAQpxlj2wZ5xc+ehC3sYenTsVRJZKZE4oXrGfmEatcaqGgxoeNDUqsWEYhadqNKGYdCsYDAUsHmnSOLhYutE8Tsw91LgNjwgYx/CgD08FEW/AxUexH8MqNCXghyrCcxTXNNcr8xljeezg5FyY+jdXL52b/9SY9GhNJ1jZluHHO8eOK7NRpwCl6j1lUA+O7VbhoRj04BLXIR2e7JlCCHHuSKgSYpHLZrfQ0LiRfP7ReduV8lFKoRQUCk+Ty20jm91CW9trqFb7KRT7AcikLyFfGMNaM1OoQhPURglqY8SmiDGQTHZRqQzM9EzVP/pUKkcAKFcOE0clXLeBVGqZ9GAJcSasJeFqwtgwlyMU1GLDzBJPJxSv2Kli9ltLj9WUE5odNTt33L3+CLfFDSRMipqqkFcOvdbFAwIgwhIDf0/IXszcOfdF9VmXgzMBbtUx1QeNtcQGKlGMMZat+yf4y/v2kq+ENKU8Pvy6NXM9WMVqxMB0hUMTFq3BnCQb2Zn/0VrR0Zikf6rCsDWEWFYYBy/h0NzV8Oq950II8RJIqBJikVNK09H+M5RKzxPHRcCiVAbPa0DrBC0t1xKFhbly6LPFJkZHH2R0BKK4jFL+zNpV9eE5UVxBax9jAqyNqFT2A/XhgdXqIFqnyBee4dCh/0MUlzAmIJNeSTLZDUgPlhCv1GvWtrP9SI49o0XKtYjGpMtUOSTju0yW6j1Ylnp589mBcylHcyTtMOJo8pWAShTPHfctVWKZX2VzbRlJm6Y1TlIlIIFDCUM/mm1EJ5yTsN6DtJ161b5jqw/OfmXyTzsGAUXKVewfK5LxXcaLNe7fMzYXqhoTLkubU7RkfJ4fyDFaqHGyTqeEq0j7DrWwvpbV7HUvcWH9igYaO1NSWl0IsaAkVAlxEfC8Zhoa1mGtpVTaheM04fstuE4TcVTEcRJz5dCV0mSzW8hkrmLnzn8hk1mF1iFRNEWlchBwMKaKMSebXB5jTBXf72B8/MfUaiMopbEzVb2MCWQtKyHOwJZVbfy6Vvx01yjb+3NMFgKmqxHlIMZzFCta0wxNVwliQ0djktmf0e7mFJ1NSXYO5ykFZVxHUQsNl1VXs05pMtZDo9HADv8wjTZBSYXsi5KsNc1sxsUC24hmwtVR88IW9SGJ1lgK1ZjvbB/ksqVN9R1zlUePBp9lbWm6WpIEoeGqZVlCYzg4XmQkX6MaxriOQzWICeP6elSTlfoXO7Mhb4+FqTDgycePoBwtxSqEEAtGQpUQF4FUahnJRBel8gFcp4Fs6/VEYYF0ZhWZzOq5whLHmh2i19X5JkZGCkzlhlHKx/c7qNWGqdfkMnDCLAhDofA0UTQNMNO7pahUB7BERGFhZl4W5HLbqFT7pbCFEC+R1orrV7Wxua+Vxw5OcnCixPODeQ5PlBgt1ljalGRJUwKFojHh4ruaq5dnaUp59GZTPDuQ44s/3U8QxGjfoSvQ+NbDQVN/lmFt1M6UioicAleQoBcfjSLAsgGNJTghWM2ajUvWQsqv/zx7WrGyPUOhEtLRlOGWtUeLV1y3PMvzg3l2jRRIuJrJYsDKJY0saUxhrSWMLYcny2gFazsaeXDv+LzrVYOYhoRLoRbRP3XivFEhhDhXJFQJcRGYDUwTE/eTz+8gCgs4ToIlbbeedihec/M1uG59rtTE5E8IghGUUlg7O38qOO4ZljAcp/5dcv0jltZJtE6hlMf09JM0Nl4KwMDA1zAmQGsfkGGBQrxUs+Fqtmfm2GIQPS1JlIWB6Sq92RSb+1rnhsVdtzyLVppdIwUq1ZCWF6ZIRfV9JSwJLLGxJByDG6UJjU8SRQWoABkUXfUVpFDUy7f3Ks2ANWwnJuEouluS9E9ViY2lMelwy9p2LlvaPFeoYrYKIMDjh6d46sgUQWgYKVTxtObGNUt4YSjP6vYMqzsayVdCnjw8yYGxEq5m3vDAGPjX54e5YmkzvdnUOXnvhRDiZCRUCXERmB3S19Ky6YTeoZf63Obma3n2uU+Qyz2C67YSx2WsDYiiPNbWqAeo2eGA9pg/CqUcfL+V1uyNFIs7qVTrRTBiU8N1GykWdzExcb/0VgnxCs2GrNNxXc0Hb14JwJMPHqZ/X5lKFNGAooYlQPGUE7LO+CyxScDBBzwggWIX9eqCUA9Ub8fDs/Vy6glXs9M1pDyHtV0NdDYmuGVtB++7fsXMFzMn6p+qEISGS7qbmCjWmCqH3PvCCE0pj9esbWfLqjYe2TfOjiNT1GKDoxWhmT/suBLEdDQl54U1IYQ41yRUCXERmQ1Ix85psta8pGF4Wru0NF9NPv8kcZTD2BitPRQOFgelPKwNqX937AD1YX9K1QtaVCqHmZxSJPzOuflbcVSkkH8aa+u9aJXqIMlkN22tt5DNbpGAJcSrxBiLzge0pTyeVpZqKaaAxQcyxqGmDOM2BBQZ9NzXJQdmilFAvTiFh+IAhtVK0x4rDmdcbl3fwa3rO+b1kJ2qDflKyEihykQpoBREVIKY2Bgaky5WwWMHJ/mrn+xl/2iRfLVG7B/ATU9hoyxxuQ9m2tbTnJQiFUKIBSWhSoiLXC63jf6Br1GrDWNtSGfHW1m27H1z+601cwv81oJREolufD9LsbgXYwISic564QpbHwYYhtPMzrNSaOo9WDFKOVgb0tx8zVwPWWPTlYRRHt9vpZB/lkq1H9dJU8g/PRcAhRBn32MHJ7l/YIr1lYj2wFBOaP5ZhRTVEbr8IzTgcGm1h/W1ZcQ4HMHQgsJHzQWsYQxGwzocjKOYUDHX9bXxG3dcckLAOX6tqtk5YU8dnsLTmqlyQDmICGNLJukTxZaByQr7x4scHi8TxpbYP4jTvBVUCLa+OHlcXkVDwuU1a9vP8TsohBDzSagS4iJXqfZTqw0ThdOEYY7hke/Q0LCeeGYe+sDA1ymWnsKagCgq4GgfhSaV7CaMpgmDScDiuS24bjONjRmKxX14XgvGVAnDaaytkUz0oJTGcTNzPVBL2m6lVh2gVD6AsTGum0GrJNXaEEPD3waQIYFCvAr6pyrs8yFY4tIwFNLR4NPhODwRD/BM8jlstZuNjeNEcYZrow5aUERYDhxToOJgAqZXtlAZL7M/CBnzfV6/rBk4MUQZa/n6Y4cJQoPvaYy13L9rjGcHpmlr8ImMxXc0DUmHXCkk5Tk8MzjNQ3vHyFdDCrUYt2kSrUJMrRudGEK5U6Q9xa/9zGq2SNU/IcQCk1AlxEUulezF2pAwzOF5WbT2mJx8kHJ5CLiVgcG/Q6kqLS3XYKyhIbOGdGYVyUQP1hompx5CofD8NvL5HVgTkG25hp6ed6GUpn/gG0xNPUSlchjt+MRRae7asz1W4xM/ZXLyQYJgnCieBqUol/czMPA1QApYCHG29WZT+L5DcTJgDYrGUsybHc1a70p+HEzzbLqfp2OH7c4IH4paWYXDfmK+RTh3jtBYRppdlq/uZnDHIDo0fGf7AArFpV2NfG3b0RDVm03PzZ3aOZTngT3j3L93jKHpKoO5Cr6rac34hJGhKeXS0Zjgvp2jTJeDuV4vG2XBejiJIcBjdbaX337nJm5ctUSG/gkhFpyEKiEuci0tm+jseCvDI99Ba4+E34nFYkx9OF8cT2FthanJh0mnV9LW9pp5Iaet7Sbg6DDByckHsdi5sumOTuB7S0imeomiaVy34YR5XKtXfZy21luYnHyQQnEncVQgm72BUnGXrGslxKtgtqhD5ckRmiihEg6t4zWyZgmNhVtJ2UMMN6Zo18toRVEGWlBcgTNXTr0UGr722BG6mhJkEi6gGMrV+M6OQcYKrfNCFNbiuYqH944TGkPCc8DaepCKDY6CMLYoZXEMPHF4klwpmit3A8zMoQLHm6K3cSmfvv0t3Lhahv0JIc4PEqqEuMgppVm27H00Nl46F3KsNVTKfwfU15vx3CZiU8Hz22huvvaU51FKU632Y0zAofzT9XVmoimqtWFQkEmvIZVaRi63bV45dWsNSmlS6T6iuEw+v53hmeHKscn8AAAmFklEQVR/fqKLrs63obX8uhLibJmtFljDoRgOEY5VqGI5oqFBNbM6vpKlXVmaCjlStZCdJmY1eq6c+qzYwsB0DajhaUi4momiJrYW39PsHMrje5qb1yzhX58fYceRadK+w57hPCOFGsVaPTglXY2xEa6jyVeiE5YV1wp8x8GL1nJFdwu/+trVXL9yCUIIcb6QTylCiBOqAkZRldjcM/P/i8RxFVAUCs+Qy22jtfWGk56nMhOoGhouYWzsXoJwHGMCjKlSq42QyazFWkO1OjB3XLG4k4nJB6hVB6jWhqlUBoCIIJjCcRKMjf2QdGoFy5d/4Ny8GUJcRPy+ZhqAyu4pxp8bx63UyDQlsEnLkakyzcawzFGsNpoAO1dO/WRCA3FgqEVVDo4V2bJ6CbtHiqzvbATg6f5pqkFMNYyZroTEsSWmXsqmYmOsBROYEwIVgLGQ9Bw+8vp1fODGPhnuJ4Q470ioEkLME8cBjz/xbygWDwJvoF4a3aCUTxBMMTF5PwATkw+gULS23jxX/jyV7EVrn2JxJyg7V6QCFFEUkc8/i+IbNDZeRRQVGB37EQDF4j7CaAqtU8RxEaV8lNL4fgdxVKRY2rVA74YQi5vSisSqFvy+ZvY3OOzbMcSEtjyvIhoHA1Qp5iljyWMZmimnPrvobxea4Zlts0HIdRTGwlNHpikEMU0Jj6eOTDFWqOIqQMF4MZgXnCwQnTqr1dsJ9C3JSKASQpy3JFQJIebZs/dzlEq7Af+YrRaFQiuHanWYgwe/QLl8ALDk8zvmLRBcKLxAsbSLtEpQq40TRVNYGwHge60YE1CtDVOtDlKtjcysbWU4OnNCAxWUglptFK09tE7ODREUQpx9SiuuunEZQU+G/qkK/hOD9BRrONahYg3/SDg3l+oqHN6Gh49iNh7N7jPWkvJcHK3YP1pEKUU5iLisuxnX0ZSDGKXqIem4NXznLR9+vMaky1uu7JFAJYQ4b8knFCEEcLTQxNjojzj+o41SLkr7eF4rWEsY5XGcNI6TIYzyVKr9AExPP0E+/xRRmCMMx3Gc5OwZAE0UF1HKIzf1MJXqYaytUp+fccL31lhbL5ZhrSU39SRHjvx/c8UvhBBn3+w8q397bS+NoQUDu22Mj5qZSwVg6PHGSTg59rsT+DC3L+MpXK3wHIWxhrFCwHC+Rr4as6M/h9aK9sYEjUn3hEAF9Z/8pKfQM6ELIO1pbl3bxq+/fi3vv6Hv1X8ThBDiFZKeKiEEAFNTWzl48AtEcf4ke5M4TgOe10IQjAGWOC4DlkSik1SyF4By5TDV2jCe24KxAanUShQaz28jioo0NV1JY8MlTE49xKm/k56r9QXExPE0pXKZAwe/SLm8H60z1IIhkoke2tpumRt6KIQ4ewq+JollNfPnUjnpg0wkD9NQvJobTYKCrjJiHBxV73nSWhEZQy1i3uyrWmTwXU13c5LRQu2kvVKuVly9LEu+GpErBbRkfP7bGy/hJlnYVwhxAZBQJYTAWsPg4N+SLzyLIgGUj9sfYkyZVGoFUVSgselKlFJzc6pm15uKoxLV6iAVcwilPZYsuQ3fa8SYgGSym8aGS2bmR73cITwhUTTJ4NDfzTzX4DgNFApPzw09FEKcPXFPhu8fnKQtVnNzqQCUOwUqApMgZVx8q1mNZruNqUSWpqRDW8ZjohTMO58FlmWTNCR9hqerFGoRU6UAa0EpSLiKvrYMH3rdanzHmVs0eLb0uxBCnO8kVAkhmJraSm76ceK4jFIe4Mz8qdPaIY4r5KafpLnpctqXvPaEIGOtoRaMopRPMt2NtdDYcCnp9Aoq1X6isMD09JPUgmGsna35dareqpMxWDv7QU0Rx1Uq1SEqlSMSqoQ4y15zSQc/3TvGM2MlqpGd+0m1UZYOoNkqUtbHswluQ7EXw3bieol0a1mWTXNkskQhMCjqpdbTnsut69rpnywzlKvQkvLZ0NPIxmUtNCY9lrem2dzXKvOmhBAXJAlVQggmJh/AxBWU8mYKRzhonZzbb0wZcNG6kcamjVQqR4D6wsGzQ+9yuW0U8k9jTIVSaT+Ok2Jy6hFSqWV0d/08Q8PfwtqQRGIZhcIuOG69m5fHYm2VOC4QRcUzeelCiJO4fmUbv/3my3hgzziDU2Xu2z1GvhIRl/tIeUtoi5tJ4hICCQxdaLoaHfLViKXZFP/9TZfyzW1H+OnuMTIJF2Mt2tFzPU/H9kRJiBJCLAYSqoQQKBRK+/hOkiCYQiuX2ETHHRVRqx1mcPAbeF6WRKJrriJfpdpPqbgPYwJct5lK5RBxXCWX20oYjNWfHRao1kao1UZmyqy/lF4qZ+a4kxWosMRxlXJlgMGhvyeV7J0X8oQQr5zWihtXL+HG1UswxvKlB/bxxfv3kyuFFMI2xtE0Ua8RWgOGMVQjTUvG553XLeemte0orRgr1ShUQhpTHresXTJXDEMIIRYbCVVCCFpbbyaf30EY5fG8Vmq1EaK4fMJxcTxNpRKhZqp9TU4+SHVmwd9KpZ9qrZ84rmBtjONk5qoDTkw+QD6/gyicntkPJw9Kx9I4TgPWRhhTOukRxpQYHv4WQW0TWtdLwMtQQCHOLq0V//6W1VyxLMuRiTJ6oMDkU2OUagYH+KmOcJdmuL2ziUu6mnjf9SuAem+Xfr2S+VFCiIuChCohLnLGRBQKO1E6ges209iwgXLlMJOTj530eK0darVRYlOlUh0gjqp4fhOV6jBxXGJ2zak4LhCGLr7XzuTkg5RKe3GcDMZEvLReKkMcT3O6ohZxnEPrNLEpUan2k0VClRBn22wP0/Wr2rDXWiqXdfPTxwfYVQ1Yu6aF372hD9fVJ32OEEJcDCRUCXERs9awa/fvMTz8DxhTA2Kmp58ilVpBtuV6Cieprl4PTgpLTC73OFp7mGJtZvv8OVJaN4PSlEp7sDYgikKAmblbxw8vPGUrT7t/dOz7NDVdTRQWZCigEK8ypRXptVneuDbLGxe6MUIIcZ6QUCXERSyX28b42L/MG15nbYVyeQ/QMLPFoz5rYlY9OMVxCWNqKJXCddM4Too4PvY4CIJ+wmAIS8BsKXTQuG4zYTh61l6HtQGVykGGR/4RrX0SiS5AhgIKIYQQ4tyQUCXERaxSOUJsKifZE1Op7Jv5/6fqUbJYG2KtSxRNzQzrO16EnXu+pt7rpAnDyTNs+YmCYBxjqiT8DgAZCiiEEEKIc0bGxghxEQuj/Am9S7OODp2bv2bViSoYE76Eq2kcp3FmHaxXWkp9lsP8uVYuWnlonSQMc1gbkkr2nuE1hBBCCCFeGumpEuIiFtTGObEQhIvntRDH9e31cKVRiplFe0+klIO1p5v7FGNMgLVVXt6ivw6zlQKV8lHKJZXqJZVcBSqmWHyhfvY4xPOa0TpJZ8dbaWnZ9DKuIYQQQgjxykmoEuJipsDzMkQRM4v+urhuI45Oga0HmcbGjbiOg3Y8crknieMpjoYil9l5UqceJjjLYO3JhhqeiodSCq08jAlw3AytrbfQ1LgR12sgnVpOc/O1TE8/QblymDgq4boNpFLLpEiFEEIIIc4pCVVCXMTaWm8hP72DUnkPcVwmmVxJEAxRC8ZRKg1AGI7T1fkOHDeDiQ3V2gEqlUMAKHy045FI9FAu7+PMh/XNUmjt4/ttKKVROPQsfRfLl33ghLCUzW6RghRCCCGEWFASqoS4iGWzW1BKMzFxPxOT91OtDBHFeepFKOrzqCqVw+zb/2e0tt5AEAxRrfYzOxzPUiaOHcrlF85qu7TO4LoZGhs30tZ2E1FYJAhG2bfvT2htvXmu3UIIIYQQ5wMJVUJcxJTSZLNb5uYfDQ1/B08r4rhMHM8O54sxpszExAO4bvYk60udrndqturfS5lHVZ8/ZUyVKNJUyvuYVIpyeS9BbQKlIJ/fMdduIYQQQojzgXzVK4RAKU1b22vIpFcALgp3pkrfUdZWiaIJXl6RCfD9bhyngRevIAjzfx1ZFDHlykGmph6iUulHKY3jZAijPJVq/8tqgxBCCCHEq0lClRACgJaWTfT1/SrZ7BZ8vwPPXXLCMdYGKJV8yed0nGbqPV0VTvfrRqkEWntonQIUsakvLlx/niGKS8RxCc9tknLpQgghhDivyPA/IQRQ761qbb2BanWAUmk3ROWTHmdtMPsMwCGR6CKKAqwtYUyA1h7G1FBoXKcRi5kpxf5iPVUK31+C1g6VyiCzFQVdtwGAhN9JQ+MGUsmltLbeLOXShRBCCHFekVAlhJgniopUq8NE0alKpFtAoZSHo1MopWhoWE4qtZyJ8fuITQWlXBKJdhKJToIgx2xv01GK+cMIFcbEeF4Wx5nG0T5BmAMcPLfplJX/hBBCCCHOBxKqhBDzOG6GZLKLamXyFEfUA5K1AagMWqcxJiSKCmQy60Epgtoont8CuFgTUg9Rxxa0OH5eliEMR4micayNMcZDKYvWPolEB40Nl0qgEkIIIcR5S0KVEGKedGo5mfQajDk0s+X4MHM0HEVRnkSii6amKzAmINtyPUuX3kk+/xSVaj8TEw9QLh/AcTLE8TRHhwDGx5xboXUSa6OZyoL1Hi2tfBobL0crTbU2+Kq9XiGEEEKIMyWhSggxz+x8pULxMGOj4HltRNEQJy+dHlOtjpDLPYFSmmrlCA0N62ltvYEsWyiX9jOtFMpJz4Sqo+dQ+KAUSrkzgSqg3oPloZSD0i5RlCOZ6JLCFEIIIYQ4r0moEkLMM7sGVEPDNTzF94njCi+2FlUc5ymV8jhOhqrSjE/cT2vrDQBkW29iYvIBKpUjHD+PyhKCdVBKY23I0blaDr7fSVvbzTQ2biCdWi6FKYQQQghxXpNQJYQ4DXWa/fWCFnGcRymXoDZ8zDMVnttEBVV/pBJYW+PoYsARxkQz19BoncJ1m+nsuJ3Vqz8h86iEEEIIcUGQTyxCiBeltfciexXgcTR4afxEJwDWGiYnHySMpvH9NkAdM8TveBYwKGXJpFfQ1vYaCVRCCCGEuGDIpxYhxItqad7MiZ3asz1LCbRyZx4rHCdBItEBQC63jXx+B7XaENXqAEp5KOUDCepB7HiahoaN9PX9qgz3E0IIIcQFRUKVEOJF9fT8Ar7fwfxfF/Xhe57XQSK5HNdtJp1ejee1UquOMTn5CEPD38aYgFRyJVp5NDVdRjq1HK0VEB53FYdkoofu7rfR2nqD9FIJIYQQ4oIic6qEEC+qpeU6Muk+gmDkuD2WWm0Q123E2ohqdQitNeMT/0qh+HR9W20QazWxCSkUXsBaF8dJoVQCY8KZ+VXgOGkSiaWkU8vP/QsUQgghhDhD8nWwEOJFKaXJNFyGUicbshcRRTniuIQxFYyBSmWIfP45rLUo5RBFBaCGMWWszRPHZawN0Rocp5HW1htJpZbT0nK1DPsTQgghxAVJeqqEEKeVTHZgrTnF3tlKfmBMCYgxJiCffxrH8We2HXO0DXCcVhwnges2o1WSTHqlFKcQQgghxAVLQpUQ4rQ8twnHSRLHwSmOmF2D6tj1rOKZNa6OZ1my5FaiqEAmvZp0ZhWpZK/0UgkhhBDigiWhSghxWqnUMlLJXoqlFzixJLqD6zYTRTlmF/CdLZF+cj5RWMBxErS1vYZsdsur13AhhBBCiHNAQpUQ4rRaWjaxZs0n2bvvf1As7gRAKYeGhivQWhFFBTyvkTgOiOMCcVyj3mt1YrBqa7uN9o7XSu+UEEIIIRYNCVVCiNNSStPWdhNauwwMfA1jArT26el5F0ppKtV+kome+oK/Uw9Rqw6Rm36cWm2YY4NVwl/O8mV30tp6w8K9GCGEEEKIs0xClRDiJZvtWapU++d6mpTSZDk6hK+t7SasNRw+cjf9R75GFE9jTExj4wZW9n1IhvsJIYQQYtG5YELV5z73Ob73ve+xfft2fN8nl8udcMzhw4f50Ic+xH333UdDQwPvf//7+fznP4/rXjAvU4jzmlKabHbLvBB1quOWL/sATY2XnRDAhBBCCCEWmwsmbQRBwDve8Q5uuOEGvvSlL52wP45j3vSmN9HV1cXDDz/M0NAQ73vf+/A8j9///d9fgBYLcXF7qQFMCCGEEOJCd8F8bXzXXXfxsY99jCuuuOKk+3/wgx/w/PPP8zd/8zdcddVVvPGNb+R3f/d3+cIXvkAQnKoMtBBCCCGEEEKcmQump+p0HnnkEa644go6Ozvntt1xxx186EMf4rnnnuPqq68+6fNqtRq1Wm3ucT6fByAMQ8IwfHUb/TLNtud8a5dYnOR+E+ea3HPiXJL7TZxrcs9dmF7qv9eiCVXDw8PzAhUw93h4ePiUz/v85z/PXXfddcL2H/zgB6TT6bPbyLPkhz/84UI3QVxE5H4T55rcc+JckvtNnGtyz11YyuXySzpuQUPVJz/5Sf7wD//wRY954YUXuOSSS161NvzWb/0WH//4x+ce5/N5li1bxu23305TU9Ordt1XIgxDfvjDH/KGN7wBz/MWujlikZP7TZxrcs+Jc0nuN3GuyT13YZodxXY6CxqqPvGJT/CBD3zgRY9ZtWrVSzpXV1cXjz322LxtIyMjc/tOJZFIkEgkTtjued55e8Ofz20Ti4/cb+Jck3tOnEtyv4lzTe65C8tL/bda0FDV3t5Oe3v7WTnXDTfcwOc+9zlGR0fp6OgA6t2rTU1NbNiw4axcQwghhBBCCCGOd8HMqTp8+DCTk5McPnyYOI7Zvn07AGvWrKGhoYHbb7+dDRs28N73vpc/+qM/Ynh4mE996lP86q/+6kl7ooQQQgghhBDibLhgQtXv/M7v8NWvfnXu8Ww1v/vuu4/Xvva1OI7Dd7/7XT70oQ9xww03kMlkeP/7389nP/vZhWqyEEIIIYQQ4iJwwYSqu+++m7vvvvtFj1mxYgXf//73z02DhBBCCCGEEIILaPFfIYQQQgghhDgfSagSQgghhBBCiDMgoUoIIYQQQgghzoCEKiGEEEIIIYQ4AxKqhBBCCCGEEOIMSKgSQgghhBBCiDMgoUoIIYQQQgghzoCEKiGEEEIIIYQ4AxKqhBBCCCGEEOIMSKgSQgghhBBCiDMgoUoIIYQQQgghzoCEKiGEEEIIIYQ4AxKqhBBCCCGEEOIMSKgSQgghhBBCiDPgLnQDzjfWWgDy+fwCt+REYRhSLpfJ5/N4nrfQzRGLnNxv4lyTe06cS3K/iXNN7rkL02wmmM0IpyKh6jiFQgGAZcuWLXBLhBBCCCGEEOeDQqFAc3PzKfcre7rYdZExxjA4OEhjYyNKqYVuzjz5fJ5ly5Zx5MgRmpqaFro5YpGT+02ca3LPiXNJ7jdxrsk9d2Gy1lIoFOjp6UHrU8+ckp6q42it6e3tXehmvKimpib5YRTnjNxv4lyTe06cS3K/iXNN7rkLz4v1UM2SQhVCCCGEEEIIcQYkVAkhhBBCCCHEGZBQdQFJJBJ8+tOfJpFILHRTxEVA7jdxrsk9J84lud/EuSb33OImhSqEEEIIIYQQ4gxIT5UQQgghhBBCnAEJVUIIIYQQQghxBiRUCSGEEEIIIcQZkFAlhBBCCCGEEGdAQtUF4HOf+xw33ngj6XSalpaWkx5z+PBh3vSmN5FOp+no6OA3fuM3iKLo3DZULFp9fX0opeb9+YM/+IOFbpZYRL7whS/Q19dHMplky5YtPPbYYwvdJLFIfeYznznh99kll1yy0M0Si8T999/PW97yFnp6elBK8e1vf3vefmstv/M7v0N3dzepVIrbbruNPXv2LExjxVkloeoCEAQB73jHO/jQhz500v1xHPOmN72JIAh4+OGH+epXv8rdd9/N7/zO75zjlorF7LOf/SxDQ0Nzfz784Q8vdJPEIvHNb36Tj3/843z605/mySefZOPGjdxxxx2Mjo4udNPEInXZZZfN+3324IMPLnSTxCJRKpXYuHEjX/jCF066/4/+6I/4n//zf/K//tf/YuvWrWQyGe644w6q1eo5bqk426Sk+gXk7rvv5qMf/Si5XG7e9n/+53/mzW9+M4ODg3R2dgLwv/7X/+K//tf/ytjYGL7vL0BrxWLS19fHRz/6UT760Y8udFPEIrRlyxY2bdrEX/3VXwFgjGHZsmV8+MMf5pOf/OQCt04sNp/5zGf49re/zfbt2xe6KWKRU0rxrW99i7e//e1AvZeqp6eHT3ziE/yX//JfAJienqazs5O7776bd73rXQvYWnGmpKdqEXjkkUe44oor5gIVwB133EE+n+e5555bwJaJxeQP/uAPaGtr4+qrr+aP//iPZXipOCuCIOCJJ57gtttum9umtea2227jkUceWcCWicVsz5499PT0sGrVKn7pl36Jw4cPL3STxEXgwIEDDA8Pz/t919zczJYtW+T33SLgLnQDxJkbHh6eF6iAucfDw8ML0SSxyHzkIx/hmmuuobW1lYcffpjf+q3fYmhoiD/90z9d6KaJC9z4+DhxHJ/0d9jOnTsXqFViMduyZQt3330369evZ2hoiLvuuotbbrmFZ599lsbGxoVunljEZj+Tnez3nXxeu/BJT9UC+eQnP3nCRNnj/8gHCvFqejn34Mc//nFe+9rXcuWVV/Kf//N/5k/+5E/4y7/8S2q12gK/CiGEeHne+MY38o53vIMrr7ySO+64g+9///vkcjn+9m//dqGbJoS4gElP1QL5xCc+wQc+8IEXPWbVqlUv6VxdXV0nVMoaGRmZ2yfEyZzJPbhlyxaiKOLgwYOsX7/+VWiduFgsWbIEx3HmfmfNGhkZkd9f4pxoaWlh3bp17N27d6GbIha52d9pIyMjdHd3z20fGRnhqquuWqBWibNFQtUCaW9vp729/ayc64YbbuBzn/sco6OjdHR0APDDH/6QpqYmNmzYcFauIRafM7kHt2/fjtZ67n4T4pXyfZ9rr72We++9d24ytzGGe++9l1/7tV9b2MaJi0KxWGTfvn28973vXeimiEVu5cqVdHV1ce+9986FqHw+z9atW09Z4VlcOCRUXQAOHz7M5OQkhw8fJo7juYpFa9asoaGhgdtvv50NGzbw3ve+lz/6oz9ieHiYT33qU/zqr/4qiURiYRsvLniPPPIIW7du5XWvex2NjY088sgjfOxjH+M973kP2Wx2oZsnFoGPf/zjvP/97+e6665j8+bN/Pmf/zmlUol/9+/+3UI3TSxC/+W//Bfe8pa3sGLFCgYHB/n0pz+N4zi8+93vXuimiUWgWCzO6/U8cOAA27dvp7W1leXLl/PRj36U3/u932Pt2rWsXLmS3/7t36anp2fuSyVxAbPivPf+97/fAif8ue++++aOOXjwoH3jG99oU6mUXbJkif3EJz5hwzBcuEaLReOJJ56wW7Zssc3NzTaZTNpLL73U/v7v/76tVqsL3TSxiPzlX/6lXb58ufV9327evNk++uijC90ksUj94i/+ou3u7ra+79ulS5faX/zFX7R79+5d6GaJReK+++476We297///dZaa40x9rd/+7dtZ2enTSQS9vWvf73dtWvXwjZanBWyTpUQQgghhBBCnAGp/ieEEEIIIYQQZ0BClRBCCCGEEEKcAQlVQgghhBBCCHEGJFQJIYQQQgghxBmQUCWEEEIIIYQQZ0BClRBCCCGEEEKcAQlVQgghhBBCCHEGJFQJIYQQQgghxBmQUCWEEEIIIYQQZ0BClRBCCABe+9rX8tGPfvSE7XfffTctLS1zjz/zmc+glOJnf/ZnTzj2j//4j1FK8drXvvaEff39/fi+z+WXX37S6yul5v40Nzdz00038eMf//hF22yt5f/8n//Dli1baGhooKWlheuuu44///M/p1wuv+hzLyY/+clPUEqRy+Ve9LhqtcoHPvABrrjiClzX5e1vf/s5aZ8QQlzoJFQJIYR42bq7u7nvvvvo7++ft/3LX/4yy5cvP+lz7r77bt75zneSz+fZunXrSY/5yle+wtDQEA899BBLlizhzW9+M/v37z9lO9773vfy0Y9+lLe97W3cd999bN++nd/+7d/mH//xH/nBD37wyl/gRSqOY1KpFB/5yEe47bbbFro5QghxwZBQJYQQ4mXr6Ojg9ttv56tf/erctocffpjx8XHe9KY3nXC8tZavfOUrvPe97+XOO+/kS1/60knP29LSQldXF5dffjlf/OIXqVQq/PCHPzzpsX/7t3/LPffcw9e//nX+23/7b2zatIm+vj7e9ra38eMf/5jXve51ABhj+OxnP0tvby+JRIKrrrqKf/mXf5k7z8GDB1FK8bd/+7fccsstpFIpNm3axO7du9m2bRvXXXcdDQ0NvPGNb2RsbGzueR/4wAd4+9vfzl133UV7eztNTU385//8nwmCYO6YWq3GRz7yETo6Okgmk9x8881s27Ztbv9sD9K9997LddddRzqd5sYbb2TXrl3zXus//uM/cs0115BMJlm1ahV33XUXURTN7VdK8dd//df8/M//POl0mrVr1/Kd73xn7vXNvhfZbBalFB/4wAdO+p5mMhm++MUv8su//Mt0dXWd9BghhBAnklAlhBDiFfngBz/I3XffPff4y1/+Mr/0S7+E7/snHHvfffdRLpe57bbbeM973sM3vvENSqXSi54/lUoBzAspx7rnnntYv349b3vb207YNzuEEOAv/uIv+JM/+RP+x//4Hzz99NPccccdvPWtb2XPnj3znvPpT3+aT33qUzz55JO4rsudd97Jb/7mb/IXf/EXPPDAA+zdu5ff+Z3fmfece++9lxdeeIGf/OQnfP3rX+cf/uEfuOuuu+b2/+Zv/iZ///d/z1e/+lWefPJJ1qxZwx133MHk5OS88/z3//7f+ZM/+RMef/xxXNflgx/84Ny+Bx54gPe97338+q//Os8//zz/+3//b+6++24+97nPzTvHXXfdxTvf+U6efvppfu7nfo5f+qVfYnJykmXLlvH3f//3AOzatYuhoSH+4i/+4kXfeyGEEC+TFUIIIay1t956q/31X//1E7Z/5Stfsc3NzXOPP/3pT9uNGzfaIAhsR0eH/elPf2qLxaJtbGy0O3bssL/+679ub7311nnnuPPOO+1HP/rRuccbN260X/nKV+YdA9hvfetb1lprS6WS/ZVf+RXrOI7dsWPHSdt76aWX2re+9a2nfV09PT32c5/73LxtmzZtsr/yK79irbX2wIEDFrB//dd/Pbf/61//ugXsvffeO7ft85//vF2/fv3c4/e///22tbXVlkqluW1f/OIXbUNDg43j2BaLRet5nr3nnnvm9gdBYHt6euwf/dEfWWutve+++yxgf/SjH80d873vfc8CtlKpWGutff3rX29///d/f177/+///b+2u7t77jFgP/WpT809LhaLFrD//M//PO86U1NTp32/jn19b3vb217y8UIIcTFzFy7OCSGEuJB5nsd73vMevvKVr7B//37WrVvHlVdeecJxuVyOf/iHf+DBBx+c2/ae97yHL33pSycMQ3v3u9+N4zhUKhXa29v50pe+dNJzQn1I4enk83kGBwe56aab5m2/6aab2LFjx7xtx16ns7MTgCuuuGLettHR0XnP2bhxI+l0eu7xDTfcQLFY5MiRI0xPTxOG4bxre57H5s2beeGFF0557e7ubgBGR0dZvnw5O3bs4KGHHprXMxXHMdVqlXK5PHf9Y8+RyWRoamo6ob1CCCFeHRKqhBBCANDU1MT09PQJ23O53NxQuuN98IMfZMuWLTz77LPzhqwd62tf+xrVapUtW7bMbbPWYoxh9+7drFu3bm77n/3Zn3HbbbfR3NxMe3v7i7Z33bp17Ny586W8tJfE87y5/6+UOuk2Y8xZu97prj17rWKxyF133cW/+Tf/5oTnJZPJk55j9jyvVnuFEELMJ3OqhBBCALB+/XqefPLJE7Y/+eST84LPsS677DIuu+wynn32We68886THvOlL32JT3ziE2zfvn3uz44dO7jlllv48pe/PO/Yrq4u1qxZc9pABXDnnXeye/du/vEf//GEfdZapqenaWpqoqenh4ceemje/oceeogNGzac9hqns2PHDiqVytzjRx99lIaGBpYtW8bq1avxfX/etcMwZNu2bS/r2tdccw27du1izZo1J/zR+qX9Z3x2nlscxy/5ukIIIV466akSQggBwIc+9CH+6q/+io985CP8h//wH0gkEnzve9/j61//Ov/0T/90yuf9+Mc/JgzDeWtZzdq+fTtPPvkk99xzD5dccsm8fe9+97v57Gc/y+/93u/hui//P0fvfOc7+da3vsW73/1uPvWpT3H77bfT3t7OM888w5/92Z/x4Q9/mLe//e38xm/8Bp/+9KdZvXo1V111FV/5ylfYvn0799xzz8u+5vGCIODf//t/z6c+9SkOHjzIpz/9aX7t134NrTWZTIYPfehD/MZv/P/bu3vWVIIwDMPPihaijbaBDQgLy1YKqdMoCCKCYJNCiWgpNnJACIgRBc1PELS0sQkigm1+gQGxT2sRlAUhFsmpTjgfOWDYIkXuqxtmmfetBh52ZveHotGoTNPU3d2dDoeDKpXKyTVarZay2axM01ShUJDP59Pj46PW67W63e5Ja5yfn8swDM3nc2UyGQWDQYXD4Q+f3Ww2Oh6Pen5+luu6Wq1WkqR4PH5yzwDw3RCqAACSpFgspoeHB93c3CiVSul4PMq2bU2n0w9/9PtLKBT679xoNJLjOP8EKknK5/Oq1WpaLBbK5XKf7tcwDE0mEw2HQ43HY/V6Pfn9flmWpVKppHQ6LUmq1+va7/dqNBrabrdyHEez2UyWZX265t+SyaQsy9Ll5aVeXl50dXWldrv9Pt/v9/X6+qpisSjXdXVxcaHlcqlIJHJyjXQ6rfl8rk6no8FgoEAgINu2Va1WT17j7OxMt7e3ajabKpfLKpVKf3y58XeZTEZPT0/v40QiIem0O2wA8F0Zb+ySAAB82vX1tXa7ne7v77+6FQDAF+NOFQAAAAB4QKgCAAAAAA84/gcAAAAAHvCmCgAAAAA8IFQBAAAAgAeEKgAAAADwgFAFAAAAAB4QqgAAAADAA0IVAAAAAHhAqAIAAAAADwhVAAAAAODBTwPks9Tb4esKAAAAAElFTkSuQmCC\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "The UMAP embeddings allow us to see how separable or non-separable different classes are with our specific model. If two specific images are very similar, then they will be placed near each other on this graph.\n", - "\n", - "Reflection: Do you notice any challenges in distinguishing images based on this figure? Are there any types of clothing in the dataset which the model has no trouble distinguishing from the others?" - ], - "metadata": { - "id": "vhrav-ep1K65" - }, - "id": "vhrav-ep1K65" - }, - { - "cell_type": "markdown", - "source": [ - "Reflection: The Visualizing Image Embeddings with UMAP figure shows significant challenges in distinguishing several classes. The model's image embeddings for many classes are not clearly separable leading to two large overlapping clusters:\n", - "\n", - "The upper-body cluster: In the top-left of the plot the turquoise dots \"Ankel boot\" and grey dots \"Sneaker\" and brown dots \"Sandal\" are mixed. This shows that the model visually perceives these two classes as being similar, which explains why they were the most confused pair in the confusion matrix.\n", - "\n", - "The central cluster: The large mass in the middle is a mix of many different classes. Specifically \"T-shirt/top\", \"Shirt,\" \"Pullover,\" \"Coat,\" and \"Dress\".\n", - "\n", - "The \"Trouser\" and \"Bag\" are in their own isolated clusters. The model sees them as visually unique and has no trouble distinguishing them from everything else." - ], - "metadata": { - "id": "ca_gxEzl1OLD" - }, - "id": "ca_gxEzl1OLD" - }, - { - "cell_type": "markdown", - "id": "132f943e", - "metadata": { - "id": "132f943e" - }, - "source": [ - "\n", - "\n", - "## Mini-Experiment\n", - "\n", - "In this section, you will conduct a mini-experiment of your choice to further explore the capabilities of zero-shot classification with transformers. This can be anything you'd like, but here are some ideas to get you started.\n", - "\n", - "### A. Alternative Model\n", - "\n", - "So far we have been utilizing OpenAI's CLIP model for zero-shot classification. However, there are many other vision–language models available in the `transformers` library that you can experiment with. For example, there are larger CLIP models such as [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14), and open-source versions such as [laion/CLIP-ViT-B-32-laion2B-s34B-b79K](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K). You can also search huggingface [here](https://huggingface.co/models?sort=trending&search=clip) to find other models that might be suitable for zero-shot classification.\n", - "\n", - "You can try using a different model to see if it improves the zero-shot classification performance. You should:\n", - "- [ ] Load a different model and processor from the `transformers` library.\n", - "- [ ] Run the zero-shot classification baseline with the new model and report the accuracy and confusion matrix.\n", - "- [ ] Reflect on the performance of the new model compared to the original CLIP model\n", - " - How does the new model perform compared to the original CLIP model?\n", - " - Do you notice any differences in the types of errors made by the new model?\n", - "\n", - "### B. Multiple-Description Classification\n", - "\n", - "Another interesting experiment is to explore multiple-description classification. *This involves providing multiple text prompts for each class, allowing the model to choose the most relevant one. For example, instead of just \"T-shirt/top\", you could provide \"a photo of a T-shirt\", \"a photo of a top\", and \"a photo of a shirt\". This can help the model better understand the class and increases the likelihood of a correct prediction. You should:\n", - "\n", - "- [ ] Create a list of multiple prompts for each class.\n", - "- [ ] Use the `get_text_embeddings` function to create text embeddings for the multiple prompts.\n", - "- [ ] Run the zero-shot classification baseline again using the multiple prompts and report the accuracy and confusion matrix.\n", - "- [ ] Consider the model to be correct if it guesses *any* of the prompts belonging to the correct class.\n", - "\n", - "### C. Top-K Classification\n", - "\n", - "In some classification tasks, it can be useful to consider if the right answer is among the top K (e.g. top 3) predictions. This can be particularly useful in cases where the model is uncertain or when there are multiple similar classes. You should:\n", - "\n", - "- [ ] Modify the `get_predictions` function to return the top K predictions for each image.\n", - "- [ ] Modify the accuracy calculation to consider the model correct if the true class is among the top K predictions.\n", - "- [ ] Report the accuracy and confusion matrix for the top K predictions. Report at least two different values of K (e.g. K=2 and K=4).\n", - "\n", - "### D. Other Ideas\n", - "\n", - "You are welcome to come up with your own mini-experiment! Explain your idea in the report and implement it. Did it work as you expected? What did you learn from it?" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "94af85f8", - "metadata": { - "id": "94af85f8", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "a13bc2af-491c-4a91-eb8b-e615337e7e22" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Loading new model: openai/clip-vit-large-patch14...\n", - "New model loaded and moved to device.\n", - "\n", - "Running BASELINE classification with LARGE model...\n", - "LARGE Model - Baseline Accuracy: 0.5952\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Running IMPROVED prompt classification with LARGE model...\n", - "LARGE Model - Improved Prompt Accuracy: 0.7037\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "from transformers import CLIPModel, CLIPProcessor\n", - "import torch\n", - "from sklearn.metrics import accuracy_score, confusion_matrix\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# ------------------------------------------------------------\n", - "# 1. Loading new larger model and processor\n", - "# ------------------------------------------------------------\n", - "large_model_name = \"openai/clip-vit-large-patch14\"\n", - "\n", - "print(f\"Loading new model: {large_model_name}\")\n", - "\n", - "large_model = CLIPModel.from_pretrained(large_model_name)\n", - "large_processor = CLIPProcessor.from_pretrained(large_model_name, use_fast=False)\n", - "\n", - "large_model.eval()\n", - "large_model.to(device)\n", - "print(\"Model loaded and moved to device: GPU\")\n", - "\n", - "# ------------------------------------------------------------\n", - "# We must re-define the helper functions to use the NEW model\n", - "# ------------------------------------------------------------\n", - "\n", - "def get_text_embeddings_large(class_names: list[str]) -> torch.Tensor:\n", - " \"\"\"Get text embeddings using the LARGE model.\"\"\"\n", - " tokenized = large_processor(text=class_names,\n", - " padding=True,\n", - " return_tensors=\"pt\").to(device)\n", - " with torch.no_grad():\n", - " text_embeddings = large_model.get_text_features(**tokenized)\n", - " text_feats = text_embeddings / text_embeddings.norm(dim=-1, keepdim=True)\n", - " return text_feats\n", - "\n", - "def get_image_embeddings_large(images: torch.Tensor) -> torch.Tensor:\n", - " \"\"\"Get image embeddings using the LARGE model.\"\"\"\n", - " with torch.no_grad():\n", - " image_embeddings = large_model.get_image_features(pixel_values=images)\n", - " image_feats = image_embeddings / image_embeddings.norm(dim=-1, keepdim=True)\n", - " return image_feats\n", - "\n", - "# get_cosine_similarity, get_predictions, and plot_confusion_matrix\n", - "# are unchanged as they work on the output embeddings.\n", - "\n", - "# ------------------------------------------------------------\n", - "# 2. Running Baseline classification with new model\n", - "# ------------------------------------------------------------\n", - "print(\"\\nRunning Baseline classification with LARGE model.\")\n", - "y_true_large_base, y_pred_large_base = [], []\n", - "\n", - "text_features_large_base = get_text_embeddings_large(CLASS_NAMES)\n", - "\n", - "for pixel_values, labels in test_loader:\n", - " image_features = get_image_embeddings_large(pixel_values)\n", - " similarity = get_cosine_similarity(image_features, text_features_large_base)\n", - " predictions = get_predictions(similarity)\n", - "\n", - " y_true_large_base.extend(labels.tolist())\n", - " y_pred_large_base.extend(predictions.tolist())\n", - "\n", - "# Reporting accuracy\n", - "accuracy_large_base = accuracy_score(y_true_large_base, y_pred_large_base)\n", - "print(f\"Baseline Accuracy [LARGE Model]: {accuracy_large_base:.4f}\")\n", - "\n", - "# Report confusion matrix\n", - "plot_confusion_matrix(y_true_large_base, y_pred_large_base, CLASS_NAMES)\n", - "\n", - "# ------------------------------------------------------------\n", - "# 3. Running Improved prompt classification with the new model\n", - "# ------------------------------------------------------------\n", - "print(\"\\nRunning improved prompt classification with LARGE model.\")\n", - "y_true_large_imp, y_pred_large_imp = [], []\n", - "\n", - "text_features_large_imp = get_text_embeddings_large(improved_CLASS_NAMES)\n", - "\n", - "for pixel_values, labels in test_loader:\n", - " image_features = get_image_embeddings_large(pixel_values)\n", - " similarity = get_cosine_similarity(image_features, text_features_large_imp)\n", - " predictions = get_predictions(similarity)\n", - "\n", - " y_true_large_imp.extend(labels.tolist())\n", - " y_pred_large_imp.extend(predictions.tolist())\n", - "\n", - "# Reporting accuracy\n", - "accuracy_large_imp = accuracy_score(y_true_large_imp, y_pred_large_imp)\n", - "print(f\"Improved Prompt Accuracy [LARGE Model]: {accuracy_large_imp:.4f}\")\n", - "\n", - "# Reporting confusion matrix\n", - "plot_confusion_matrix(y_true_large_imp, y_pred_large_imp, CLASS_NAMES)" - ] - }, - { - "cell_type": "markdown", - "source": [ - "Mini-Experiment: Alternative Model\n", - "\n", - "Model: openai/clip-vit-large-patch14\n", - "\n", - "Original Model (base):\n", - "- Baseline Accuracy: 0.6240 (62.40%)\n", - "- Improved Accuracy: 0.6477 (64.77%)\n", - "- Gain from Prompts: +2.37%\n", - "\n", - "New Model (large):\n", - "- Baseline Accuracy: 0.5952 (59.52%)\n", - "- Improved Accuracy: 0.7037 (70.37%)\n", - "- Gain from Prompts: +10.85%\n", - "\n", - "Observation:\n", - "- The new large model was actually worse when using the simple baseline prompts (59.52% vs. 62.40%).\n", - "- The new large model accuracy jumped +10.85% (from 59.52% to 70.37%). This is a much larger improvement than the base model's +2.37% gain. This shows the large model has a deeper understanding of language and can better use the specific details prompts to tell the images apart.\n", - "-Overall, the large model has higher performance with a higher final accuracy (70.37% vs. 64.77%)." - ], - "metadata": { - "id": "C_31xYva-UAd" - }, - "id": "C_31xYva-UAd" - }, - { - "cell_type": "markdown", - "id": "234eab38", - "metadata": { - "id": "234eab38" - }, - "source": [ - "### Short Report\n", - "\n", - "In this section, you will write a short report summarizing your findings from the mini-experiment. The report should include the following sections:\n", - "\n", - "- **Introduction**: Briefly describe the mini-experiment you conducted and its objectives.\n", - "- **Methodology**: Explain the steps you took to conduct the experiment, including any modifications you made to the code or model.\n", - "- **Results**: Present the results of your experiment.\n", - "- **Discussion**: Reflect on the performance of the model and the implications of your findings. Consider the strengths and weaknesses of zero-shot transformers versus a trained CNN." - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Introduction\n", - "\n", - "For the mini-experiment, I conducted **\"Alternative Model\" (Option A)**. \n", - "The objective was to compare the zero-shot classification performance of the original `openai/clip-vit-base-patch32` model with a larger, more powerful model, `openai/clip-vit-large-patch14`, on the **Fashion-MNIST** dataset.\n", - "\n", - "---\n", - "\n", - "# Methodology\n", - "\n", - "The experiment involved modifying the initial setup code to load the `openai/clip-vit-large-patch14` model and its corresponding processor. \n", - "The entire classification pipeline was then re-executed:\n", - "\n", - "1. **Baseline accuracy** was re-established using the simple, single-word `CLASS_NAMES`. \n", - "2. **Improved accuracy** was then measured using the same set of descriptive `improved_CLASS_NAMES` to evaluate how the new model responded to prompt engineering.\n", - "\n", - "---\n", - "\n", - "# Results\n", - "\n", - "The results showed a significant difference in how the two models performed:\n", - "\n", - "| Model | Baseline Accuracy (Simple Prompts) | Improved Accuracy (Detailed Prompts) | Gain from Prompts |\n", - "|----------------------|------------------------------------|--------------------------------------|-------------------|\n", - "| base-patch32 (Original) | 62.40% | 64.77% | +2.37% |\n", - "| large-patch14 (New) | 59.52% | 70.37% | +10.85% |\n", - "\n", - "The new large model had a worse baseline accuracy but responded dramatically better to the improved prompts, achieving a much higher final accuracy.\n", - "\n", - "---\n", - "\n", - "# Discussion\n", - "\n", - "The large model's poor baseline (59.52%) suggests that a more complex model can be more easily confused by simple prompts. \n", - "However, its **+10.85% accuracy gain** from prompt engineering shows it has a far better language understanding. It was able to use specific prompt details (e.g., *\"long-sleeve\"*, *\"open-toed\"*) to overcome visual ambiguities, resulting in a better overall accuracy of **70.37%**.\n", - "\n", - "This experiment highlights the main strength of zero-shot transformers achieving **70% accuracy on a dataset without any task-specific training** is powerful compared to a trained CNN that failed to learn. \n", - "The main weakness remains a high sensitivity to prompt quality as the UMAP plot showed difficulty in separating similar classes (like *\"Shirt\"* and *\"T-shirt\"*).\n" - ], - "metadata": { - "id": "Layz3zyoBGaE" - }, - "id": "Layz3zyoBGaE" - }, - { - "cell_type": "markdown", - "id": "745659f3", - "metadata": { - "id": "745659f3" - }, - "source": [ - "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n", - "### Submission Parameters:\n", - "* Submission Due Date: `23:59 PM - 02/11/2025`\n", - "* The branch name for your repo should be: `assignment-2`\n", - "* What to submit for this assignment:\n", - " * This Jupyter Notebook (assignment_2.ipynb)\n", - " * The Lab 4 notebook (labs/lab_4.ipynb)\n", - " * The Lab 5 notebook (labs/lab_5.ipynb)\n", - " * The Lab 6 notebook (labs/lab_6.ipynb)\n", - "* What the pull request link should look like for this assignment: `https://github.com//deep_learning/pull/`\n", - "* Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n", - "Checklist:\n", - "- [ ] Created a branch with the correct naming convention.\n", - "- [ ] Ensured that the repository is public.\n", - "- [ ] Reviewed the PR description guidelines and adhered to them.\n", - "- [ ] Verify that the link is accessible in a private browser window.\n", - "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-7-help-ml`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - }, - "colab": { - "provenance": [], - "gpuType": "T4" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "3750ae40cd39499ab50991df5946d2c4": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_45f50bbda4034dee9e57c6ea0679ced6", - "IPY_MODEL_b50a1290227a4270915f679dbb5628f1", - "IPY_MODEL_dadfc824823d42df8b240ecf6565ea34" - ], - "layout": "IPY_MODEL_e3bfa3e2b1e54a488527ff0ae3273df4" - } - }, - "45f50bbda4034dee9e57c6ea0679ced6": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_85e6a4ec07da45deade08d16cc6eef61", - "placeholder": "​", - "style": "IPY_MODEL_f7d9356562064c31acab1b6bb00b7a69", - "value": "config.json: " - } - }, - "b50a1290227a4270915f679dbb5628f1": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_b7088f8adb6d4e0db3bf112a11bfb2fe", - "max": 1, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_44d8945ab43c456093a1a2c25e020463", - "value": 1 - } - }, - "dadfc824823d42df8b240ecf6565ea34": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_91236763ae8a481982409ca37aabfdc6", - "placeholder": "​", - "style": "IPY_MODEL_ca0b9f0b91544d98bce5968b606f2fbb", - "value": " 4.19k/? [00:00<00:00, 163kB/s]" - } - }, - "e3bfa3e2b1e54a488527ff0ae3273df4": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "85e6a4ec07da45deade08d16cc6eef61": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "f7d9356562064c31acab1b6bb00b7a69": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "b7088f8adb6d4e0db3bf112a11bfb2fe": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "20px" - } - }, - "44d8945ab43c456093a1a2c25e020463": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "91236763ae8a481982409ca37aabfdc6": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "ca0b9f0b91544d98bce5968b606f2fbb": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "4d9a3ea5e7054175870ed1827cb11aff": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4f3c81150aff4f1bb51a108b8d66179a", - "IPY_MODEL_0caba3aa88fb44a4b449f1deff0e77f7", - "IPY_MODEL_e2df6d112d4b47eb845fa7a9f78cf821" - ], - "layout": "IPY_MODEL_86de6d4aa4bc4e14a4ec52969739d6ca" - } - }, - "4f3c81150aff4f1bb51a108b8d66179a": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6f24ebc5ab6f46c2b380070325c893d7", - "placeholder": "​", - "style": "IPY_MODEL_b616cab018e646e2b138ad3b746a6616", - "value": "pytorch_model.bin: 100%" - } - }, - "0caba3aa88fb44a4b449f1deff0e77f7": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_915793ec7e324a4d8cf6d085e0c161c6", - "max": 605247071, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_d1f277973f0f4bd1a013009e6fb296b1", - "value": 605247071 - } - }, - "e2df6d112d4b47eb845fa7a9f78cf821": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_11b7bb4b6eef4f6d89f853792342c126", - "placeholder": "​", - "style": "IPY_MODEL_477a3264053345ed84e0e1c2446409bf", - "value": " 605M/605M [00:05<00:00, 197MB/s]" - } - }, - "86de6d4aa4bc4e14a4ec52969739d6ca": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "6f24ebc5ab6f46c2b380070325c893d7": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "b616cab018e646e2b138ad3b746a6616": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "915793ec7e324a4d8cf6d085e0c161c6": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "d1f277973f0f4bd1a013009e6fb296b1": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "11b7bb4b6eef4f6d89f853792342c126": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "477a3264053345ed84e0e1c2446409bf": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "dedc22fcd16d4f6d8d1f58f2a587b8db": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_a45f0dccae9340a185d58ddeb9209039", - "IPY_MODEL_680d40e1dc86411d8da97903d6386d9b", - "IPY_MODEL_a4af94e1834747dd8e523e8b3025608b" - ], - "layout": "IPY_MODEL_7c76cd40a8584d4d876c027c972060fd" - } - }, - "a45f0dccae9340a185d58ddeb9209039": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_02bb96f01b524da68d893596f26f609b", - "placeholder": "​", - "style": "IPY_MODEL_16c9e8b36d6042348a7e91aa85ee9ee2", - "value": "model.safetensors: 100%" - } - }, - "680d40e1dc86411d8da97903d6386d9b": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_f073119ca5c94362897eda2f2c245e9f", - "max": 605157884, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_a9cef28515db48c8975a809b3d35e174", - "value": 605157884 - } - }, - "a4af94e1834747dd8e523e8b3025608b": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_23c727feada1401e902a9c3056267376", - "placeholder": "​", - "style": "IPY_MODEL_9d1ab73f90c44d0eb0833ee96b57ed38", - "value": " 605M/605M [00:07<00:00, 93.9MB/s]" - } - }, - "7c76cd40a8584d4d876c027c972060fd": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "02bb96f01b524da68d893596f26f609b": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "16c9e8b36d6042348a7e91aa85ee9ee2": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "f073119ca5c94362897eda2f2c245e9f": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "a9cef28515db48c8975a809b3d35e174": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "23c727feada1401e902a9c3056267376": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "9d1ab73f90c44d0eb0833ee96b57ed38": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "d6547cdb382c40d8beff462d009bdae0": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_73816aeaaa8847f084e8cad15cd0d957", - "IPY_MODEL_270989562a5c4f1d835b0b5da15aed86", - "IPY_MODEL_534906f008474c2090930d058a261a37" - ], - "layout": "IPY_MODEL_2289e8cf8dbf499aa8e778b1aa88dbac" - } - }, - "73816aeaaa8847f084e8cad15cd0d957": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_a8cbcd4b56144be0bbfe320eb581c9cf", - "placeholder": "​", - "style": "IPY_MODEL_21bdb4ecd8274ec9b67f1281d6383529", - "value": "preprocessor_config.json: 100%" - } - }, - "270989562a5c4f1d835b0b5da15aed86": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_1db6eca3754d4754bdf68a06d49754ed", - "max": 316, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_d073e859935849e0800a7d4ede2fb8ec", - "value": 316 - } - }, - "534906f008474c2090930d058a261a37": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_91f734f319f54df3b3a6b396337bbfac", - "placeholder": "​", - "style": "IPY_MODEL_4bbbf1e5d5c4490695b42526f4cf5269", - "value": " 316/316 [00:00<00:00, 5.50kB/s]" - } - }, - "2289e8cf8dbf499aa8e778b1aa88dbac": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "a8cbcd4b56144be0bbfe320eb581c9cf": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "21bdb4ecd8274ec9b67f1281d6383529": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "1db6eca3754d4754bdf68a06d49754ed": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "d073e859935849e0800a7d4ede2fb8ec": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "91f734f319f54df3b3a6b396337bbfac": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "4bbbf1e5d5c4490695b42526f4cf5269": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "b94cb23cc9f546c2b1e370ed3fd16e33": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1f247f8d0b064ad6a8907b882eb21a13", - "IPY_MODEL_7ff2d4d5d76b4bf98d5185063c113ef4", - "IPY_MODEL_c6dcb311005b42e98d12ae53d8139600" - ], - "layout": "IPY_MODEL_3e0a0055fdb64ba5864cb9db5071a6ac" - } - }, - "1f247f8d0b064ad6a8907b882eb21a13": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_ba2bfc83dfa44868a4119d55cc0400bd", - "placeholder": "​", - "style": "IPY_MODEL_9b7d4215bda64c189f8f7ca90e80072c", - "value": "tokenizer_config.json: 100%" - } - }, - "7ff2d4d5d76b4bf98d5185063c113ef4": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_b158d65ffb754442a11b1906bfa09c38", - "max": 592, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_e4c1a8e9d7624d2ba97c5158eebb6bdb", - "value": 592 - } - }, - "c6dcb311005b42e98d12ae53d8139600": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_64a8b8cb2a6d4feabb26b8b9b1cbfeaf", - "placeholder": "​", - "style": "IPY_MODEL_b01c58c8d7f2443e8305834526355a0c", - "value": " 592/592 [00:00<00:00, 12.3kB/s]" - } - }, - "3e0a0055fdb64ba5864cb9db5071a6ac": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "ba2bfc83dfa44868a4119d55cc0400bd": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "9b7d4215bda64c189f8f7ca90e80072c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "b158d65ffb754442a11b1906bfa09c38": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "e4c1a8e9d7624d2ba97c5158eebb6bdb": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "64a8b8cb2a6d4feabb26b8b9b1cbfeaf": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "b01c58c8d7f2443e8305834526355a0c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "d2c1e60c41844a18b82f0cb28880b916": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_be4390e933dc45b8a4d7b1ebea712aa3", - "IPY_MODEL_2558716cf8c8437590a8594e6e5d5e98", - "IPY_MODEL_afd3848573814a008ba6f8f3e906bf6b" - ], - "layout": "IPY_MODEL_087cb104dc444f6484923bcdb567b8fa" - } - }, - "be4390e933dc45b8a4d7b1ebea712aa3": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_01c9984f7d7e46129f7f26a79dd17755", - "placeholder": "​", - "style": "IPY_MODEL_992513aa0e9c4baa96cb5191e045a082", - "value": "vocab.json: " - } - }, - "2558716cf8c8437590a8594e6e5d5e98": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_b0090c94753d41dabd9ac2dd423e9201", - "max": 1, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_41039a29e2e74c95a8af8f6e1b2ffb11", - "value": 1 - } - }, - "afd3848573814a008ba6f8f3e906bf6b": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_d09ba93de344459f99b09f5f5ac37f3e", - "placeholder": "​", - "style": "IPY_MODEL_09962b3ebab64b1f8dfafae83a294cc2", - "value": " 862k/? [00:00<00:00, 8.06MB/s]" - } - }, - "087cb104dc444f6484923bcdb567b8fa": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "01c9984f7d7e46129f7f26a79dd17755": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "992513aa0e9c4baa96cb5191e045a082": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "b0090c94753d41dabd9ac2dd423e9201": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "20px" - } - }, - "41039a29e2e74c95a8af8f6e1b2ffb11": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "d09ba93de344459f99b09f5f5ac37f3e": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "09962b3ebab64b1f8dfafae83a294cc2": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "17fc66ed0df348cf92223bfeae38fbc0": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_240ea684bf644ad39fa1dbb9df4349f1", - "IPY_MODEL_6f8ade98160e4143989bda894b0a170e", - "IPY_MODEL_2ae17bbf2db2418bb0a1552a35485b09" - ], - "layout": "IPY_MODEL_1825818985064b028a4be6220958086c" - } - }, - "240ea684bf644ad39fa1dbb9df4349f1": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_f53717f5ffa547ae812e5ac208c2064a", - "placeholder": "​", - "style": "IPY_MODEL_c83df788a5ca47a2af0434b9b9696ef8", - "value": "merges.txt: " - } - }, - "6f8ade98160e4143989bda894b0a170e": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_2e823e9f707a4497b52a39f8085167e5", - "max": 1, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_36b753ceca714ee09786fd806939af01", - "value": 1 - } - }, - "2ae17bbf2db2418bb0a1552a35485b09": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_93a4b3cb88eb41659d0f2b0761d70426", - "placeholder": "​", - "style": "IPY_MODEL_b710ea11c6ca49e39b28423b2e7de06d", - "value": " 525k/? [00:00<00:00, 7.46MB/s]" - } - }, - "1825818985064b028a4be6220958086c": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "f53717f5ffa547ae812e5ac208c2064a": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "c83df788a5ca47a2af0434b9b9696ef8": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "2e823e9f707a4497b52a39f8085167e5": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "20px" - } - }, - "36b753ceca714ee09786fd806939af01": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "93a4b3cb88eb41659d0f2b0761d70426": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "b710ea11c6ca49e39b28423b2e7de06d": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "e370bffadf8a444eb0083b3337e19579": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_a2fdd5e9d6964b18b2064f3ead1cb60a", - "IPY_MODEL_84238eff2dfd48d4aebc73a5c057b871", - "IPY_MODEL_062f69c14c2e459ba6dd79cf96e36213" - ], - "layout": "IPY_MODEL_00e03185b187495688cfbb21de2aa0d1" - } - }, - "a2fdd5e9d6964b18b2064f3ead1cb60a": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_72c526138af3440d93bac8a5db9f72a5", - "placeholder": "​", - "style": "IPY_MODEL_902061e0c6b147ddb9e7c0c28b68b045", - "value": "special_tokens_map.json: 100%" - } - }, - "84238eff2dfd48d4aebc73a5c057b871": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_346e74103d7d4b0abca523841fad013a", - "max": 389, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_f0f93bbdbc494d89bcf54751f4615abd", - "value": 389 - } - }, - "062f69c14c2e459ba6dd79cf96e36213": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_c954d22b66e4457cb1c661ed7b7c8ef8", - "placeholder": "​", - "style": "IPY_MODEL_f9df48ee9bb7405984197bbf584b88f1", - "value": " 389/389 [00:00<00:00, 9.56kB/s]" - } - }, - "00e03185b187495688cfbb21de2aa0d1": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "72c526138af3440d93bac8a5db9f72a5": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "902061e0c6b147ddb9e7c0c28b68b045": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "346e74103d7d4b0abca523841fad013a": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "f0f93bbdbc494d89bcf54751f4615abd": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "c954d22b66e4457cb1c661ed7b7c8ef8": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "f9df48ee9bb7405984197bbf584b88f1": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "6e5548d364044237985c6eea263828ca": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_cfc02aade9e34d96b4c031cd3487888a", - "IPY_MODEL_ac86c3f3f5f04dd08d9044a3ad31e061", - "IPY_MODEL_919a3d32344d4647ae0656b5682cccf2" - ], - "layout": "IPY_MODEL_dcec9feb3df64446822c89184dc13f0f" - } - }, - "cfc02aade9e34d96b4c031cd3487888a": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_58d9ee30d6b04072a71c39b4b1ab0a2a", - "placeholder": "​", - "style": "IPY_MODEL_8ce69d8b62894c55b1940b9f2e90ae4a", - "value": "tokenizer.json: " - } - }, - "ac86c3f3f5f04dd08d9044a3ad31e061": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_f928225848a84bc7a90a29e48ac7c0e0", - "max": 1, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_037f8bbdd54e4cbf9b530b4a09e0b1d5", - "value": 1 - } - }, - "919a3d32344d4647ae0656b5682cccf2": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_fe462e23b1e449889b1544c8ef540bb0", - "placeholder": "​", - "style": "IPY_MODEL_e022787742b340d49474e2659755ce37", - "value": " 2.22M/? [00:00<00:00, 17.4MB/s]" - } - }, - "dcec9feb3df64446822c89184dc13f0f": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "58d9ee30d6b04072a71c39b4b1ab0a2a": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "8ce69d8b62894c55b1940b9f2e90ae4a": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "f928225848a84bc7a90a29e48ac7c0e0": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "20px" - } - }, - "037f8bbdd54e4cbf9b530b4a09e0b1d5": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "fe462e23b1e449889b1544c8ef540bb0": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "e022787742b340d49474e2659755ce37": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - } - } - }, - "accelerator": "GPU" - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file From 63736f001823fad4b0261b433a1404f4e7c2a3d4 Mon Sep 17 00:00:00 2001 From: Vincent Van Schaik <193274586+VincentSchaik@users.noreply.github.com> Date: Sun, 2 Nov 2025 13:38:31 -0500 Subject: [PATCH 04/16] Resolving Notebook rendering error. --- 02_activities/assignments/assignment_2.ipynb | 4561 ++++++++++++++++++ 1 file changed, 4561 insertions(+) create mode 100644 02_activities/assignments/assignment_2.ipynb diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb new file mode 100644 index 000000000..53914c815 --- /dev/null +++ b/02_activities/assignments/assignment_2.ipynb @@ -0,0 +1,4561 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "197fbb2d", + "metadata": { + "id": "197fbb2d" + }, + "source": [ + "# Assignment 2 – Zero-Shot Image Classification with Transformers\n", + "\n", + "In this assignment, you will apply a pre-trained vision–language transformer (e.g. CLIP) to perform **zero-shot** classification on the Fashion-MNIST dataset—classifying each image without any task-specific training. You will build on the concepts from Assignment 1 by comparing this “off-the-shelf” approach to the CNN you previously trained.\n", + "\n", + "You will:\n", + "1. **Load** the Fashion-MNIST images using PyTorch instead of Keras.\n", + "2. **Run a zero-shot baseline** with simple text prompts to set a performance reference.\n", + "3. **Engineer improved prompts** and measure the resulting accuracy gains.\n", + "4. **Visualise image embeddings** with UMAP to inspect class separability.\n", + "5. **Conduct one mini-experiment** of your choice.\n", + "6. **Summarise findings** and reflect on strengths and weaknesses of zero-shot transformers versus a trained CNN.\n", + "\n", + "# 1. Loading the Fashion-MNIST Dataset\n", + "\n", + "As in assignment 1, we'll load the Fashion-MNIST dataset, but this time using `torchvision.datasets` to ensure compatibility with the `transformers` library. We will also load our model and processor from the `transformers` library.\n", + "\n", + "The transformers library allows us to use pre-trained models like CLIP, which can perform zero-shot classification by leveraging the text prompts we provide. There are two key objects we will use: the `CLIPModel` for the model itself and the `CLIPProcessor` for preparing our images and text prompts.\n", + "\n", + "Since we are not actually training a model in this assignment, we will set the CLIP model to evaluation mode. If the model is designed to utilize features like dropout or batch normalization, setting it to evaluation mode ensures that these features behave correctly during inference (prediction). Setting the model to evaluaton mode also tells PyTorch that we don't have to compute gradients, which can save memory and speed up inference.\n", + "\n", + "In order to speed up processing, we will also move the model to an \"accelerator\" if available. This is typically a GPU, but modern MacBooks also have an \"Apple Silicon\" accelerator that can be used for inference, called MPS (Metal Performance Shaders). If you are using a MacBook with Apple Silicon, you can use the MPS device for faster processing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "026463ce", + "metadata": { + "id": "026463ce" + }, + "outputs": [], + "source": [ + "# Uncomment and run if required\n", + "#!pip install transformers torchvision torch accelerate" + ] + }, + { + "cell_type": "code", + "source": [ + "# Installing cuml\n", + "!pip install cuml-cu12 --extra-index-url=https://pypi.nvidia.com" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UUQ0KHWYxwaj", + "outputId": "2f284dfc-d49f-4c40-a1bf-1e518d6b8e42" + }, + "id": "UUQ0KHWYxwaj", + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://pypi.nvidia.com\n", + "Requirement already satisfied: cuml-cu12 in /usr/local/lib/python3.12/dist-packages (25.6.0)\n", + "Requirement already satisfied: cuda-python<13.0a0,>=12.6.2 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (12.6.2.post1)\n", + "Requirement already satisfied: cudf-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: cupy-cuda12x>=12.0.0 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (13.3.0)\n", + "Requirement already satisfied: cuvs-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.1)\n", + "Requirement already satisfied: dask-cuda==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: dask-cudf-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (1.5.2)\n", + "Requirement already satisfied: libcuml-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: numba<0.62.0a0,>=0.59.1 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (0.60.0)\n", + "Requirement already satisfied: numpy<3.0a0,>=1.23 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (2.0.2)\n", + "Requirement already satisfied: nvidia-cublas-cu12 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (12.6.4.1)\n", + "Requirement already satisfied: nvidia-cufft-cu12 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (11.3.0.4)\n", + "Requirement already satisfied: nvidia-curand-cu12 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (10.3.7.77)\n", + "Requirement already satisfied: nvidia-cusolver-cu12 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (11.7.1.2)\n", + "Requirement already satisfied: nvidia-cusparse-cu12 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (12.5.4.2)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.0)\n", + "Requirement already satisfied: pylibraft-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: raft-dask-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: rapids-dask-dependency==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: rmm-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: scikit-learn>=1.5 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (1.6.1)\n", + "Requirement already satisfied: scipy>=1.8.0 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (1.16.3)\n", + "Requirement already satisfied: treelite==4.4.1 in /usr/local/lib/python3.12/dist-packages (from cuml-cu12) (4.4.1)\n", + "Requirement already satisfied: cachetools in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (5.5.2)\n", + "Requirement already satisfied: fsspec>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (2025.3.0)\n", + "Requirement already satisfied: libcudf-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: numba-cuda<0.12.0a0,>=0.11.0 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (0.11.0)\n", + "Requirement already satisfied: nvidia-cuda-nvcc-cu12 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (12.5.82)\n", + "Requirement already satisfied: nvidia-cuda-nvrtc-cu12 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (12.6.77)\n", + "Requirement already satisfied: nvtx>=0.2.1 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (0.2.13)\n", + "Requirement already satisfied: pandas<2.2.4dev0,>=2.0 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (2.2.2)\n", + "Requirement already satisfied: pyarrow<20.0.0a0,>=14.0.0 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (18.1.0)\n", + "Requirement already satisfied: pylibcudf-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: pynvjitlink-cu12 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (0.7.0)\n", + "Requirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (13.9.4)\n", + "Requirement already satisfied: typing_extensions>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from cudf-cu12==25.6.*->cuml-cu12) (4.15.0)\n", + "Requirement already satisfied: libcuvs-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from cuvs-cu12==25.6.*->cuml-cu12) (25.6.1)\n", + "Requirement already satisfied: click>=8.1 in /usr/local/lib/python3.12/dist-packages (from dask-cuda==25.6.*->cuml-cu12) (8.3.0)\n", + "Requirement already satisfied: pynvml<13.0.0a0,>=12.0.0 in /usr/local/lib/python3.12/dist-packages (from dask-cuda==25.6.*->cuml-cu12) (12.0.0)\n", + "Requirement already satisfied: zict>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from dask-cuda==25.6.*->cuml-cu12) (3.0.0)\n", + "Requirement already satisfied: libraft-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from libcuml-cu12==25.6.*->cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: rapids-logger==0.1.* in /usr/local/lib/python3.12/dist-packages (from libcuml-cu12==25.6.*->cuml-cu12) (0.1.19)\n", + "Requirement already satisfied: distributed-ucxx-cu12==0.44.* in /usr/local/lib/python3.12/dist-packages (from raft-dask-cu12==25.6.*->cuml-cu12) (0.44.0)\n", + "Requirement already satisfied: nvidia-nccl-cu12>=2.19 in /usr/local/lib/python3.12/dist-packages (from raft-dask-cu12==25.6.*->cuml-cu12) (2.27.3)\n", + "Requirement already satisfied: ucx-py-cu12==0.44.* in /usr/local/lib/python3.12/dist-packages (from raft-dask-cu12==25.6.*->cuml-cu12) (0.44.0)\n", + "Requirement already satisfied: dask==2025.5.0 in /usr/local/lib/python3.12/dist-packages (from rapids-dask-dependency==25.6.*->cuml-cu12) (2025.5.0)\n", + "Requirement already satisfied: distributed==2025.5.0 in /usr/local/lib/python3.12/dist-packages (from rapids-dask-dependency==25.6.*->cuml-cu12) (2025.5.0)\n", + "Requirement already satisfied: librmm-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from rmm-cu12==25.6.*->cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: cloudpickle>=3.0.0 in /usr/local/lib/python3.12/dist-packages (from dask==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (3.1.1)\n", + "Requirement already satisfied: partd>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from dask==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (1.4.2)\n", + "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.12/dist-packages (from dask==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (6.0.3)\n", + "Requirement already satisfied: toolz>=0.10.0 in /usr/local/lib/python3.12/dist-packages (from dask==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (0.12.1)\n", + "Requirement already satisfied: jinja2>=2.10.3 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (3.1.6)\n", + "Requirement already satisfied: locket>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (1.0.0)\n", + "Requirement already satisfied: msgpack>=1.0.2 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (1.1.2)\n", + "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (5.9.5)\n", + "Requirement already satisfied: sortedcontainers>=2.0.5 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (2.4.0)\n", + "Requirement already satisfied: tblib>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (3.2.0)\n", + "Requirement already satisfied: tornado>=6.2.0 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (6.5.1)\n", + "Requirement already satisfied: urllib3>=1.26.5 in /usr/local/lib/python3.12/dist-packages (from distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (2.5.0)\n", + "Requirement already satisfied: ucxx-cu12==0.44.* in /usr/local/lib/python3.12/dist-packages (from distributed-ucxx-cu12==0.44.*->raft-dask-cu12==25.6.*->cuml-cu12) (0.44.0)\n", + "Requirement already satisfied: libkvikio-cu12==25.6.* in /usr/local/lib/python3.12/dist-packages (from libcudf-cu12==25.6.*->cudf-cu12==25.6.*->cuml-cu12) (25.6.0)\n", + "Requirement already satisfied: libucx-cu12<1.19,>=1.15.0 in /usr/local/lib/python3.12/dist-packages (from ucx-py-cu12==0.44.*->raft-dask-cu12==25.6.*->cuml-cu12) (1.18.1)\n", + "Requirement already satisfied: libucxx-cu12==0.44.* in /usr/local/lib/python3.12/dist-packages (from ucxx-cu12==0.44.*->distributed-ucxx-cu12==0.44.*->raft-dask-cu12==25.6.*->cuml-cu12) (0.44.0)\n", + "Requirement already satisfied: fastrlock>=0.5 in /usr/local/lib/python3.12/dist-packages (from cupy-cuda12x>=12.0.0->cuml-cu12) (0.8.3)\n", + "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.12/dist-packages (from numba<0.62.0a0,>=0.59.1->cuml-cu12) (0.43.0)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=1.5->cuml-cu12) (3.6.0)\n", + "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.12/dist-packages (from nvidia-cufft-cu12->cuml-cu12) (12.6.85)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<2.2.4dev0,>=2.0->cudf-cu12==25.6.*->cuml-cu12) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<2.2.4dev0,>=2.0->cudf-cu12==25.6.*->cuml-cu12) (2025.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas<2.2.4dev0,>=2.0->cudf-cu12==25.6.*->cuml-cu12) (2025.2)\n", + "Requirement already satisfied: nvidia-ml-py<13.0.0a0,>=12.0.0 in /usr/local/lib/python3.12/dist-packages (from pynvml<13.0.0a0,>=12.0.0->dask-cuda==25.6.*->cuml-cu12) (12.575.51)\n", + "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->cudf-cu12==25.6.*->cuml-cu12) (4.0.0)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->cudf-cu12==25.6.*->cuml-cu12) (2.19.2)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2>=2.10.3->distributed==2025.5.0->rapids-dask-dependency==25.6.*->cuml-cu12) (3.0.3)\n", + "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->cudf-cu12==25.6.*->cuml-cu12) (0.1.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas<2.2.4dev0,>=2.0->cudf-cu12==25.6.*->cuml-cu12) (1.17.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b4701b15", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 450, + "referenced_widgets": [ + "3750ae40cd39499ab50991df5946d2c4", + "45f50bbda4034dee9e57c6ea0679ced6", + "b50a1290227a4270915f679dbb5628f1", + "dadfc824823d42df8b240ecf6565ea34", + "e3bfa3e2b1e54a488527ff0ae3273df4", + "85e6a4ec07da45deade08d16cc6eef61", + "f7d9356562064c31acab1b6bb00b7a69", + "b7088f8adb6d4e0db3bf112a11bfb2fe", + "44d8945ab43c456093a1a2c25e020463", + "91236763ae8a481982409ca37aabfdc6", + "ca0b9f0b91544d98bce5968b606f2fbb", + "4d9a3ea5e7054175870ed1827cb11aff", + "4f3c81150aff4f1bb51a108b8d66179a", + "0caba3aa88fb44a4b449f1deff0e77f7", + "e2df6d112d4b47eb845fa7a9f78cf821", + "86de6d4aa4bc4e14a4ec52969739d6ca", + "6f24ebc5ab6f46c2b380070325c893d7", + "b616cab018e646e2b138ad3b746a6616", + "915793ec7e324a4d8cf6d085e0c161c6", + "d1f277973f0f4bd1a013009e6fb296b1", + "11b7bb4b6eef4f6d89f853792342c126", + "477a3264053345ed84e0e1c2446409bf", + "dedc22fcd16d4f6d8d1f58f2a587b8db", + "a45f0dccae9340a185d58ddeb9209039", + "680d40e1dc86411d8da97903d6386d9b", + "a4af94e1834747dd8e523e8b3025608b", + "7c76cd40a8584d4d876c027c972060fd", + "02bb96f01b524da68d893596f26f609b", + "16c9e8b36d6042348a7e91aa85ee9ee2", + "f073119ca5c94362897eda2f2c245e9f", + "a9cef28515db48c8975a809b3d35e174", + "23c727feada1401e902a9c3056267376", + "9d1ab73f90c44d0eb0833ee96b57ed38", + "d6547cdb382c40d8beff462d009bdae0", + "73816aeaaa8847f084e8cad15cd0d957", + "270989562a5c4f1d835b0b5da15aed86", + "534906f008474c2090930d058a261a37", + "2289e8cf8dbf499aa8e778b1aa88dbac", + "a8cbcd4b56144be0bbfe320eb581c9cf", + "21bdb4ecd8274ec9b67f1281d6383529", + "1db6eca3754d4754bdf68a06d49754ed", + "d073e859935849e0800a7d4ede2fb8ec", + "91f734f319f54df3b3a6b396337bbfac", + "4bbbf1e5d5c4490695b42526f4cf5269", + "b94cb23cc9f546c2b1e370ed3fd16e33", + "1f247f8d0b064ad6a8907b882eb21a13", + "7ff2d4d5d76b4bf98d5185063c113ef4", + "c6dcb311005b42e98d12ae53d8139600", + "3e0a0055fdb64ba5864cb9db5071a6ac", + "ba2bfc83dfa44868a4119d55cc0400bd", + "9b7d4215bda64c189f8f7ca90e80072c", + "b158d65ffb754442a11b1906bfa09c38", + "e4c1a8e9d7624d2ba97c5158eebb6bdb", + "64a8b8cb2a6d4feabb26b8b9b1cbfeaf", + "b01c58c8d7f2443e8305834526355a0c", + "d2c1e60c41844a18b82f0cb28880b916", + "be4390e933dc45b8a4d7b1ebea712aa3", + "2558716cf8c8437590a8594e6e5d5e98", + "afd3848573814a008ba6f8f3e906bf6b", + "087cb104dc444f6484923bcdb567b8fa", + "01c9984f7d7e46129f7f26a79dd17755", + "992513aa0e9c4baa96cb5191e045a082", + "b0090c94753d41dabd9ac2dd423e9201", + "41039a29e2e74c95a8af8f6e1b2ffb11", + "d09ba93de344459f99b09f5f5ac37f3e", + "09962b3ebab64b1f8dfafae83a294cc2", + "17fc66ed0df348cf92223bfeae38fbc0", + "240ea684bf644ad39fa1dbb9df4349f1", + "6f8ade98160e4143989bda894b0a170e", + "2ae17bbf2db2418bb0a1552a35485b09", + "1825818985064b028a4be6220958086c", + "f53717f5ffa547ae812e5ac208c2064a", + "c83df788a5ca47a2af0434b9b9696ef8", + "2e823e9f707a4497b52a39f8085167e5", + "36b753ceca714ee09786fd806939af01", + "93a4b3cb88eb41659d0f2b0761d70426", + "b710ea11c6ca49e39b28423b2e7de06d", + "e370bffadf8a444eb0083b3337e19579", + "a2fdd5e9d6964b18b2064f3ead1cb60a", + "84238eff2dfd48d4aebc73a5c057b871", + "062f69c14c2e459ba6dd79cf96e36213", + "00e03185b187495688cfbb21de2aa0d1", + "72c526138af3440d93bac8a5db9f72a5", + "902061e0c6b147ddb9e7c0c28b68b045", + "346e74103d7d4b0abca523841fad013a", + "f0f93bbdbc494d89bcf54751f4615abd", + "c954d22b66e4457cb1c661ed7b7c8ef8", + "f9df48ee9bb7405984197bbf584b88f1", + "6e5548d364044237985c6eea263828ca", + "cfc02aade9e34d96b4c031cd3487888a", + "ac86c3f3f5f04dd08d9044a3ad31e061", + "919a3d32344d4647ae0656b5682cccf2", + "dcec9feb3df64446822c89184dc13f0f", + "58d9ee30d6b04072a71c39b4b1ab0a2a", + "8ce69d8b62894c55b1940b9f2e90ae4a", + "f928225848a84bc7a90a29e48ac7c0e0", + "037f8bbdd54e4cbf9b530b4a09e0b1d5", + "fe462e23b1e449889b1544c8ef540bb0", + "e022787742b340d49474e2659755ce37" + ] + }, + "id": "b4701b15", + "outputId": "fa28abf3-6d42-498c-ace4-ed3f2356bde3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0.00B [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "3750ae40cd39499ab50991df5946d2c4" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "pytorch_model.bin: 0%| | 0.00/605M [00:00" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Display the first batch of images from `test_loader`\n", + "\n", + "def show_batch(loader):\n", + " images, labels = next(iter(loader))\n", + " images = images.cpu() # Move images to CPU for plotting\n", + " # Renormalize to [0, 1] for visualization\n", + " images = (images - images.min()) / (images.max() - images.min())\n", + " _, axes = plt.subplots(1, len(images), figsize=(15, 5))\n", + " for ax, img, label in zip(axes, images, labels):\n", + " ax.imshow(img.permute(1, 2, 0))\n", + " ax.set_title(CLASS_NAMES[label.item()])\n", + " ax.axis('off')\n", + " plt.show()\n", + "\n", + "show_batch(test_loader)" + ] + }, + { + "cell_type": "markdown", + "id": "e1183726", + "metadata": { + "id": "e1183726" + }, + "source": [ + "We’re now ready to run our zero-shot classification baseline!\n", + "\n", + "# Brief Introduction to Zero-Shot Classification\n", + "\n", + "In Assignment 1, we followed the typical machine-learning pipeline: we trained a CNN on the Fashion-MNIST dataset, using labelled examples to update the model’s weights. While effective, that approach requires a curated, task-specific training set—a luxury you don’t always have in practice.\n", + "\n", + "Zero-shot classification flips the script. A large vision–language model (VLM) such as **CLIP** is first pre-trained on hundreds of millions of image–text pairs scraped from the web. Because it learns *joint* visual–textual embeddings, the model can later solve new tasks simply by “measuring” how similar an image is to a **text prompt** that describes each candidate class—without seeing a single task-labelled example.\n", + "\n", + "**How it works** \n", + "1. Feed an image through CLIP’s vision encoder → **image feature**. \n", + "2. Feed a textual prompt (e.g. “a photo of a sandal”) through CLIP’s text encoder → **text feature**. \n", + "3. Compute cosine similarity between the image feature and every class’s text feature. \n", + "4. Pick the class whose prompt is most similar.\n", + "\n", + "For our first attempt, we’ll use the bare class names as prompts, e.g.:\n", + "\n", + "- \"T-shirt/top\"\n", + "- \"Trouser\"\n", + "\n", + "### You should:\n", + "\n", + "- [ ] Build embeddings: use the `get_text_embeddings` helper function to create text embeddings for the class names.\n", + "- [ ] Run inference: use the `get_image_embeddings` helper function to create image embeddings.\n", + "- [ ] Compute cosine similarity: complete and use the `get_cosine_similarity` helper function to compute the cosine similarity between the image and text embeddings.\n", + "- [ ] Make predictions: use the `get_predictions` helper function to get the predicted class for each image in the batch.\n", + "\n", + "Note that for normalized vectors like the ones we are using, cosine similarity is equivalent to the dot product. This means we can use the handy formula `cosine_similarity = vector_a @ vector_b.T` to compute the similarity between the image and text embeddings." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4c3acc29", + "metadata": { + "id": "4c3acc29" + }, + "outputs": [], + "source": [ + "def get_text_embeddings(class_names: list[str]) -> torch.Tensor:\n", + " \"\"\" Get text embeddings for the given class names using CLIP.\n", + " Args:\n", + " class_names (list[str]): List of class names to encode.\n", + " Returns:\n", + " torch.Tensor: Normalized text embeddings for the class names.\n", + " \"\"\"\n", + " tokenized = clip_processor(text=class_names,\n", + " padding=True,\n", + " return_tensors=\"pt\").to(device)\n", + "\n", + " with torch.no_grad():\n", + " text_embeddings = clip_model.get_text_features(**tokenized)\n", + "\n", + " text_feats = text_embeddings / text_embeddings.norm(dim=-1, keepdim=True)\n", + "\n", + " return text_feats\n", + "\n", + "def get_image_embeddings(images: torch.Tensor) -> torch.Tensor:\n", + " \"\"\" Get image embeddings for the given images using CLIP.\n", + " Args:\n", + " images (torch.Tensor): Batch of images to encode.\n", + " Returns:\n", + " torch.Tensor: Normalized image embeddings for the images.\n", + " \"\"\"\n", + " with torch.no_grad():\n", + " image_embeddings = clip_model.get_image_features(pixel_values=images)\n", + "\n", + " image_feats = image_embeddings / image_embeddings.norm(dim=-1, keepdim=True)\n", + "\n", + " return image_feats" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "16653654", + "metadata": { + "id": "16653654" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def get_cosine_similarity(image_feats: torch.Tensor, text_feats: torch.Tensor) -> np.ndarray:\n", + " \"\"\"\n", + " Compute cosine similarity between image features and text features.\n", + " Args:\n", + " image_feats (torch.Tensor): Image features of shape (N, D).\n", + " text_feats (torch.Tensor): Text features of shape (M, D).\n", + " Returns:\n", + " numpy.ndarray: Cosine similarity matrix of shape (N, M), where N is the number of images and M is the number of text prompts.\n", + " \"\"\"\n", + " image_feats = image_feats.cpu() # Ensure image features are on CPU\n", + " text_feats = text_feats.cpu() # Ensure text features are on CPU\n", + "\n", + " # Compute cosine similarity, which is the dot product of normalized vectors\n", + " return image_feats @ text_feats.T\n", + "\n", + "def get_predictions(similarity: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Get predictions based on cosine similarity scores.\n", + " Args:\n", + " similarity (numpy.ndarray): Cosine similarity matrix of shape (N, M), where N is the number of images and M is the number of text prompts.\n", + " Returns:\n", + " numpy.ndarray: Predicted class indices for each image, shape (N,).\n", + " \"\"\"\n", + " # Get the index of the maximum similarity for each image\n", + " return np.argmax(similarity, axis=1)\n" + ] + }, + { + "cell_type": "markdown", + "id": "55d9e1de", + "metadata": { + "id": "55d9e1de" + }, + "source": [ + "With these functions complete, you are ready to run the zero-shot classification baseline. Complete the code to follow these steps:\n", + "\n", + "- [ ] Build text embeddings for the class names using the `get_text_embeddings` function (this only needs to be done once).\n", + "- [ ] For each batch of images:\n", + " - [ ] Get image embeddings using the `get_image_embeddings` function.\n", + " - [ ] Compute cosine similarity between the image and text embeddings using the `get_cosine_similarity` function.\n", + " - [ ] Save the predictions so that we can build a confusion matrix later.\n", + "- [ ] Report the accuracy of the predictions and the confusion matrix using the `accuracy_score` and `confusion_matrix` functions from `sklearn.metrics`." + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import accuracy_score, confusion_matrix\n", + "\n", + "y_true, y_pred = [], []\n", + "\n", + "# Build text embeddings for the class names\n", + "text_features = get_text_embeddings(CLASS_NAMES)\n", + "\n", + "for pixel_values, labels in test_loader:\n", + " # Get image embeddings\n", + " image_features = get_image_embeddings(pixel_values)\n", + "\n", + " # Compute cosine similarity\n", + " similarity = get_cosine_similarity(image_features, text_features)\n", + "\n", + " # Get predictions\n", + " predictions = get_predictions(similarity)\n", + "\n", + " # Save the predictions and true labels\n", + " y_true.extend(labels.tolist())\n", + " y_pred.extend(predictions.tolist())\n", + "\n", + "# Report the accuracy of the predictions\n", + "accuracy = accuracy_score(y_true, y_pred)\n", + "print(f\"Accuracy: {accuracy:.4f}\")\n", + "\n", + "# Report the confusion matrix\n", + "def plot_confusion_matrix(y_true, y_pred, class_names):\n", + " cm = confusion_matrix(y_true, y_pred)\n", + " plt.figure(figsize=(10, 8))\n", + " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", + " plt.title('Confusion Matrix')\n", + " plt.colorbar()\n", + " tick_marks = np.arange(len(class_names))\n", + " plt.xticks(tick_marks, class_names, rotation=45)\n", + " plt.yticks(tick_marks, class_names)\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_confusion_matrix(y_true, y_pred, CLASS_NAMES)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 825 + }, + "id": "67jlM9d3nXqL", + "outputId": "131bd7bd-0a45-4e58-d305-2006484a365c" + }, + "id": "67jlM9d3nXqL", + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Accuracy: 0.6240\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "id": "0857f4a8", + "metadata": { + "id": "0857f4a8" + }, + "source": [ + "Reflection: Consider the results. How does the performance of this zero-shot baseline compare to the CNN you trained in Assignment 1? What are the strengths and weaknesses of this approach?" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Reflection: The accurtacy of the trained CNN baseline in Assignment-1 was 0.25% compared to this zero-shot CLIP model baseline accuracy of 62.40%. This is dramatically better model.\n", + "\n", + "Strength: The zero-shot model required no task-specific training and is off-the-shelf and trained on learned concepts and not just 10 categories.\n", + "\n", + "Weakness: 62.4% is not perfect. The confusion matrix shows it still struggles to tell similar items apart like \"Shirt\" and \"T-shirt/top\" or \"Sandal\" and \"Sneaker\"." + ], + "metadata": { + "id": "17X6qPx3apKI" + }, + "id": "17X6qPx3apKI" + }, + { + "cell_type": "markdown", + "id": "599e16af", + "metadata": { + "id": "599e16af" + }, + "source": [ + "## Improving Zero-Shot Classification with Prompt Engineering\n", + "\n", + "In the previous section, we directly used the class names as text prompts for zero-shot classification. However, we can often improve performance by crafting more descriptive prompts that better capture the visual characteristics of each class. For example, instead of just \"T-shirt/top\", we could use \"a photo of a T-shirt\" or \"a photo of a top\". This additional context can help the model make more accurate predictions.\n", + "\n", + "In this section, we will experiment with more detailed prompts for each class to see if we can improve the zero-shot classification performance. You should:\n", + "\n", + "- [ ] Create a list of improved prompts for each class. For example, instead of just \"T-shirt/top\", you could use \"a photo of a T-shirt\" or \"a photo of a top\".\n", + "- [ ] Use the `get_text_embeddings` function to create text embeddings for the improved prompts.\n", + "- [ ] Run the zero-shot classification baseline again using the improved prompts and report the accuracy and confusion matrix.\n", + "\n", + "Note: Take advantage of the confusion matrix above. If two classes are often confused, consider how you might improve the prompts to help the model distinguish them better.\n", + "\n", + "The aim for this section is for you to improve the performance of the model. However, if you find that the performance does not improve significantly, you can still reflect on the process and consider how you might further refine the prompts with more effort." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5abf5cc1", + "metadata": { + "id": "5abf5cc1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 825 + }, + "outputId": "170eae40-9229-4017-98ca-ce49b446feb2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Accuracy with improved prompts: 0.6477\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Improved prompts\n", + "improved_CLASS_NAMES = [\n", + " # Baseline prompt:\n", + " # \"T-shirt/top\",\n", + " # \"Trouser\",\n", + " # \"Pullover\",\n", + " # \"Dress\",\n", + " # \"Coat\",\n", + " # \"Sandal\",\n", + " # \"Shirt\",\n", + " # \"Sneaker\",\n", + " # \"Bag\",\n", + " # \"Ankle boot\"\n", + "\n", + " #Improved prompt:\n", + "\n", + " \"a photo of a casual short-sleeve T-shirt\", # 0: T-shirt/top Confused with Shirt, Pullover, Coat\n", + " \"a photo of a pair of trousers or pants\", # 1: Trouser Generally distinct\n", + " \"a photo of a long-sleeve knit pullover sweater\", # 2: Pullover Confused with T-shirt/top\n", + " \"a photo of a one-piece dress\", # 3: Dress Generally distinct\n", + " \"a photo of a heavy outerwear coat\", # 4: Coat Confused with Shirt\n", + " \"a photo of an open-toed sandal\", # 5: Sandal Confused with Sneaker, Ankle boot\n", + " \"a photo of a collared, button-down shirt\", # 6: Shirt Confused with T-shirt/top, Coat\n", + " \"a photo of an athletic lace-up sneaker\", # 7: Sneaker Confused with Sandal, Ankle boot\n", + " \"a photo of a handbag or purse\", # 8: Bag Generally distinct\n", + " \"a photo of a boot that covers the ankle\" # 9: Ankle boot Confused with Sneaker, Sandal\n", + "]\n", + "\n", + "y_true_improved, y_pred_improved = [], []\n", + "\n", + "# Build text embeddings for the improved class names\n", + "text_features_improved = get_text_embeddings(improved_CLASS_NAMES)\n", + "\n", + "for pixel_values, labels in test_loader:\n", + " # Get image embeddings\n", + " image_features_improved = get_image_embeddings(pixel_values)\n", + "\n", + " # Compute cosine similarity\n", + " similarity_improved = get_cosine_similarity(image_features_improved, text_features_improved)\n", + "\n", + " # Get predictions\n", + " predictions_improved = get_predictions(similarity_improved)\n", + "\n", + " # Save the predictions and true labels\n", + " y_true_improved.extend(labels.tolist())\n", + " y_pred_improved.extend(predictions_improved.tolist())\n", + "\n", + "# Report the accuracy of the predictions\n", + "accuracy_improved = accuracy_score(y_true_improved, y_pred_improved)\n", + "print(f\"Accuracy with improved prompts: {accuracy_improved:.4f}\")\n", + "\n", + "# Report the confusion matrix\n", + "plot_confusion_matrix(y_true_improved, y_pred_improved, CLASS_NAMES)" + ] + }, + { + "cell_type": "markdown", + "id": "28e88284", + "metadata": { + "id": "28e88284" + }, + "source": [ + "Reflection: How did your detailed prompts affect the zero-shot classification performance? Did you see a significant improvement compared to the baseline? What insights did you gain about the model's understanding of the classes? Do you think that with more effort you could further improve the performance? If so, how?" + ] + }, + { + "cell_type": "markdown", + "source": [ + "The accuracy with the improved prompts is 64.77% which is a slight improvement compared to the baseline accuracy of 62.40%.\n", + "\n", + "The confusion matrix shows some changes in how the model is predicting the classes. For instance, the confusion between \"T-shirt/top\" and \"Shirt\" seems to have decreased slightly compared to the baseline. Also the confusion between \"Sandal\" and \"Sneaker\" also appears to be reduced.\n", + "\n", + "The improved prompts have helped the model distinguish between some of the more confusable classes. However, there is still room for improvement, and some confusions, like those between \"Ankel boot\" and \"Sneaker\", or \"Coat\" and \"Pullover\" still persist.\n", + "\n", + "Improvments: Multiple-Description Classification (suggested Mini-Experiment below). Instead of one prompt per class we create multiple descriptive prompts for each." + ], + "metadata": { + "id": "Ya8Lzu_eodso" + }, + "id": "Ya8Lzu_eodso" + }, + { + "cell_type": "markdown", + "id": "e817d7b4", + "metadata": { + "id": "e817d7b4" + }, + "source": [ + "## Visualizing Image Embeddings with UMAP\n", + "\n", + "To better understand how the model perceives the different classes, we can visualize the image embeddings using UMAP (Uniform Manifold Approximation and Projection). UMAP is a dimensionality reduction technique that helps us see how similar or dissimilar the embeddings are in a lower-dimensional space.\n", + "\n", + "By visualizing the embeddings, we can gain insights into how well the model can distinguish certain images, even without considering the text prompts. This can help us identify clusters of similar images and see if there are any overlaps between classes.\n", + "\n", + "You should:\n", + "\n", + "- [ ] Use the `get_image_embeddings` function to get the image embeddings for the entire test set.\n", + "- [ ] Use UMAP to reduce the dimensionality of the image embeddings to 2D.\n", + "- [ ] Plot the 2D embeddings, coloring each point by its true class label.\n", + "\n", + "You may need to install the `umap-learn` library if you haven't already. You can do this by running `pip install umap-learn`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a7a20757", + "metadata": { + "id": "a7a20757", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "651b63be-6d83-4329-93a3-9cfbde948757" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: umap-learn in /usr/local/lib/python3.12/dist-packages (0.5.9.post2)\n", + "Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (2.0.2)\n", + "Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (1.16.3)\n", + "Requirement already satisfied: scikit-learn>=1.6 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (1.6.1)\n", + "Requirement already satisfied: numba>=0.51.2 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (0.60.0)\n", + "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (0.5.13)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from umap-learn) (4.67.1)\n", + "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.12/dist-packages (from numba>=0.51.2->umap-learn) (0.43.0)\n", + "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.12/dist-packages (from pynndescent>=0.5->umap-learn) (1.5.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=1.6->umap-learn) (3.6.0)\n" + ] + } + ], + "source": [ + "# Uncomment the following line to install UMAP if you haven't already\n", + "!pip install umap-learn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5fd3b4b", + "metadata": { + "id": "e5fd3b4b" + }, + "outputs": [], + "source": [ + "from umap import UMAP\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# ------------------------------------------------------------\n", + "# 1. Collect image embeddings\n", + "# ------------------------------------------------------------\n", + "all_img_emb = []\n", + "all_labels = []\n", + "\n", + "with torch.no_grad():\n", + " for pixel_values, labels in test_loader:\n", + " image_features = get_image_embeddings(pixel_values.to(device))\n", + " all_img_emb.append(image_features.cpu().numpy())\n", + " all_labels.append(labels.numpy())\n", + "\n", + "all_img_emb = np.concatenate(all_img_emb)\n", + "all_labels = np.concatenate(all_labels)\n", + "\n", + "# ------------------------------------------------------------\n", + "# 2. Fit UMAP\n", + "# ------------------------------------------------------------\n", + "reducer = UMAP(n_components=2, random_state=42)\n", + "embedding = reducer.fit_transform(all_img_emb)\n", + "\n", + "# ------------------------------------------------------------\n", + "# 3. Plot coloured by ground-truth label\n", + "# ------------------------------------------------------------\n", + "plt.figure(figsize=(10, 8))\n", + "for i, class_name in enumerate(CLASS_NAMES):\n", + " plt.scatter(embedding[all_labels == i, 0], embedding[all_labels == i, 1], label=class_name, alpha=0.5, s=5)\n", + "\n", + "plt.title('UMAP Projection of Fashion-MNIST Image Embeddings')\n", + "plt.xlabel('UMAP Component 1')\n", + "plt.ylabel('UMAP Component 2')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "source": [ + "# ------------------------------------------------------------\n", + "# Importing cuml instead of umap\n", + "# ------------------------------------------------------------\n", + "from cuml import UMAP\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "\n", + "# ------------------------------------------------------------\n", + "# 1. Collect image embeddings\n", + "# ------------------------------------------------------------\n", + "all_img_emb = []\n", + "all_labels = []\n", + "\n", + "print(\"Collecting all image embeddings (Device: GPU)\")\n", + "with torch.no_grad():\n", + " for pixel_values, labels in test_loader:\n", + " image_features = get_image_embeddings(pixel_values.to(device))\n", + "\n", + " all_img_emb.append(image_features)\n", + " all_labels.append(labels)\n", + "\n", + "all_img_emb_gpu = torch.cat(all_img_emb)\n", + "all_labels_cpu = torch.cat(all_labels)\n", + "print(f\"Collected {len(all_img_emb_gpu)} embeddings on the GPU.\")\n", + "\n", + "# ------------------------------------------------------------\n", + "# 2. Fit UMAP\n", + "# ------------------------------------------------------------\n", + "reducer = UMAP(n_components=2, random_state=42)\n", + "\n", + "print(\"Running UMAP on GPU\")\n", + "embedding_gpu = reducer.fit_transform(all_img_emb_gpu)\n", + "print(\"GPU UMAP Done!\")\n", + "\n", + "\n", + "# ------------------------------------------------------------\n", + "# 3. Plot coloured by ground-truth label\n", + "# ------------------------------------------------------------\n", + "\n", + "embedding_cpu = embedding_gpu.get() # .get() converts CuPy array to NumPy\n", + "all_labels_numpy = all_labels_cpu.numpy()\n", + "\n", + "plt.figure(figsize=(10, 8))\n", + "for i, class_name in enumerate(CLASS_NAMES):\n", + " # Use the NumPy arrays for plotting\n", + " mask = (all_labels_numpy == i)\n", + " plt.scatter(embedding_cpu[mask, 0], embedding_cpu[mask, 1], label=class_name, alpha=0.5, s=5)\n", + "\n", + "plt.title('Projection of Fashion-MNIST')\n", + "plt.xlabel('UMAP Component 1')\n", + "plt.ylabel('UMAP Component 2')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 807 + }, + "id": "FmuhARQVywHi", + "outputId": "47988497-e7f9-4949-e29f-5a7324269636" + }, + "id": "FmuhARQVywHi", + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting all image embeddings (Device: GPU)\n", + "Collected 10000 embeddings on the GPU.\n", + "[2025-11-02 04:23:34.254] [CUML] [info] build_algo set to brute_force_knn because random_state is given\n", + "Running UMAP on GPU\n", + "GPU UMAP Done!\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The UMAP embeddings allow us to see how separable or non-separable different classes are with our specific model. If two specific images are very similar, then they will be placed near each other on this graph.\n", + "\n", + "Reflection: Do you notice any challenges in distinguishing images based on this figure? Are there any types of clothing in the dataset which the model has no trouble distinguishing from the others?" + ], + "metadata": { + "id": "vhrav-ep1K65" + }, + "id": "vhrav-ep1K65" + }, + { + "cell_type": "markdown", + "source": [ + "Reflection: The Visualizing Image Embeddings with UMAP figure shows significant challenges in distinguishing several classes. The model's image embeddings for many classes are not clearly separable leading to two large overlapping clusters:\n", + "\n", + "The upper-body cluster: In the top-left of the plot the turquoise dots \"Ankel boot\" and grey dots \"Sneaker\" and brown dots \"Sandal\" are mixed. This shows that the model visually perceives these two classes as being similar, which explains why they were the most confused pair in the confusion matrix.\n", + "\n", + "The central cluster: The large mass in the middle is a mix of many different classes. Specifically \"T-shirt/top\", \"Shirt,\" \"Pullover,\" \"Coat,\" and \"Dress\".\n", + "\n", + "The \"Trouser\" and \"Bag\" are in their own isolated clusters. The model sees them as visually unique and has no trouble distinguishing them from everything else." + ], + "metadata": { + "id": "ca_gxEzl1OLD" + }, + "id": "ca_gxEzl1OLD" + }, + { + "cell_type": "markdown", + "id": "132f943e", + "metadata": { + "id": "132f943e" + }, + "source": [ + "\n", + "\n", + "## Mini-Experiment\n", + "\n", + "In this section, you will conduct a mini-experiment of your choice to further explore the capabilities of zero-shot classification with transformers. This can be anything you'd like, but here are some ideas to get you started.\n", + "\n", + "### A. Alternative Model\n", + "\n", + "So far we have been utilizing OpenAI's CLIP model for zero-shot classification. However, there are many other vision–language models available in the `transformers` library that you can experiment with. For example, there are larger CLIP models such as [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14), and open-source versions such as [laion/CLIP-ViT-B-32-laion2B-s34B-b79K](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K). You can also search huggingface [here](https://huggingface.co/models?sort=trending&search=clip) to find other models that might be suitable for zero-shot classification.\n", + "\n", + "You can try using a different model to see if it improves the zero-shot classification performance. You should:\n", + "- [ ] Load a different model and processor from the `transformers` library.\n", + "- [ ] Run the zero-shot classification baseline with the new model and report the accuracy and confusion matrix.\n", + "- [ ] Reflect on the performance of the new model compared to the original CLIP model\n", + " - How does the new model perform compared to the original CLIP model?\n", + " - Do you notice any differences in the types of errors made by the new model?\n", + "\n", + "### B. Multiple-Description Classification\n", + "\n", + "Another interesting experiment is to explore multiple-description classification. *This involves providing multiple text prompts for each class, allowing the model to choose the most relevant one. For example, instead of just \"T-shirt/top\", you could provide \"a photo of a T-shirt\", \"a photo of a top\", and \"a photo of a shirt\". This can help the model better understand the class and increases the likelihood of a correct prediction. You should:\n", + "\n", + "- [ ] Create a list of multiple prompts for each class.\n", + "- [ ] Use the `get_text_embeddings` function to create text embeddings for the multiple prompts.\n", + "- [ ] Run the zero-shot classification baseline again using the multiple prompts and report the accuracy and confusion matrix.\n", + "- [ ] Consider the model to be correct if it guesses *any* of the prompts belonging to the correct class.\n", + "\n", + "### C. Top-K Classification\n", + "\n", + "In some classification tasks, it can be useful to consider if the right answer is among the top K (e.g. top 3) predictions. This can be particularly useful in cases where the model is uncertain or when there are multiple similar classes. You should:\n", + "\n", + "- [ ] Modify the `get_predictions` function to return the top K predictions for each image.\n", + "- [ ] Modify the accuracy calculation to consider the model correct if the true class is among the top K predictions.\n", + "- [ ] Report the accuracy and confusion matrix for the top K predictions. Report at least two different values of K (e.g. K=2 and K=4).\n", + "\n", + "### D. Other Ideas\n", + "\n", + "You are welcome to come up with your own mini-experiment! Explain your idea in the report and implement it. Did it work as you expected? What did you learn from it?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "94af85f8", + "metadata": { + "id": "94af85f8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "a13bc2af-491c-4a91-eb8b-e615337e7e22" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loading new model: openai/clip-vit-large-patch14...\n", + "New model loaded and moved to device.\n", + "\n", + "Running BASELINE classification with LARGE model...\n", + "LARGE Model - Baseline Accuracy: 0.5952\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Running IMPROVED prompt classification with LARGE model...\n", + "LARGE Model - Improved Prompt Accuracy: 0.7037\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "from transformers import CLIPModel, CLIPProcessor\n", + "import torch\n", + "from sklearn.metrics import accuracy_score, confusion_matrix\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# ------------------------------------------------------------\n", + "# 1. Loading new larger model and processor\n", + "# ------------------------------------------------------------\n", + "large_model_name = \"openai/clip-vit-large-patch14\"\n", + "\n", + "print(f\"Loading new model: {large_model_name}\")\n", + "\n", + "large_model = CLIPModel.from_pretrained(large_model_name)\n", + "large_processor = CLIPProcessor.from_pretrained(large_model_name, use_fast=False)\n", + "\n", + "large_model.eval()\n", + "large_model.to(device)\n", + "print(\"Model loaded and moved to device: GPU\")\n", + "\n", + "# ------------------------------------------------------------\n", + "# We must re-define the helper functions to use the NEW model\n", + "# ------------------------------------------------------------\n", + "\n", + "def get_text_embeddings_large(class_names: list[str]) -> torch.Tensor:\n", + " \"\"\"Get text embeddings using the LARGE model.\"\"\"\n", + " tokenized = large_processor(text=class_names,\n", + " padding=True,\n", + " return_tensors=\"pt\").to(device)\n", + " with torch.no_grad():\n", + " text_embeddings = large_model.get_text_features(**tokenized)\n", + " text_feats = text_embeddings / text_embeddings.norm(dim=-1, keepdim=True)\n", + " return text_feats\n", + "\n", + "def get_image_embeddings_large(images: torch.Tensor) -> torch.Tensor:\n", + " \"\"\"Get image embeddings using the LARGE model.\"\"\"\n", + " with torch.no_grad():\n", + " image_embeddings = large_model.get_image_features(pixel_values=images)\n", + " image_feats = image_embeddings / image_embeddings.norm(dim=-1, keepdim=True)\n", + " return image_feats\n", + "\n", + "# get_cosine_similarity, get_predictions, and plot_confusion_matrix\n", + "# are unchanged as they work on the output embeddings.\n", + "\n", + "# ------------------------------------------------------------\n", + "# 2. Running Baseline classification with new model\n", + "# ------------------------------------------------------------\n", + "print(\"\\nRunning Baseline classification with LARGE model.\")\n", + "y_true_large_base, y_pred_large_base = [], []\n", + "\n", + "text_features_large_base = get_text_embeddings_large(CLASS_NAMES)\n", + "\n", + "for pixel_values, labels in test_loader:\n", + " image_features = get_image_embeddings_large(pixel_values)\n", + " similarity = get_cosine_similarity(image_features, text_features_large_base)\n", + " predictions = get_predictions(similarity)\n", + "\n", + " y_true_large_base.extend(labels.tolist())\n", + " y_pred_large_base.extend(predictions.tolist())\n", + "\n", + "# Reporting accuracy\n", + "accuracy_large_base = accuracy_score(y_true_large_base, y_pred_large_base)\n", + "print(f\"Baseline Accuracy [LARGE Model]: {accuracy_large_base:.4f}\")\n", + "\n", + "# Report confusion matrix\n", + "plot_confusion_matrix(y_true_large_base, y_pred_large_base, CLASS_NAMES)\n", + "\n", + "# ------------------------------------------------------------\n", + "# 3. Running Improved prompt classification with the new model\n", + "# ------------------------------------------------------------\n", + "print(\"\\nRunning improved prompt classification with LARGE model.\")\n", + "y_true_large_imp, y_pred_large_imp = [], []\n", + "\n", + "text_features_large_imp = get_text_embeddings_large(improved_CLASS_NAMES)\n", + "\n", + "for pixel_values, labels in test_loader:\n", + " image_features = get_image_embeddings_large(pixel_values)\n", + " similarity = get_cosine_similarity(image_features, text_features_large_imp)\n", + " predictions = get_predictions(similarity)\n", + "\n", + " y_true_large_imp.extend(labels.tolist())\n", + " y_pred_large_imp.extend(predictions.tolist())\n", + "\n", + "# Reporting accuracy\n", + "accuracy_large_imp = accuracy_score(y_true_large_imp, y_pred_large_imp)\n", + "print(f\"Improved Prompt Accuracy [LARGE Model]: {accuracy_large_imp:.4f}\")\n", + "\n", + "# Reporting confusion matrix\n", + "plot_confusion_matrix(y_true_large_imp, y_pred_large_imp, CLASS_NAMES)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Mini-Experiment: Alternative Model\n", + "\n", + "Model: openai/clip-vit-large-patch14\n", + "\n", + "Original Model (base):\n", + "- Baseline Accuracy: 0.6240 (62.40%)\n", + "- Improved Accuracy: 0.6477 (64.77%)\n", + "- Gain from Prompts: +2.37%\n", + "\n", + "New Model (large):\n", + "- Baseline Accuracy: 0.5952 (59.52%)\n", + "- Improved Accuracy: 0.7037 (70.37%)\n", + "- Gain from Prompts: +10.85%\n", + "\n", + "Observation:\n", + "- The new large model was actually worse when using the simple baseline prompts (59.52% vs. 62.40%).\n", + "- The new large model accuracy jumped +10.85% (from 59.52% to 70.37%). This is a much larger improvement than the base model's +2.37% gain. This shows the large model has a deeper understanding of language and can better use the specific details prompts to tell the images apart.\n", + "-Overall, the large model has higher performance with a higher final accuracy (70.37% vs. 64.77%)." + ], + "metadata": { + "id": "C_31xYva-UAd" + }, + "id": "C_31xYva-UAd" + }, + { + "cell_type": "markdown", + "id": "234eab38", + "metadata": { + "id": "234eab38" + }, + "source": [ + "### Short Report\n", + "\n", + "In this section, you will write a short report summarizing your findings from the mini-experiment. The report should include the following sections:\n", + "\n", + "- **Introduction**: Briefly describe the mini-experiment you conducted and its objectives.\n", + "- **Methodology**: Explain the steps you took to conduct the experiment, including any modifications you made to the code or model.\n", + "- **Results**: Present the results of your experiment.\n", + "- **Discussion**: Reflect on the performance of the model and the implications of your findings. Consider the strengths and weaknesses of zero-shot transformers versus a trained CNN." + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Introduction\n", + "\n", + "For the mini-experiment, I conducted **\"Alternative Model\" (Option A)**. \n", + "The objective was to compare the zero-shot classification performance of the original `openai/clip-vit-base-patch32` model with a larger, more powerful model, `openai/clip-vit-large-patch14`, on the **Fashion-MNIST** dataset.\n", + "\n", + "---\n", + "\n", + "# Methodology\n", + "\n", + "The experiment involved modifying the initial setup code to load the `openai/clip-vit-large-patch14` model and its corresponding processor. \n", + "The entire classification pipeline was then re-executed:\n", + "\n", + "1. **Baseline accuracy** was re-established using the simple, single-word `CLASS_NAMES`. \n", + "2. **Improved accuracy** was then measured using the same set of descriptive `improved_CLASS_NAMES` to evaluate how the new model responded to prompt engineering.\n", + "\n", + "---\n", + "\n", + "# Results\n", + "\n", + "The results showed a significant difference in how the two models performed:\n", + "\n", + "| Model | Baseline Accuracy (Simple Prompts) | Improved Accuracy (Detailed Prompts) | Gain from Prompts |\n", + "|----------------------|------------------------------------|--------------------------------------|-------------------|\n", + "| base-patch32 (Original) | 62.40% | 64.77% | +2.37% |\n", + "| large-patch14 (New) | 59.52% | 70.37% | +10.85% |\n", + "\n", + "The new large model had a worse baseline accuracy but responded dramatically better to the improved prompts, achieving a much higher final accuracy.\n", + "\n", + "---\n", + "\n", + "# Discussion\n", + "\n", + "The large model's poor baseline (59.52%) suggests that a more complex model can be more easily confused by simple prompts. \n", + "However, its **+10.85% accuracy gain** from prompt engineering shows it has a far better language understanding. It was able to use specific prompt details (e.g., *\"long-sleeve\"*, *\"open-toed\"*) to overcome visual ambiguities, resulting in a better overall accuracy of **70.37%**.\n", + "\n", + "This experiment highlights the main strength of zero-shot transformers achieving **70% accuracy on a dataset without any task-specific training** is powerful compared to a trained CNN that failed to learn. \n", + "The main weakness remains a high sensitivity to prompt quality as the UMAP plot showed difficulty in separating similar classes (like *\"Shirt\"* and *\"T-shirt\"*).\n" + ], + "metadata": { + "id": "Layz3zyoBGaE" + }, + "id": "Layz3zyoBGaE" + }, + { + "cell_type": "markdown", + "id": "745659f3", + "metadata": { + "id": "745659f3" + }, + "source": [ + "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n", + "### Submission Parameters:\n", + "* Submission Due Date: `23:59 PM - 02/11/2025`\n", + "* The branch name for your repo should be: `assignment-2`\n", + "* What to submit for this assignment:\n", + " * This Jupyter Notebook (assignment_2.ipynb)\n", + " * The Lab 4 notebook (labs/lab_4.ipynb)\n", + " * The Lab 5 notebook (labs/lab_5.ipynb)\n", + " * The Lab 6 notebook (labs/lab_6.ipynb)\n", + "* What the pull request link should look like for this assignment: `https://github.com//deep_learning/pull/`\n", + "* Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n", + "Checklist:\n", + "- [ ] Created a branch with the correct naming convention.\n", + "- [ ] Ensured that the repository is public.\n", + "- [ ] Reviewed the PR description guidelines and adhered to them.\n", + "- [ ] Verify that the link is accessible in a private browser window.\n", + "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-7-help-ml`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + }, + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "3750ae40cd39499ab50991df5946d2c4": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_45f50bbda4034dee9e57c6ea0679ced6", + "IPY_MODEL_b50a1290227a4270915f679dbb5628f1", + "IPY_MODEL_dadfc824823d42df8b240ecf6565ea34" + ], + "layout": "IPY_MODEL_e3bfa3e2b1e54a488527ff0ae3273df4" + } + }, + "45f50bbda4034dee9e57c6ea0679ced6": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_85e6a4ec07da45deade08d16cc6eef61", + "placeholder": "​", + "style": "IPY_MODEL_f7d9356562064c31acab1b6bb00b7a69", + "value": "config.json: " + } + }, + "b50a1290227a4270915f679dbb5628f1": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_b7088f8adb6d4e0db3bf112a11bfb2fe", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_44d8945ab43c456093a1a2c25e020463", + "value": 1 + } + }, + "dadfc824823d42df8b240ecf6565ea34": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_91236763ae8a481982409ca37aabfdc6", + "placeholder": "​", + "style": "IPY_MODEL_ca0b9f0b91544d98bce5968b606f2fbb", + "value": " 4.19k/? [00:00<00:00, 163kB/s]" + } + }, + "e3bfa3e2b1e54a488527ff0ae3273df4": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "85e6a4ec07da45deade08d16cc6eef61": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f7d9356562064c31acab1b6bb00b7a69": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "b7088f8adb6d4e0db3bf112a11bfb2fe": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "20px" + } + }, + "44d8945ab43c456093a1a2c25e020463": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "91236763ae8a481982409ca37aabfdc6": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ca0b9f0b91544d98bce5968b606f2fbb": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4d9a3ea5e7054175870ed1827cb11aff": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4f3c81150aff4f1bb51a108b8d66179a", + "IPY_MODEL_0caba3aa88fb44a4b449f1deff0e77f7", + "IPY_MODEL_e2df6d112d4b47eb845fa7a9f78cf821" + ], + "layout": "IPY_MODEL_86de6d4aa4bc4e14a4ec52969739d6ca" + } + }, + "4f3c81150aff4f1bb51a108b8d66179a": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6f24ebc5ab6f46c2b380070325c893d7", + "placeholder": "​", + "style": "IPY_MODEL_b616cab018e646e2b138ad3b746a6616", + "value": "pytorch_model.bin: 100%" + } + }, + "0caba3aa88fb44a4b449f1deff0e77f7": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_915793ec7e324a4d8cf6d085e0c161c6", + "max": 605247071, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_d1f277973f0f4bd1a013009e6fb296b1", + "value": 605247071 + } + }, + "e2df6d112d4b47eb845fa7a9f78cf821": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_11b7bb4b6eef4f6d89f853792342c126", + "placeholder": "​", + "style": "IPY_MODEL_477a3264053345ed84e0e1c2446409bf", + "value": " 605M/605M [00:05<00:00, 197MB/s]" + } + }, + "86de6d4aa4bc4e14a4ec52969739d6ca": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "6f24ebc5ab6f46c2b380070325c893d7": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b616cab018e646e2b138ad3b746a6616": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "915793ec7e324a4d8cf6d085e0c161c6": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "d1f277973f0f4bd1a013009e6fb296b1": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "11b7bb4b6eef4f6d89f853792342c126": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "477a3264053345ed84e0e1c2446409bf": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "dedc22fcd16d4f6d8d1f58f2a587b8db": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a45f0dccae9340a185d58ddeb9209039", + "IPY_MODEL_680d40e1dc86411d8da97903d6386d9b", + "IPY_MODEL_a4af94e1834747dd8e523e8b3025608b" + ], + "layout": "IPY_MODEL_7c76cd40a8584d4d876c027c972060fd" + } + }, + "a45f0dccae9340a185d58ddeb9209039": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_02bb96f01b524da68d893596f26f609b", + "placeholder": "​", + "style": "IPY_MODEL_16c9e8b36d6042348a7e91aa85ee9ee2", + "value": "model.safetensors: 100%" + } + }, + "680d40e1dc86411d8da97903d6386d9b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f073119ca5c94362897eda2f2c245e9f", + "max": 605157884, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_a9cef28515db48c8975a809b3d35e174", + "value": 605157884 + } + }, + "a4af94e1834747dd8e523e8b3025608b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_23c727feada1401e902a9c3056267376", + "placeholder": "​", + "style": "IPY_MODEL_9d1ab73f90c44d0eb0833ee96b57ed38", + "value": " 605M/605M [00:07<00:00, 93.9MB/s]" + } + }, + "7c76cd40a8584d4d876c027c972060fd": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "02bb96f01b524da68d893596f26f609b": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "16c9e8b36d6042348a7e91aa85ee9ee2": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f073119ca5c94362897eda2f2c245e9f": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a9cef28515db48c8975a809b3d35e174": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "23c727feada1401e902a9c3056267376": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9d1ab73f90c44d0eb0833ee96b57ed38": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "d6547cdb382c40d8beff462d009bdae0": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_73816aeaaa8847f084e8cad15cd0d957", + "IPY_MODEL_270989562a5c4f1d835b0b5da15aed86", + "IPY_MODEL_534906f008474c2090930d058a261a37" + ], + "layout": "IPY_MODEL_2289e8cf8dbf499aa8e778b1aa88dbac" + } + }, + "73816aeaaa8847f084e8cad15cd0d957": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a8cbcd4b56144be0bbfe320eb581c9cf", + "placeholder": "​", + "style": "IPY_MODEL_21bdb4ecd8274ec9b67f1281d6383529", + "value": "preprocessor_config.json: 100%" + } + }, + "270989562a5c4f1d835b0b5da15aed86": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1db6eca3754d4754bdf68a06d49754ed", + "max": 316, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_d073e859935849e0800a7d4ede2fb8ec", + "value": 316 + } + }, + "534906f008474c2090930d058a261a37": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_91f734f319f54df3b3a6b396337bbfac", + "placeholder": "​", + "style": "IPY_MODEL_4bbbf1e5d5c4490695b42526f4cf5269", + "value": " 316/316 [00:00<00:00, 5.50kB/s]" + } + }, + "2289e8cf8dbf499aa8e778b1aa88dbac": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a8cbcd4b56144be0bbfe320eb581c9cf": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "21bdb4ecd8274ec9b67f1281d6383529": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "1db6eca3754d4754bdf68a06d49754ed": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "d073e859935849e0800a7d4ede2fb8ec": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "91f734f319f54df3b3a6b396337bbfac": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4bbbf1e5d5c4490695b42526f4cf5269": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "b94cb23cc9f546c2b1e370ed3fd16e33": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1f247f8d0b064ad6a8907b882eb21a13", + "IPY_MODEL_7ff2d4d5d76b4bf98d5185063c113ef4", + "IPY_MODEL_c6dcb311005b42e98d12ae53d8139600" + ], + "layout": "IPY_MODEL_3e0a0055fdb64ba5864cb9db5071a6ac" + } + }, + "1f247f8d0b064ad6a8907b882eb21a13": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ba2bfc83dfa44868a4119d55cc0400bd", + "placeholder": "​", + "style": "IPY_MODEL_9b7d4215bda64c189f8f7ca90e80072c", + "value": "tokenizer_config.json: 100%" + } + }, + "7ff2d4d5d76b4bf98d5185063c113ef4": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_b158d65ffb754442a11b1906bfa09c38", + "max": 592, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_e4c1a8e9d7624d2ba97c5158eebb6bdb", + "value": 592 + } + }, + "c6dcb311005b42e98d12ae53d8139600": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_64a8b8cb2a6d4feabb26b8b9b1cbfeaf", + "placeholder": "​", + "style": "IPY_MODEL_b01c58c8d7f2443e8305834526355a0c", + "value": " 592/592 [00:00<00:00, 12.3kB/s]" + } + }, + "3e0a0055fdb64ba5864cb9db5071a6ac": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ba2bfc83dfa44868a4119d55cc0400bd": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9b7d4215bda64c189f8f7ca90e80072c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "b158d65ffb754442a11b1906bfa09c38": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "e4c1a8e9d7624d2ba97c5158eebb6bdb": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "64a8b8cb2a6d4feabb26b8b9b1cbfeaf": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b01c58c8d7f2443e8305834526355a0c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "d2c1e60c41844a18b82f0cb28880b916": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_be4390e933dc45b8a4d7b1ebea712aa3", + "IPY_MODEL_2558716cf8c8437590a8594e6e5d5e98", + "IPY_MODEL_afd3848573814a008ba6f8f3e906bf6b" + ], + "layout": "IPY_MODEL_087cb104dc444f6484923bcdb567b8fa" + } + }, + "be4390e933dc45b8a4d7b1ebea712aa3": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_01c9984f7d7e46129f7f26a79dd17755", + "placeholder": "​", + "style": "IPY_MODEL_992513aa0e9c4baa96cb5191e045a082", + "value": "vocab.json: " + } + }, + "2558716cf8c8437590a8594e6e5d5e98": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_b0090c94753d41dabd9ac2dd423e9201", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_41039a29e2e74c95a8af8f6e1b2ffb11", + "value": 1 + } + }, + "afd3848573814a008ba6f8f3e906bf6b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d09ba93de344459f99b09f5f5ac37f3e", + "placeholder": "​", + "style": "IPY_MODEL_09962b3ebab64b1f8dfafae83a294cc2", + "value": " 862k/? [00:00<00:00, 8.06MB/s]" + } + }, + "087cb104dc444f6484923bcdb567b8fa": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "01c9984f7d7e46129f7f26a79dd17755": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "992513aa0e9c4baa96cb5191e045a082": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "b0090c94753d41dabd9ac2dd423e9201": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "20px" + } + }, + "41039a29e2e74c95a8af8f6e1b2ffb11": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "d09ba93de344459f99b09f5f5ac37f3e": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "09962b3ebab64b1f8dfafae83a294cc2": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "17fc66ed0df348cf92223bfeae38fbc0": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_240ea684bf644ad39fa1dbb9df4349f1", + "IPY_MODEL_6f8ade98160e4143989bda894b0a170e", + "IPY_MODEL_2ae17bbf2db2418bb0a1552a35485b09" + ], + "layout": "IPY_MODEL_1825818985064b028a4be6220958086c" + } + }, + "240ea684bf644ad39fa1dbb9df4349f1": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f53717f5ffa547ae812e5ac208c2064a", + "placeholder": "​", + "style": "IPY_MODEL_c83df788a5ca47a2af0434b9b9696ef8", + "value": "merges.txt: " + } + }, + "6f8ade98160e4143989bda894b0a170e": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2e823e9f707a4497b52a39f8085167e5", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_36b753ceca714ee09786fd806939af01", + "value": 1 + } + }, + "2ae17bbf2db2418bb0a1552a35485b09": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_93a4b3cb88eb41659d0f2b0761d70426", + "placeholder": "​", + "style": "IPY_MODEL_b710ea11c6ca49e39b28423b2e7de06d", + "value": " 525k/? [00:00<00:00, 7.46MB/s]" + } + }, + "1825818985064b028a4be6220958086c": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f53717f5ffa547ae812e5ac208c2064a": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c83df788a5ca47a2af0434b9b9696ef8": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2e823e9f707a4497b52a39f8085167e5": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "20px" + } + }, + "36b753ceca714ee09786fd806939af01": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "93a4b3cb88eb41659d0f2b0761d70426": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b710ea11c6ca49e39b28423b2e7de06d": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "e370bffadf8a444eb0083b3337e19579": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a2fdd5e9d6964b18b2064f3ead1cb60a", + "IPY_MODEL_84238eff2dfd48d4aebc73a5c057b871", + "IPY_MODEL_062f69c14c2e459ba6dd79cf96e36213" + ], + "layout": "IPY_MODEL_00e03185b187495688cfbb21de2aa0d1" + } + }, + "a2fdd5e9d6964b18b2064f3ead1cb60a": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_72c526138af3440d93bac8a5db9f72a5", + "placeholder": "​", + "style": "IPY_MODEL_902061e0c6b147ddb9e7c0c28b68b045", + "value": "special_tokens_map.json: 100%" + } + }, + "84238eff2dfd48d4aebc73a5c057b871": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_346e74103d7d4b0abca523841fad013a", + "max": 389, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_f0f93bbdbc494d89bcf54751f4615abd", + "value": 389 + } + }, + "062f69c14c2e459ba6dd79cf96e36213": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c954d22b66e4457cb1c661ed7b7c8ef8", + "placeholder": "​", + "style": "IPY_MODEL_f9df48ee9bb7405984197bbf584b88f1", + "value": " 389/389 [00:00<00:00, 9.56kB/s]" + } + }, + "00e03185b187495688cfbb21de2aa0d1": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "72c526138af3440d93bac8a5db9f72a5": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "902061e0c6b147ddb9e7c0c28b68b045": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "346e74103d7d4b0abca523841fad013a": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f0f93bbdbc494d89bcf54751f4615abd": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "c954d22b66e4457cb1c661ed7b7c8ef8": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f9df48ee9bb7405984197bbf584b88f1": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "6e5548d364044237985c6eea263828ca": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cfc02aade9e34d96b4c031cd3487888a", + "IPY_MODEL_ac86c3f3f5f04dd08d9044a3ad31e061", + "IPY_MODEL_919a3d32344d4647ae0656b5682cccf2" + ], + "layout": "IPY_MODEL_dcec9feb3df64446822c89184dc13f0f" + } + }, + "cfc02aade9e34d96b4c031cd3487888a": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_58d9ee30d6b04072a71c39b4b1ab0a2a", + "placeholder": "​", + "style": "IPY_MODEL_8ce69d8b62894c55b1940b9f2e90ae4a", + "value": "tokenizer.json: " + } + }, + "ac86c3f3f5f04dd08d9044a3ad31e061": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f928225848a84bc7a90a29e48ac7c0e0", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_037f8bbdd54e4cbf9b530b4a09e0b1d5", + "value": 1 + } + }, + "919a3d32344d4647ae0656b5682cccf2": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fe462e23b1e449889b1544c8ef540bb0", + "placeholder": "​", + "style": "IPY_MODEL_e022787742b340d49474e2659755ce37", + "value": " 2.22M/? [00:00<00:00, 17.4MB/s]" + } + }, + "dcec9feb3df64446822c89184dc13f0f": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "58d9ee30d6b04072a71c39b4b1ab0a2a": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8ce69d8b62894c55b1940b9f2e90ae4a": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f928225848a84bc7a90a29e48ac7c0e0": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "20px" + } + }, + "037f8bbdd54e4cbf9b530b4a09e0b1d5": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "fe462e23b1e449889b1544c8ef540bb0": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "e022787742b340d49474e2659755ce37": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + }, + "accelerator": "GPU" + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 0f1e3b5b4c3baac399c060438a3e645cdf621f7a Mon Sep 17 00:00:00 2001 From: Vincent Van Schaik <193274586+VincentSchaik@users.noreply.github.com> Date: Sun, 2 Nov 2025 13:39:53 -0500 Subject: [PATCH 05/16] =?UTF-8?q?Assignment=202=20=E2=80=93=20Zero-Shot=20?= =?UTF-8?q?Image=20Classification=20with=20Transformers?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 02_activities/assignments/assignment_2.ipynb | 22 ++++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb index 53914c815..3e7d353dc 100644 --- a/02_activities/assignments/assignment_2.ipynb +++ b/02_activities/assignments/assignment_2.ipynb @@ -57,7 +57,7 @@ "outputId": "2f284dfc-d49f-4c40-a1bf-1e518d6b8e42" }, "id": "UUQ0KHWYxwaj", - "execution_count": 1, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "b4701b15", "metadata": { "colab": { @@ -446,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "f2da3fba", "metadata": { "colab": { @@ -508,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "21d97e23", "metadata": { "colab": { @@ -588,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "4c3acc29", "metadata": { "id": "4c3acc29" @@ -630,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "16653654", "metadata": { "id": "16653654" @@ -737,7 +737,7 @@ "outputId": "131bd7bd-0a45-4e58-d305-2006484a365c" }, "id": "67jlM9d3nXqL", - "execution_count": 7, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -806,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "5abf5cc1", "metadata": { "id": "5abf5cc1", @@ -941,7 +941,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "a7a20757", "metadata": { "id": "a7a20757", @@ -1090,7 +1090,7 @@ "outputId": "47988497-e7f9-4949-e29f-5a7324269636" }, "id": "FmuhARQVywHi", - "execution_count": 10, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1191,7 +1191,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "94af85f8", "metadata": { "id": "94af85f8", From a80e97b0e023e4c487d57752fee2353f84cb2046 Mon Sep 17 00:00:00 2001 From: VincentSchaik <193274586+VincentSchaik@users.noreply.github.com> Date: Sun, 2 Nov 2025 15:26:32 -0500 Subject: [PATCH 06/16] Deep Learning Module - Completed. --- 01_materials/labs/lab_1.ipynb | 861 +++---- 01_materials/labs/lab_2.ipynb | 467 ++-- 01_materials/labs/lab_3.ipynb | 957 ++++---- 01_materials/labs/lab_4.ipynb | 1889 ++++++++------- 01_materials/labs/lab_5.ipynb | 244 +- 01_materials/labs/lab_6.ipynb | 113 +- 02_activities/assignments/assignment_1.ipynb | 148 +- 02_activities/assignments/assignment_2.ipynb | 2166 +++++++++--------- 8 files changed, 3520 insertions(+), 3325 deletions(-) diff --git a/01_materials/labs/lab_1.ipynb b/01_materials/labs/lab_1.ipynb index 7258be26a..c137719cf 100644 --- a/01_materials/labs/lab_1.ipynb +++ b/01_materials/labs/lab_1.ipynb @@ -61,14 +61,14 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "(1797, 8, 8)" ] }, + "execution_count": 93, "metadata": {}, - "execution_count": 93 + "output_type": "execute_result" } ], "source": [ @@ -99,14 +99,14 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "(1797, 64)" ] }, + "execution_count": 94, "metadata": {}, - "execution_count": 94 + "output_type": "execute_result" } ], "source": [ @@ -139,14 +139,14 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "(1797,)" ] }, + "execution_count": 95, "metadata": {}, - "execution_count": 95 + "output_type": "execute_result" } ], "source": [ @@ -178,14 +178,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -229,14 +229,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAH4CAYAAACbup4ZAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAJR1JREFUeJzt3X9MlXX/x/HXUQjQELK0piLIbX/Ubu4sTR3TovQe647q2A9duyspZ81aE7fK7A/hj1a5oqjV7NcSWzWmzrB5t5YtaN2bg9TgvmmxnEpCNAfJEfohRny+f9yDbw7FI58LzvvA87H5h5fXeV0fDm/O61yc47lCzjknAAAQU+NivQAAAEAhAwBgAoUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhS2pqalIoFNILL7wQWGZ1dbVCoZCqq6sDy8TYwDzCEuZx5MRtIZeXlysUCmnfvn2xXsqwqaio0DXXXKPk5GRNmTJFq1atUnt7e6yXhTNgHmEJ8xif4raQR7vNmzfr7rvv1uTJk/Xiiy9q9erVqqio0JIlS3Ty5MlYLw9jDPMIS0brPCbEegEY6NSpU3rqqad03XXXac+ePQqFQpKk3Nxc3XLLLXrrrbf06KOPxniVGCuYR1gymudxVJ8hnzp1Shs3btTcuXOVlpamiRMnavHixaqqqjrrbV566SVlZmYqJSVF119/vRoaGgbs09jYqDvvvFOTJ09WcnKy5s2bp48++uic6/n111/V2Nh4zl+rNDQ0KBKJaMWKFf3DJkkFBQW68MILVVFRcc5jwR7mEZYwj/aM6kLu7OzU22+/rby8PG3atEklJSVqa2tTfn6+6urqBuz/7rvv6pVXXtEjjzyiDRs2qKGhQTfeeKOOHTvWv88333yjhQsX6ttvv9WTTz6p0tJSTZw4UeFwWB9++OGg66mtrdUVV1yhV199ddD9uru7JUkpKSkD/i0lJUVff/21ent7o7gHYAnzCEuYR4NcnNqyZYuT5L766quz7tPT0+O6u7tP29bR0eEuvfRS98ADD/RvO3LkiJPkUlJSXEtLS//2mpoaJ8mtW7euf9uSJUtcTk6OO3nyZP+23t5el5ub6y6//PL+bVVVVU6Sq6qqGrCtuLh40K+tra3NhUIht2rVqtO2NzY2OklOkmtvbx80AyOLeWQeLWEe43MeR/UZ8vjx43XBBRdIknp7e3X8+HH19PRo3rx5OnDgwID9w+Gwpk+f3v/3+fPna8GCBfr4448lScePH9fnn3+u5cuXq6urS+3t7Wpvb9dPP/2k/Px8HTx4UD/88MNZ15OXlyfnnEpKSgZd9yWXXKLly5dr69atKi0t1eHDh/Xll19qxYoVSkxMlCT99ttv53t3IMaYR1jCPBoU4ycEQxbNM0DnnCsvL3c5OTkuMTGx/9mTJDdr1qz+ffqeAW7cuHHA7e+9916XlJTknPv/Z4SD/Tlw4IBz7szPAM9HJBJxt95662nZ99xzj7v99tudJNfR0TGkXAwP5rFjSLkYHsxjx5ByY21Uv8v6vffeU2FhocLhsB5//HFNnTpV48eP17PPPqtDhw6dd17f6xKPPfaY8vPzz7jP7NmzvdbcJy0tTbt27dLRo0fV1NSkzMxMZWZmKjc3V1OmTFF6enogx8HIYR5hCfNoz6gu5B07dig7O1s7d+487d14xcXFZ9z/4MGDA7Z99913ysrKkiRlZ2dLkhITE7V06dLgF3wGM2fO1MyZMyVJkUhE+/fv1x133DEix0awmEdYwjzaM+pfQ5Yk51z/tpqaGu3du/eM+1dWVp72Gkdtba1qamp00003SZKmTp2qvLw8vfHGG/rxxx8H3L6trW3Q9UT7tv6z2bBhg3p6erRu3boh3R6xxTzCEubRnrg/Q37nnXf0ySefDNi+du1aFRQUaOfOnVq2bJluvvlmHTlyRK+//rquvPJK/fzzzwNuM3v2bC1atEhr1qxRd3e3ysrKdPHFF+uJJ57o3+e1117TokWLlJOTo9WrVys7O1vHjh3T3r171dLSovr6+rOutba2VjfccIOKi4vP+caF5557Tg0NDVqwYIESEhJUWVmpTz/9VE8//bSuvfba6O8gjCjmEZYwj3Emxq9hD1nfmxbO9qe5udn19va6Z555xmVmZrqkpCR39dVXu927d7uVK1e6zMzM/qy+Ny08//zzrrS01GVkZLikpCS3ePFiV19fP+DYhw4dcvfdd5+77LLLXGJiops+fborKChwO3bs6N/H5239zjm3e/duN3/+fJeamuomTJjgFi5c6LZt2+Zzl2EYMY+whHmMTyHn/vT7CgAAEBOj+jVkAADiBYUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABUX0wSG9vr1pbW5WamnraR6wB0v8+6aerq0vTpk3TuHHD/xyPecRgmEdYcj7zGFUht7a2KiMjI5DFYfRqbm7WjBkzhv04zCOiwTzCkmjmMapCTk1N7Q+cNGmS/8o8rF+/3jvj9ddf9874xz/+4Z2xefNm7wwLVzXp7OxURkZG/5wMN0vzeOTIEe+MoqIiE+t4+eWXvTNuuOEG7wxf8TqPHR0d3mspLy/3znjppZe8M4KYg61bt3pnWHA+8xhVIff9GmbSpEkxfwBMSkqK6fH79F0I20cQ92Wsvx9/NlK/rrM0j0E86Cck+H+kfBC/mp04caJ3Rqy/H38Wb/P4xx9/eK8lOTnZOyOI+83K46Ml0dyvvKkLAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAzwv+7beaisrPTOCOKarStXrvTOCOJanSUlJd4ZZWVl3hljVRDXn126dKl3xkMPPeSdMXfuXO+Mv//9794ZzjnvjLEqiDk4dOiQd8abb77pnfHZZ595Z2zatMk7Y/369d4ZI4kzZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMSRvJgkUjEOyMzM9M7o6yszDsjPT3dO6O8vNw7I4ivZazavn27d8Zf/vIX74wgLqIexNcya9Ys7wzE1kMPPeSdcdddd3lndHR0eGcE8bUE8bM1kjhDBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMCBhJA8WDoe9M4qKirwzKisrvTOampq8M06cOGFiHVlZWd4Z8Wj//v3eGUuXLvXOOHz4sHfGgw8+6J2B2Lrrrru8M958803vjO3bt3tn7Nu3zzsjCB0dHd4ZF110UQAriQ5nyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAYkjOTB0tPTvTNKSkq8M+6//37vjLS0NO+MIFRWVnpnFBUVeWfEowcffNA7Y+nSpd4ZTz75pHfG+vXrvTM2bdrknRFvF4S35K677vLOmDt3rnfG9u3bvTO2bdvmnfHQQw95Z3z22WfeGUF8X6LFGTIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABCbFewPkqKiryzsjLy/POyMrK8s4oLCz0zmhqavLOGKuCuJh7R0dHACuxYf/+/d4Z8XZB+NEmOzvbO2P9+vUBrMTfRRddFOsljDjOkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAxIGMmD1dXVeWeEw2ETGUEI4v6IRCLeGRi6v//9794ZS5cu9c4I4qLyQVwQ/vDhw94ZGLrt27d7Z2RnZ3tnzJ071ztjz5493hlB/GyNJM6QAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADEgYyYPNmTPHOyMrK8s74+WXX/bOCMJVV13lnVFWVua/EAzZ+vXrvTPefPNN74xQKOSdEYQg7g8M3UUXXeSdsXTp0gBW4i87O9s7Y+7cuQGsZORwhgwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYENX1kJ1zkqTOzs5hXUw0enp6Yr2EwPzxxx/eGT///LN3hu/3te/2fXMy3CzN4y+//OKd8fvvvwewEhuYx9gKYh5H6n47lyAeH7u6urwzRnIeQy6KvVpaWpSRkeG1KIx+zc3NmjFjxrAfh3lENJhHWBLNPEZVyL29vWptbVVqaqpCoVBgC8To4JxTV1eXpk2bpnHjhv9VEOYRg2EeYcn5zGNUhQwAAIYXb+oCAMAAChkAAAMoZElNTU0KhUJ64YUXAsusrq5WKBRSdXV1YJkYG5hHWMI8jpy4LeTy8nKFQiHt27cv1ksZNhUVFbrmmmuUnJysKVOmaNWqVWpvb4/1snAGzCMsYR7jU9wW8mi3efNm3X333Zo8ebJefPFFrV69WhUVFVqyZIlOnjwZ6+VhjGEeYclonceoPhgEI+vUqVN66qmndN1112nPnj39/5UiNzdXt9xyi9566y09+uijMV4lxgrmEZaM5nkc1WfIp06d0saNGzV37lylpaVp4sSJWrx4saqqqs56m5deekmZmZlKSUnR9ddfr4aGhgH7NDY26s4779TkyZOVnJysefPm6aOPPjrnen799Vc1Njae89cqDQ0NikQiWrFixWn/r7GgoEAXXnihKioqznks2MM8whLm0Z5RXcidnZ16++23lZeXp02bNqmkpERtbW3Kz89XXV3dgP3fffddvfLKK3rkkUe0YcMGNTQ06MYbb9SxY8f69/nmm2+0cOFCffvtt3ryySdVWlqqiRMnKhwO68MPPxx0PbW1tbriiiv06quvDrpfd3e3JCklJWXAv6WkpOjrr79Wb29vFPcALGEeYQnzaJCLU1u2bHGS3FdffXXWfXp6elx3d/dp2zo6Otyll17qHnjggf5tR44ccZJcSkqKa2lp6d9eU1PjJLl169b1b1uyZInLyclxJ0+e7N/W29vrcnNz3eWXX96/raqqyklyVVVVA7YVFxcP+rW1tbW5UCjkVq1addr2xsZGJ8lJcu3t7YNmYGQxj8yjJcxjfM7jqD5DHj9+vC644AJJ//t4u+PHj6unp0fz5s3TgQMHBuwfDoc1ffr0/r/Pnz9fCxYs0McffyxJOn78uD7//HMtX75cXV1dam9vV3t7u3766Sfl5+fr4MGD+uGHH866nry8PDnnVFJSMui6L7nkEi1fvlxbt25VaWmpDh8+rC+//FIrVqxQYmKiJOm3334737sDMcY8whLm0aAYPyEYsmieATrnXHl5ucvJyXGJiYn9z54kuVmzZvXv0/cMcOPGjQNuf++997qkpCTn3P8/Ixzsz4EDB5xzZ34GeD4ikYi79dZbT8u+55573O233+4kuY6OjiHlYngwjx1DysXwYB47hpQba6P6XdbvvfeeCgsLFQ6H9fjjj2vq1KkaP368nn32WR06dOi88/pel3jssceUn59/xn1mz57tteY+aWlp2rVrl44ePaqmpiZlZmYqMzNTubm5mjJlitLT0wM5DkYO8whLmEd7RnUh79ixQ9nZ2dq5c+dp78YrLi4+4/4HDx4csO27775TVlaWJCk7O1uSlJiYqKVLlwa/4DOYOXOmZs6cKUmKRCLav3+/7rjjjhE5NoLFPMIS5tGeUf8asnT6haFramq0d+/eM+5fWVl52msctbW1qqmp0U033SRJmjp1qvLy8vTGG2/oxx9/HHD7tra2QdcT7dv6z2bDhg3q6enRunXrhnR7xBbzCEuYR3vi/gz5nXfe0SeffDJg+9q1a1VQUKCdO3dq2bJluvnmm3XkyBG9/vrruvLKK/Xzzz8PuM3s2bO1aNEirVmzRt3d3SorK9PFF1+sJ554on+f1157TYsWLVJOTo5Wr16t7OxsHTt2THv37lVLS4vq6+vPutba2lrdcMMNKi4uPucbF5577jk1NDRowYIFSkhIUGVlpT799FM9/fTTuvbaa6O/gzCimEdYwjzGmRi/hj1kfW9aONuf5uZm19vb65555hmXmZnpkpKS3NVXX+12797tVq5c6TIzM/uz+t608Pzzz7vS0lKXkZHhkpKS3OLFi119ff2AYx86dMjdd9997rLLLnOJiYlu+vTprqCgwO3YsaN/H5+39Tvn3O7du938+fNdamqqmzBhglu4cKHbtm2bz12GYcQ8whLmMT6FnPvT7ysAAEBMjOrXkAEAiBcUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABkT1wSC9vb1qbW1VamrqaR+xBkj/+6Sfrq4uTZs2TePGDf9zPOYRg2EeYcn5zGNUhdza2qqMjIxAFofRq7m5WTNmzBj24zCPiAbzCEuimceoCjk1NbU/cNKkSf4r8/Cf//zHO2PNmjUBrMTf5s2bvTP+9re/BbASP52dncrIyOifk+FmaR7ff/99ExknTpzwznj44Ye9M/75z396Z/iK13mMRCLea/nggw+8M3bv3u2dEcQ8jsXHx6gKue/XMJMmTYr5A+CFF17ondH3oeqxFsTXEuvvx5+N1K/rLM1jSkqKd0ZCgv9Hygcx00F8LbH+fvxZvM1j3+ULfSQnJ3tnWJnHsfj4yJu6AAAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAA/+tsjbCioiLvjMLCQu+MvLw8E+uoq6vzzhirysvLvTPuv/9+74y1a9d6ZwQxB0F8LUH8XGRlZXlnxKMg5jGIjCAeY4PICIfD3hlNTU3eGSOJM2QAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADEmK9gPP1xRdfeGdUV1d7Z1RWVnpnpKene2dg6CKRSKyXEJi6ujrvjKuuuso7IysryztjrAri8WDOnDneGYWFhd4ZZWVl3hn19fXeGfGGM2QAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADEmK9gPO1cuVK74wgLuIdxMXtv//+e++MpqYm74yxelH5oqIi74zq6mrvjPLycu+MEydOeGcEcVF5DF04HPbOCOJ7GMTjYxCPS0EI4nE6PT3dOyNanCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYkBDrBZyvIC7AHcQF4YO4aHUQX0sQFwLPysryzhirKisrvTPy8vK8M4L4HgaxDgxdEI8pdXV13hnV1dXeGXPmzPHOCIfD3hlBPMaWlJR4Z0SLM2QAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADKGQAAAygkAEAMIBCBgDAAAoZAAADEkbyYJFIxDsjiAtwFxUVeWcEIYgLkgdxAW4uTD90QczjF1984Z0xkhdRx+jG40HscIYMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgQMJIHiwSiXhnFBYWemcEcTH3IC7iXV1d7Z2B2MrKyor1EiTZWQdiK4jHpSAep4N4jG1qavLOiDecIQMAYACFDACAARQyAAAGUMgAABhAIQMAYACFDACAARQyAAAGUMgAABhAIQMAYACFDACAARQyAAAGUMgAABhAIQMAYACFDACAARQyAAAGUMgAABiQMJIHC+Ii6uXl5SYy7r//fu+MIFRVVcV6CWNaZWWld8Ztt93mnRHEzxbiX1FRkXdGWVmZd8ayZcu8M4L4uQji/hhJnCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABkR1PWTnnCSps7NzWBcTjV9++cU749SpUwGsxIYg7g/f72vf7fvmZLhZmsfffvvNO+P333/3zrBwX1gxlufx119/9c7o6ekJYCX+rPxcjBvnd956PvMYclHs1dLSooyMDK9FYfRrbm7WjBkzhv04zCOiwTzCkmjmMapC7u3tVWtrq1JTUxUKhQJbIEYH55y6uro0bdo072eT0WAeMRjmEZaczzxGVcgAAGB48aYuAAAMoJABADCAQpbU1NSkUCikF154IbDM6upqhUIhVVdXB5aJsYF5hCXM48iJ20IuLy9XKBTSvn37Yr2UYVNRUaFrrrlGycnJmjJlilatWqX29vZYLwtnwDzCEuYxPsVtIY92mzdv1t13363JkyfrxRdf1OrVq1VRUaElS5bo5MmTsV4exhjmEZaM1nmM6oNBMLJOnTqlp556Stddd5327NnT/18pcnNzdcstt+itt97So48+GuNVYqxgHmHJaJ7HUX2GfOrUKW3cuFFz585VWlqaJk6cqMWLF6uqquqst3nppZeUmZmplJQUXX/99WpoaBiwT2Njo+68805NnjxZycnJmjdvnj766KNzrufXX39VY2PjOX+t0tDQoEgkohUrVpz2/xoLCgp04YUXqqKi4pzHgj3MIyxhHu0Z1YXc2dmpt99+W3l5edq0aZNKSkrU1tam/Px81dXVDdj/3Xff1SuvvKJHHnlEGzZsUENDg2688UYdO3asf59vvvlGCxcu1Lfffqsnn3xSpaWlmjhxosLhsD788MNB11NbW6srrrhCr7766qD7dXd3S5JSUlIG/FtKSoq+/vpr9fb2RnEPwBLmEZYwjwa5OLVlyxYnyX311Vdn3aenp8d1d3eftq2jo8Ndeuml7oEHHujfduTIESfJpaSkuJaWlv7tNTU1TpJbt25d/7YlS5a4nJwcd/Lkyf5tvb29Ljc3111++eX926qqqpwkV1VVNWBbcXHxoF9bW1ubC4VCbtWqVadtb2xsdJKcJNfe3j5oBkYW88g8WsI8xuc8juoz5PHjx+uCCy6Q9L+Ptzt+/Lh6eno0b948HThwYMD+4XBY06dP7//7/PnztWDBAn388ceSpOPHj+vzzz/X8uXL1dXVpfb2drW3t+unn35Sfn6+Dh48qB9++OGs68nLy5NzTiUlJYOu+5JLLtHy5cu1detWlZaW6vDhw/ryyy+1YsUKJSYmSgrmogYYWcwjLGEeDYrxE4Ihi+YZoHPOlZeXu5ycHJeYmNj/7EmSmzVrVv8+fc8AN27cOOD29957r0tKSnLO/f8zwsH+HDhwwDl35meA5yMSibhbb731tOx77rnH3X777U6S6+joGFIuhgfz2DGkXAwP5rFjSLmxNqrfZf3ee++psLBQ4XBYjz/+uKZOnarx48fr2Wef1aFDh847r+91iccee0z5+fln3Gf27Nlea+6TlpamXbt26ejRo2pqalJmZqYyMzOVm5urKVOmKD09PZDjYOQwj7CEebRnVBfyjh07lJ2drZ07d572brzi4uIz7n/w4MEB27777jtlZWVJkrKzsyVJiYmJWrp0afALPoOZM2dq5syZkqRIJKL9+/frjjvuGJFjI1jMIyxhHu0Z9a8hS6dfGLqmpkZ79+494/6VlZWnvcZRW1urmpoa3XTTTZKkqVOnKi8vT2+88YZ+/PHHAbdva2sbdD3Rvq3/bDZs2KCenh6tW7duSLdHbDGPsIR5tCfuz5DfeecdffLJJwO2r127VgUFBdq5c6eWLVumm2++WUeOHNHrr7+uK6+8Uj///POA28yePVuLFi3SmjVr1N3drbKyMl188cV64okn+vd57bXXtGjRIuXk5Gj16tXKzs7WsWPHtHfvXrW0tKi+vv6sa62trdUNN9yg4uLic75x4bnnnlNDQ4MWLFighIQEVVZW6tNPP9XTTz+ta6+9Nvo7CCOKeYQlzGOcifFr2EPW96aFs/1pbm52vb297plnnnGZmZkuKSnJXX311W737t1u5cqVLjMzsz+r700Lzz//vCstLXUZGRkuKSnJLV682NXX1w849qFDh9x9993nLrvsMpeYmOimT5/uCgoK3I4dO/r38Xlbv3PO7d69282fP9+lpqa6CRMmuIULF7pt27b53GUYRswjLGEe41PIuT/9vgIAAMTEqH4NGQCAeEEhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgQFQfDNLb26vW1lalpqae9hFrgPS/T/rp6urStGnTNG7c8D/HYx4xGOYRlpzPPEZVyK2trcrIyAhkcRi9mpubNWPGjGE/DvOIaDCPsCSaeYyqkFNTU/sDJ02a5L+yGItEIt4Zzz77rHfGv/71L++Mhx9+OOYZnZ2dysjI6J+T4cY8DvTBBx94Z+zevds74+jRo94Z//73v71u39XVpb/+9a/MYwwFMUvvv/++d8Z///tf74yRnMeoCrnv1zCTJk0aFQPXd5kwH0lJSd4ZQfw6LTk52TsjqO/pSP26jnkcKIg5SEjw/2j7IGaaeYx/EyZM8M5ITEz0zoi3eeRNXQAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBgAIUMAIABFDIAAAZQyAAAGEAhAwBggP/11kZYZWWld0ZRUZF3Rjgc9s4oLCz0zqirq/POwNAFcS3jvLw874wg5rGkpMQ7I4ifrfT0dK/bB3HJvbHMyuNSEDNdXV3tneH7M97V1RX1vkwuAAAGUMgAABhAIQMAYACFDACAARQyAAAGUMgAABhAIQMAYACFDACAARQyAAAGUMgAABhAIQMAYACFDACAARQyAAAGUMgAABhAIQMAYACFDACAAQkjebCmpibvjCAugP799997ZwTxtQSRUV5e7p2BoQtiHuvr670z8vLyvDOCuCB8EOvA0AXxPczKyvLOKCsr886orKz0zpgzZ453hu/90dnZGfW+nCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYkDCSB4tEIt4Z33//vf9CArBr165YL0GS1NTU5J0RxEW841EQ87h161bvjLS0NO+M8vJy74wTJ054Z5SUlHhnjFWVlZXeGVYel15++eVYL0GStGXLllgv4bxwhgwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGAAhQwAgAEUMgAABlDIAAAYQCEDAGBAwkgebM6cOd4ZQVxwOogL0xcWFprIaGpq8s4Yq9LT070zbrvtNu+MrKws74wgZikcDntnBPEzPlYFcf+npaV5Z5w4ccI746qrrvLOqK+v986It3nkDBkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMCAhFgv4HwFcSH2IEQiEe+MpqYm74y8vDzvDAxdZWVlrJcgKZh1hMNh7wzEVl1dnXdGEI9tWVlZJjLmzJnjnTGSOEMGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwICHWC4hXVi4IH28X4MbwaGpq8s5IT0/3zkBsZWVlxXoJkoJ5fCwvL/fOiDecIQMAYACFDACAARQyAAAGUMgAABhAIQMAYACFDACAARQyAAAGUMgAABhAIQMAYACFDACAARQyAAAGUMgAABhAIQMAYACFDACAARQyAAAGUMgAABiQEOsFxKvq6mrvjLF4AW4Mj0gk4p1h5eL2iH9BzGNhYaF3RrzhDBkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwIKrrITvnJEmdnZ3Duph4curUKe+M0XJ/9n0dfXMy3JjHgbq7u70zfvvtN+8MC98T5jH2RsssBeF85jHkotirpaVFGRkZ/ivDqNbc3KwZM2YM+3GYR0SDeYQl0cxjVIXc29ur1tZWpaamKhQKBbZAjA7OOXV1dWnatGkaN274XwVhHjEY5hGWnM88RlXIAABgePGmLgAADKCQAQAwgEIGAMAAChkAAAMoZAAADKCQAQAwgEIGAMCA/wPS33TtkxOzIQAAAABJRU5ErkJggg==\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -329,8 +329,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "X_train shape: (1437, 64)\n", "y_train shape: (1437,)\n", @@ -386,8 +386,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Before one-hot encoding: 6\n", "After one-hot encoding: [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n" @@ -446,32 +446,20 @@ }, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1mModel: \"sequential_23\"\u001b[0m\n" - ], "text/html": [ "
Model: \"sequential_23\"\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_23\"\u001b[0m\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ dense_73 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_74 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_75 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", - "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" - ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
               "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
@@ -483,48 +471,60 @@
               "│ dense_75 (Dense)                │ (None, 10)             │           650 │\n",
               "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
               "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ dense_73 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_74 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_75 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" - ], "text/html": [ "
 Total params: 8,970 (35.04 KB)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" - ], "text/html": [ "
 Trainable params: 8,970 (35.04 KB)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ], "text/html": [ "
 Non-trainable params: 0 (0.00 B)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -611,8 +611,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/5\n", "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 18ms/step - accuracy: 0.3510 - loss: 3.4412 - val_accuracy: 0.8333 - val_loss: 0.5895\n", @@ -627,14 +627,14 @@ ] }, { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 103, "metadata": {}, - "execution_count": 103 + "output_type": "execute_result" } ], "source": [ @@ -671,20 +671,7 @@ }, { "cell_type": "code", - "source": [ - "import numpy as np\n", - "(X_train.shape[0] * 0.8) / 32\n", - "\n", - "NUM_TRAINING_SAMPLES = int(X_train.shape[0] * 0.8)\n", - "NUM_TRAINING_SAMPLES\n", - "\n", - "print(f\"Number of training samples: {NUM_TRAINING_SAMPLES}\")\n", - "print(f\"Number of validation samples: {X_train.shape[0] - NUM_TRAINING_SAMPLES}\")\n", - "# Batch sizes\n", - "print(f\"Batch size: {32}\")\n", - "# Number of steps\n", - "print(f\"Number of steps: {NUM_TRAINING_SAMPLES // 32}\")\n" - ], + "execution_count": 104, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -693,11 +680,10 @@ "id": "U_gNjnnGMOCk", "outputId": "df89e09c-c460-43c0-be92-6a95c08deb58" }, - "execution_count": 104, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Number of training samples: 1149\n", "Number of validation samples: 288\n", @@ -705,6 +691,20 @@ "Number of steps: 35\n" ] } + ], + "source": [ + "import numpy as np\n", + "(X_train.shape[0] * 0.8) / 32\n", + "\n", + "NUM_TRAINING_SAMPLES = int(X_train.shape[0] * 0.8)\n", + "NUM_TRAINING_SAMPLES\n", + "\n", + "print(f\"Number of training samples: {NUM_TRAINING_SAMPLES}\")\n", + "print(f\"Number of validation samples: {X_train.shape[0] - NUM_TRAINING_SAMPLES}\")\n", + "# Batch sizes\n", + "print(f\"Batch size: {32}\")\n", + "# Number of steps\n", + "print(f\"Number of steps: {NUM_TRAINING_SAMPLES // 32}\")\n" ] }, { @@ -732,8 +732,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9636 - loss: 0.1401 \n", "Loss: 0.14\n", @@ -773,21 +773,21 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n" ] }, { - "output_type": "display_data", "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAH4CAYAAACbup4ZAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAQShJREFUeJzt3Xt8FPW9//F3IBdCCAQIIBxCotxUBFGUIyokgIJEjWDVqpwjoVhSkSOoPyMoSiJ44SFKLEisqKFVkNNai4ACLRYsIucUpXipIhcJV5VUTbgJkeTz+4NH9rAkwJKZJN+F1/Px2D+YzH7mO7OfnTc7OzsTYWYmAABQp+rV9QAAAACBDACAEwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHDAaRHIKSkpyszMrOthOIPtUbfY/sHS0tKUlpZW18M4Y9GPwVzeHp4Defbs2YqIiAg8GjRooE6dOmn06NH69ttv/Rhjjdu0aZNuuukmNW3aVA0bNtSVV16p5cuXV7teYWFh0DapV6+emjVrpkGDBmn16tU+jtx/x4796Me8efPqengnFe79uH79emVnZ6t79+6Kj49X69atde211+rDDz/0VPfY17Jx48ZKTU3V22+/7dPIa05+fr5uvvlmtWvXThEREc7uTKsS7v0osX88Wk3vHyN9GKMk6bHHHtPZZ5+tgwcP6v3331d+fr7eeecdffbZZ2rYsKFfi/Hd9u3b1atXL9WvX18PPPCA4uLiVFBQoAEDBujdd99Vnz59ql37tttuU3p6usrKyrRhwwbNnDlTffv21Zo1a9S1a1cf18J/FWM/Wq9evepoNKcuXPvxpZde0ssvv6yf/exnGjVqlEpKSvSb3/xGl112mZYsWaKrrrqq2rWvvvpq3XHHHTIzbd26Vfn5+br++uu1ePFiDRw40Me18NeUKVO0d+9e9ezZU19//XVdD6dawrUf2T9Wrcb2j+ZRQUGBSbI1a9YETb/vvvtMks2dO/e4z923b5/XxZuZWXJysg0bNqxazx01apRFRkba+vXrA9P2799vSUlJdvHFF1er5pYtW0ySPf3000HTFy9ebJLsrrvuqlbdUHnZHscbe7gI93788MMPbe/evUHT/vWvf1mLFi3siiuuqPaYJNndd98dNO3zzz83STZo0KBq1w1FamqqpaamVvv5hYWFVl5ebmZmcXFx1d62dSHc+5H9Y7Ca3j/W2HfI/fr1kyRt2bJFkpSZmalGjRpp8+bNSk9PV3x8vIYOHSpJKi8vV15enrp06aIGDRqoVatWysrK0g8//HDsfx40efJktW3bVg0bNlTfvn31z3/+s8rlb968WZs3bz7pOFeuXKmLLrpInTt3Dkxr2LChMjIytHbtWm3cuLFa61+V3r17B8Z2tOLiYo0dO1ZJSUmKiYlRhw4dNGXKFJWXlwfNN3XqVF1++eVq3ry5YmNj1aNHD73xxhshLTvU7XG0/fv3q7S09JSe46pw6ccePXqoUaNGQdOaN2+u3r1764svvjjl9T6R8847T4mJiZXGdejQIU2cOFEdOnRQTEyMkpKSlJ2drUOHDgXNV1BQoH79+qlly5aKiYnR+eefr/z8/JCWvW3bNq1fvz6keZOTkxURERHaSoWJcOlH9o/HVxP7R98OWR+rYuWaN28emHb48GENHDhQV155paZOnRo4VJOVlaXZs2dr+PDhuueee7RlyxbNmDFD//jHP7Rq1SpFRUVJkh599FFNnjxZ6enpSk9P19q1azVgwIAqN0r//v0lHTnmfyKHDh1S06ZNK02vGNtHH32kjh07nvoGqELFWI5e3oEDB5SamqqdO3cqKytL7dq10wcffKDx48fr66+/Vl5eXmDe5557ThkZGRo6dKhKS0s1b9483XzzzVq0aJGuvfbaEy471O1RITc3Vw888IAiIiLUo0cPPf744xowYMApra9LwqUfj+ebb75RYmJitZ57PCUlJfrhhx/Uvn37wLTy8nJlZGTo/fff18iRI3Xeeefp008/1bRp07RhwwbNnz8/MG9+fr66dOmijIwMRUZGauHChRo1apTKy8t19913n3DZd9xxh9577z3ZGXr313DpR/aPVaux/aPXj9gVh2SWLVtmRUVFtn37dps3b541b97cYmNjbceOHWZmNmzYMJNk48aNC3r+ypUrTZLNmTMnaPqSJUuCpu/evduio6Pt2muvDRy+MjN76KGHTFKlQxDJycmWnJx80vFff/31lpCQYHv27Ama3qtXL5NkU6dODXVTBFQc1sjNzbWioiL75ptvbOXKlXbppZeaJPvDH/4QmHfSpEkWFxdnGzZsCKoxbtw4q1+/vm3bti0w7cCBA0HzlJaW2gUXXGD9+vULml7VIZlQt8fWrVttwIABlp+fbwsWLLC8vDxr166d1atXzxYtWhTiFqg74d6PVfnb3/5mERER9sgjj1Tr+WZHDlmPGDHCioqKbPfu3fbhhx/aNddcU+nw26uvvmr16tWzlStXBj3/hRdeMEm2atWqwLRj+9HMbODAgXbOOecETavqkHVqaqpVZ/cTroesw7Uf2T8Gq+n9o2+BfOwjOTnZlixZEpivouG2bt0a9Px77rnHmjRpYrt377aioqKgR6NGjezOO+80M7O5c+eapKCaZkcasaqGC9U777wT+B5t7dq19uWXX9qYMWMsKirKJNmkSZNOuWZFwx37aNSokT3zzDNB83br1s2uueaaSuu+bNkyk2SvvfZalcv4/vvvraioyO666y5LSEgI+puX70iq8t1331mrVq2sc+fOvtWsKeHej8f69ttvrW3btnbOOedU+m75VFS1TaKioiw7O9vKysoC82VkZFiXLl0qrfuGDRtMkk2ePLnK+sXFxVZUVGRPPPGESbLi4uLA37x+h3y0cA3kcO1H9o8n5+f+0bdD1s8//7w6deqkyMhItWrVSp07d1a9esFfUUdGRqpt27ZB0zZu3KiSkhK1bNmyyrq7d++WJG3dulWSKh0eadGiRZWHVEI1aNAgTZ8+XePGjdPFF18sSerQoYMef/xxZWdnV/o+71SMHDlSN998sw4ePKi//vWv+vWvf62ysrKgeTZu3KhPPvlELVq0qLJGxfpL0qJFizR58mStW7cu6Pu8mv5+rVmzZho+fLieeuop7dixo9Jr6KJw7cej7d+/X9ddd5327t2r999/31MvStINN9yg0aNHq7S0VGvWrNETTzyhAwcOBG2XjRs36osvvgipH1etWqWJEydq9erVOnDgQNB8JSUlatKkiafxnk7CtR/ZP56cn/tH3wK5Z8+euuSSS044T0xMTKUmLC8vV8uWLTVnzpwqn3O8F8JPo0eP1vDhw/XJJ58oOjpa3bt318svvyxJ6tSpU7XrduzYMfAzleuuu07169fXuHHj1Ldv38C2Ki8v19VXX63s7Owqa1Qsf+XKlcrIyFCfPn00c+ZMtW7dWlFRUSooKNDcuXOrPcZQJSUlSZK+//77sAjkcO5HSSotLdWNN96oTz75REuXLtUFF1zguWbbtm0D/Zienq7ExESNHj1affv21Y033ijpyPp37dpVzz77bJU1Kvpg8+bN6t+/v84991w9++yzSkpKUnR0tN555x1Nmzat0gk3Z7pw7kf2jyfn1/6xxk7qClX79u21bNkyXXHFFYqNjT3ufMnJyZKO/I/pnHPOCUwvKiqqdLZhdcTFxQX9jmzZsmWKjY3VFVdc4bl2hYcfflizZs3ShAkTtGTJEklH1n/fvn0n/X3pH//4RzVo0EBLly5VTExMYHpBQYFv4zuRr776SlLtBVJdcaEfy8vLdccdd+jdd9/V73//e6WmpnqqdzxZWVmaNm2aJkyYoCFDhigiIkLt27fXxx9/rP79+5/wk8XChQt16NAhLViwQO3atQtM93LBCFTmQj9K7B9Pxq/9Y51fOvOWW25RWVmZJk2aVOlvhw8fVnFxsSTpqquuUlRUlKZPnx50ZubRZ9kdrTqnsVf44IMP9Oabb2rEiBG+HnZLSEhQVlaWli5dqnXr1kk6sv6rV6/W0qVLK81fXFysw4cPS5Lq16+viIiIoEM6hYWFQWe9nkio26OoqKjStJ07d+qVV15Rt27d1Lp165CWF65c6Mf/+q//0n//939r5syZgU+uNSEyMlL333+/vvjiC7311luSjqz/zp07NWvWrErz//jjj9q/f7+kI/0oKWjdS0pKQt4BnsrPns5kLvTjsdg/BvN1/+j1S+jj/fD9WMOGDbO4uLgq/5aVlRU4cWDatGk2Y8YMGzNmjLVp0ybojLvx48ebJEtPT7cZM2bYiBEjrE2bNpaYmFjts+YKCwutZ8+eNnnyZHvppZfs3nvvtdjYWLvooosqnVlYsa4FBQUnrHmiH4/v3LnToqOj7ec//7mZHfmR/cUXX2yRkZF25513Wn5+vk2dOjWwvYqKiszM7N133zVJ1rt3b8vPz7fc3Fxr2bKldevWrdLZql7OIszMzLTevXtbTk6Ovfjii/bQQw9Z8+bNLTo62pYvX37S59e1cO/HadOmmSTr1auXvfrqq5UeR18sYvny5SbJJk6ceNK6quLCIGZHzkxNTEy0yy67zMzMysrKLD093SIiIuzWW2+16dOnW15env3qV7+yZs2aBbbr+vXrLTo62rp27WozZsywp556ytq3b28XXnihSbItW7YEluH1LOsFCxbYpEmTbNKkSRYdHW0XXXRR4N8ff/xxSDXqSrj3I/vHYDW9f3QikM3MXnzxRevRo4fFxsZafHy8de3a1bKzs23Xrl2BecrKyiw3N9dat25tsbGxlpaWZp999pmnDfz999/bDTfcYGeddZZFR0fb2WefbQ8++GClZjMzmz59epVnMh7rZFdzyczMtPr169umTZvMzGzv3r02fvx469Chg0VHR1tiYqJdfvnlNnXqVCstLQ087+WXX7aOHTtaTEyMnXvuuVZQUGATJ070teHmzp1rffr0sRYtWlhkZKQlJibakCFD7KOPPjrpc10Q7v1Ycbbt8R5HB93ChQtNkr3wwgsnrXu8QDYzy8nJMUmBHUppaalNmTLFunTpYjExMda0aVPr0aOH5ebmWklJSeB5CxYssG7dulmDBg0sJSXFpkyZYq+88orvgXyibXKynX9dC/d+ZP8YrKb3jxFmZ+gv86vhlltuUWFhof7+97/X9VAAZWdn6/XXX9emTZuCvjcD6gL7R+/q/KSucGFmWrFihV577bW6Hgog6cgJVI888ghhjDrH/tEffEIGAMABdX6WNQAAIJABAHACgQwAgAMIZAAAHBDSWdbl5eXatWuX4uPjT7sbhcM7M9PevXvVpk2bStfirQn0I06EfoRLTqUfQwrkXbt2BS6eDRzP9u3ba+XGE/QjQkE/wiWh9GNIgRwfHx8o2LhxY+8j8+DBBx/0XOPTTz/1XGPbtm2ea7z99tuea1RcVL4u7dmzR0lJSYE+qWku9aMfd2G68sorPdd44YUXPNc4XYRrP37yySeexzJu3DjPNfzgx/4xPz/fc43evXt7ruHVqfRjSIFccRimcePGdb4D9OMiCJGR3q+H4sehMD92GHX9ehyttg7XudSPfvRBdHS05xp1vR1cFG796PV+15I/+zY/+PG+iIuL81zDpfdFKP3ISV0AADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAc4Ma9uk7BunXrPNfIzMz0XCMlJcWJGqhbxcXFnmv40dMIf37sD/zox7y8PM81/JCWllbXQ6h1fEIGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADggsq4HUBcGDx7suUZhYaHnGqhbfvRBSUmJ5xoff/yx5xp+9GNKSornGqi+devWea5RXFzsuYYfEhIS6noIYYlPyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAAB0TW9QBOVffu3T3XaNq0qfeB+CA1NdVzjRUrVngfyBkqJyfHcw0/bsT+29/+1nMNP25un5KS4rkGqq+4uNhzDT/60Y/3RWFhoecaaWlpnmvMnj3bc43axCdkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADIszMTjbTnj171KRJE5WUlKhx48a1Ma7jWrFiRZ0uv4IfN8/2o0ZmZmad16jt/nCpH/2QkpLiucaZeDP346EfTw+DBw92okZt7h/5hAwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcEBkXQ/gVPlxI3ZX5OTkOFHD6w244Y0fPb1ixQrPNQCXjB071nONcHtf8AkZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOCAyLoewKny46bVKSkpnmt0797dc43Zs2d7rpGQkOC5BqrPlZuob9261XONwYMHO1EjMzPTc40zlR/7lLS0NM81/NjH+vG+CDd8QgYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOCCyrgdQF/Ly8jzX8OOG8MnJyZ5r+LEuqL5169Z5ruFHL/nhrbfecqKG12166NAhz2OoC4WFhZ5r+LE/GD58uOcartiyZUtdD+GU8AkZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcENL9kM1MkrRnz54aHUwo/LjXaXl5uQ8j8c6PcRw4cMBzDa+va8XzK/qkprnUj4cPH67rIZx2vL7HS0tLJYVfP+7du9fzWMrKyjzXOJ34sU1rc/8YYSHMtWPHDiUlJXkaFE5/27dvV9u2bWt8OfQjQkE/wiWh9GNIgVxeXq5du3YpPj5eERERvg0Qpwcz0969e9WmTRvVq1fz34LQjzgR+hEuOZV+DCmQAQBAzeKkLgAAHEAgAwDggNMikFNSUpSZmVnXw3AG26Nusf2DpaWlKS0tra6HccaiH4O5vD08B/Ls2bMVEREReDRo0ECdOnXS6NGj9e233/oxxhr39ddfa+TIkTr77LMVGxur9u3b67777tN3331XrXqFhYVB26RevXpq1qyZBg0apNWrV/s8+pqxefNm3X777WrZsqViY2PVsWNHPfzww3U9rJM6Hfrx8ccfV0ZGhlq1aqWIiAjl5OR4rnn0NomIiFDjxo2Vmpqqt99+2/uAa1hJSYmys7PVsWNHxcbGKjk5WSNGjNC2bdvqemgnFe79eOy+7OjHvHnzfKkZbvtHv/PiaCH9DjkUjz32mM4++2wdPHhQ77//vvLz8/XOO+/os88+U8OGDf1ajO/27dunXr16af/+/Ro1apSSkpL08ccfa8aMGVq+fLk++uijap+pedtttyk9PV1lZWXasGGDZs6cqb59+2rNmjXq2rWrz2vin3Xr1iktLU3/9m//pvvvv1/NmzfXtm3btH379roeWsjCtR8lacKECTrrrLN00UUXaenSpb7Vvfrqq3XHHXfIzLR161bl5+fr+uuv1+LFizVw4EDfluOn8vJyXX311fr88881atQoderUSZs2bdLMmTO1dOlSffHFF4qPj6/rYZ5UOPej9H/7sqP16tXLl5rhtH+sybyQJJlHBQUFJsnWrFkTNP2+++4zSTZ37tzjPnffvn1eF29mZsnJyTZs2LBqPXfOnDkmyRYtWhQ0/dFHHzVJtnbt2lOuuWXLFpNkTz/9dND0xYsXmyS76667qjXWUHnZHmVlZXbBBRfYv//7v9uBAwf8HVgtCPd+NDvSP2ZmRUVFJskmTpzoeUyS7O677w6a9vnnn5skGzRokOf6J5KammqpqanVeu6qVatMks2YMSNo+iuvvGKS7M033/RhhDUn3PvxePsyL8J5/1gTeXG0GvsOuV+/fpKkLVu2SJIyMzPVqFEjbd68Wenp6YqPj9fQoUMlHflfcF5enrp06aIGDRqoVatWysrK0g8//HDsfx40efJktW3bVg0bNlTfvn31z3/+s8rlb968WZs3bz7pOCuuotKqVaug6a1bt5YkxcbGnsJan1jv3r0DYztacXGxxo4dq6SkJMXExKhDhw6aMmVKpSt5TZ06VZdffrmaN2+u2NhY9ejRQ2+88UZIyw51e/z5z3/WZ599pokTJyo2NlYHDhw4La7+Ey79KB35jqs2nHfeeUpMTKw0rkOHDmnixInq0KGDYmJilJSUpOzs7EpX0CooKFC/fv3UsmVLxcTE6Pzzz1d+fn5Iy962bZvWr19/0vlq8/1Zm8KpHyvs378/cBW0mhAO+8ea7kffDlkfq2LlmjdvHph2+PBhDRw4UFdeeaWmTp0aOFSTlZWl2bNna/jw4brnnnu0ZcsWzZgxQ//4xz+0atUqRUVFSZIeffRRTZ48Wenp6UpPT9fatWs1YMCAKpukf//+ko58X3Eiffr0Ub169TRmzBg988wzatu2rT755BM9/vjjGjx4sM4991w/NkfQWJo2bRqYduDAAaWmpmrnzp3KyspSu3bt9MEHH2j8+PH6+uuvlZeXF5j3ueeeU0ZGhoYOHarS0lLNmzdPN998sxYtWqRrr732hMsOdXssW7ZMkhQTE6NLLrlEH330kaKjozVkyBDNnDlTzZo1O/UVd0C49GNtKikp0Q8//KD27dsHppWXlysjI0Pvv/++Ro4cqfPOO0+ffvqppk2bpg0bNmj+/PmBefPz89WlSxdlZGQoMjJSCxcu1KhRo1ReXq677777hMu+44479N577530coKXXHKJ4uLi9Mgjj6hZs2bq3LmzNm3apOzsbF166aW66qqrPG2DuhJu/Zibm6sHHnhAERER6tGjhx5//HENGDDAyyaoJBz2jzWeF54+X9v/HZJZtmyZFRUV2fbt223evHnWvHlzi42NtR07dpiZ2bBhw0ySjRs3Luj5K1euNEk2Z86coOlLliwJmr57926Ljo62a6+91srLywPzPfTQQyap0iGI5ORkS05ODmkdXnrpJUtISDBJgcewYcPsp59+OsWtcUTFIZnc3FwrKiqyb775xlauXGmXXnqpSbI//OEPgXknTZpkcXFxtmHDhqAa48aNs/r169u2bdsC0449hFxaWmoXXHCB9evXL2h6VYdkQt0eGRkZJsmaN29uQ4cOtTfeeMMeeeQRi4yMtMsvvzxo27vodOjHCn4fsh4xYoQVFRXZ7t277cMPP7Rrrrmm0qHDV1991erVq2crV64Mev4LL7xgkmzVqlWBaVV9pTFw4EA755xzgqZVdcg6NTXVQt39LFq0yFq3bh30/hw4cKDt3bs3pOfXpXDvx61bt9qAAQMsPz/fFixYYHl5edauXTurV69epcO2oQrn/aOZ/3lxNN8C+dhHcnKyLVmyJDBfRcNt3bo16Pn33HOPNWnSxHbv3m1FRUVBj0aNGtmdd95pZmZz5841SUE1zY40YlUNdyoWL15sAwYMsLy8PPvTn/5k9913n0VGRtr9999frXoVDXfso1GjRvbMM88EzdutWze75pprKq37smXLTJK99tprVS7j+++/t6KiIrvrrrssISEh6G9eviPp16+fSbJrrrkmaPqTTz5pkuwvf/lLterWltOhHyv4HcjHPqKioiw7O9vKysoC82VkZFiXLl0qrfuGDRtMkk2ePLnK+sXFxVZUVGRPPPGESbLi4uLA37x8h2xm9r//+7+Wnp5ujz/+uM2fP99ycnKsYcOGdtNNN1W7Zm05nfqxwnfffWetWrWyzp07V+v54bx/NPM/L47m2yHr559/Xp06dVJkZKRatWqlzp07VzrbLDIystLFtTdu3KiSkhK1bNmyyrq7d++WJG3dulWS1LFjx6C/t2jRIugQx6latWqVrrvuOv3P//yPLrnkEknS4MGD1bhxY+Xm5uoXv/iFzj///GrVHjlypG6++WYdPHhQf/3rX/XrX/+60vexGzdu1CeffKIWLVpUWaNi/SVp0aJFmjx5statWxf0fZ6f18+t+A7ktttuC5p+++23a/z48frggw/C4jBhuPZjTbrhhhs0evRolZaWas2aNXriiSd04MCBoO2yceNGffHFFyH146pVqzRx4kStXr260l3HSkpK1KRJE89j/uqrr9S3b1/97ne/089+9rPAelT8lnTx4sUaNGiQ5+XUtNOpH5s1a6bhw4frqaee0o4dO6p9A49w3D/WZF5IPn6H3LNnz8AAjycmJqZSE5aXl6tly5aaM2dOlc853gvhl9/85jdq1apVpbFnZGQoJydHH3zwQbU3cMeOHQPhdd1116l+/foaN26c+vbtG1hexc86srOzq6zRqVMnSdLKlSuVkZGhPn36aObMmWrdurWioqJUUFCguXPnVmt8VWnTpo2kyictVOwQjj2RxFXh2o81qW3btoF+TE9PV2JiokaPHq2+ffvqxhtvlHRk/bt27apnn322yhoVdzXavHmz+vfvr3PPPVfPPvuskpKSFB0drXfeeUfTpk3z7Rans2fP1sGDB3XdddcFTc/IyJB0ZAcZDoF8uvVjRR98//331Q7kcNw/1mReSDV4Uleo2rdvr2XLlumKK6444RlqycnJko78j+mcc84JTC8qKvIUEt9++22VZxH/9NNPkvy93+3DDz+sWbNmacKECVqyZImkI+u/b9++k37q/OMf/6gGDRpo6dKliomJCUwvKCjwbXyS1KNHD82aNUs7d+4Mmr5r1y5J4R1IoajrfqxNWVlZmjZtmiZMmKAhQ4YoIiJC7du318cff6z+/fuf8JPFwoULdejQIS1YsEDt2rULTF++fLmvY/z2229lZpXeozXx/nSRq/341VdfSfJ3fxAO+8eazos6v3TmLbfcorKyMk2aNKnS3w4fPqzi4mJJ0lVXXaWoqChNnz496MzMo8+yO1qop7F36tRJ3377rVasWBE0/fXXX5ckXXTRRaGtSAgSEhKUlZWlpUuXat26dZKOrP/q1aurvABEcXFx4AWuX7++IiIigpqhsLAw6KzXEwl1e9xwww2KiYlRQUFB0Kecl156SdKRi0uczuq6H2tTZGSk7r//fn3xxRd66623JB1Z/507d2rWrFmV5v/xxx+1f/9+SUf6UQq+6XpJSUnIO8BQf/bUqVMnmZl+//vfB02vifeni+q6H4uKiipN27lzp1555RV169Yt8HMfP4TD/rHG88Lrl9DH++H7sYYNG2ZxcXFV/i0rKytwgYJp06bZjBkzbMyYMdamTZugM+7Gjx9vkiw9Pd1mzJhhI0aMsDZt2lhiYmK1z5pbv369xcXFWaNGjWz8+PH2wgsv2G233WaS7Oqrr65yXQsKCk5Y80Q/pt+5c6dFR0fbz3/+czMz279/v1188cUWGRlpd955p+Xn59vUqVMD26uoqMjMzN59912TZL1797b8/HzLzc21li1bWrdu3Sqdrer1LMLHHnsssP7PP/+8jRw50iIiIuy2224L6fl1Kdz70czsd7/7nU2aNClQv2/fvjZp0iSbNGmSFRYWBuZbvnx5yCd9qYoLg5gdOTM1MTHRLrvsMjM7cmGY9PR0i4iIsFtvvdWmT59ueXl59qtf/cqaNWsW2K7r16+36Oho69q1q82YMcOeeuopa9++vV144YUmKXBxEzNvZ1n/61//srPOOsuio6Ptnnvusd/85jeWlZVl9evXty5dutihQ4dOWqMuhXs/ZmZmWu/evS0nJ8defPFFe+ihh6x58+YWHR1ty5cvr3JdT+f946nkRXU4EchmZi+++KL16NHDYmNjLT4+3rp27WrZ2dm2a9euwDxlZWWWm5trrVu3ttjYWEtLS7PPPvvMcwCtX7/ebrrpJktKSrKoqChLTk62//f//p/t378/aL7p06dXeSbjsU52dZvMzEyrX7++bdq0yczM9u7da+PHj7cOHTpYdHS0JSYm2uWXX25Tp0610tLSwPNefvll69ixo8XExNi5555rBQUFNnHiRN8Duby83KZPn26dOnWyqKgoS0pKsgkTJgSNxVWnQz9WhFVVj6N3ggsXLjRJ9sILL5y05vEC2cwsJycnqHZpaalNmTLFunTpYjExMda0aVPr0aOH5ebmWklJSeB5CxYssG7dulmDBg0sJSXFpkyZEriCll+BbGa2Y8cO+8UvfmFnn322RUdHW+vWre2Xv/xlYGfssnDvx7lz51qfPn2sRYsWFhkZaYmJiTZkyBD76KOPKs17puwfQ82L6ogwO8kv8xFwyy23qLCwUH//+9/reiiAsrOz9frrr2vTpk1B35sBdYH9o3d1flJXuDAzrVixQq+99lpdDwWQdOQEqkceeYQwRp1j/+gPPiEDAOCAOj/LGgAAEMgAADiBQAYAwAEEMgAADgjpLOvy8nLt2rVL8fHxvl6oG6cHM9PevXvVpk2bStfirQn0I06EfoRLTqUfQwrkXbt2BS4mDhzP9u3bq32h+VNBPyIU9CNcEko/hhTI8fHxgYKNGzf2PjIPZs6c6bnGk08+6bnG9u3bPdc4XezZs0dJSUmBPqlpLvXjokWLPNcYN26c5xolJSWea/hxV5zevXt7ruHVmdyPV1xxhecafvTS0Tccqa5PP/3Ucw0XevpU+jGkQK44DNO4ceM6b7gGDRp4ruHHYaW63g4uqq3DdS71Y8OGDT3X8OOwqh/bPi4uznONun49jnYm9mPFTT+88KMfIyO9X3PqdOvpUNaHk7oAAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4wPs9smpZTk5OXQ8BCFi3bp3nGlu3bvU+EB9kZmZ6rlFYWOi5Bqrv448/9lzjT3/6kw8j8W7IkCGeaxQXF3sfSC3iEzIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAGRtbkwP27mXlJS4rlGamqq5xorVqzwXKN79+6eayQkJHiugerz4zV0xeDBg+t6CPDowgsv9FyjsLDQc42cnBzPNZYvX+65RlpamucatYlPyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAAB0TW5sLWrVtXm4s7rvfee89zjb59+/owEu/+9Kc/ea7Bjemrz49t16RJE881SkpKPNfw46byqFspKSmea9x7772ea2zZssVzDT/WJdzwCRkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4IDI2lxYYWFhbS7uuKZNm+a5RmZmpucaftyAe926dZ5rDB482HONM1VxcbHnGiUlJZ5rXHjhhZ5rJCQkeK6B6vNj/+hHjWHDhnmusWLFCs81/NjHhhs+IQMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHBBZmwtz5QboY8eOreshSPLnxvRpaWneB4Jq8+Mm6k2aNPFco7i42HON+fPne64xePBgzzXOVLNnz/ZcIycnx3MNP/YpKSkpnmv48d4KN3xCBgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4ILI2F+bHzcvvvfdezzXy8vI811i3bp3nGqmpqZ5r+HEzcVRfcXGx5xrdu3f3XMOPPvDjhvCFhYWeayQkJHiuEY786AM/9kt+9FJJSYnnGmciPiEDAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABwQWZsLS0lJ8Vxj+fLlnmsMHjzYcw0/1mX27Nmea6BujR071nONIUOGeK7x3nvvea7hh+LiYs81EhISPNcIR2lpaZ5rzJ8/33ONpk2beq4xceJEzzXORHxCBgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAAB4R0P2QzkyTt2bOnRgcTiv3793uuUbE+XpSVlXmusW/fPs81XHhNKsbgx3YNhUv9eODAgboeglP27t3ruYbX1zVc+9GPfi4tLfVcww+HDh3yXMOF97cfTqUfIyyEuXbs2KGkpCTvI8Npbfv27Wrbtm2NL4d+RCjoR7gklH4MKZDLy8u1a9cuxcfHKyIiwrcB4vRgZtq7d6/atGmjevVq/lsQ+hEnQj/CJafSjyEFMgAAqFmc1AUAgAMIZAAAHHBaBHJKSooyMzPrehjOYHvULbZ/MLZH3WL7B0tLS1NaWlpdD6NKngN59uzZioiICDwaNGigTp06afTo0fr222/9GGONKiwsDBr/0Y958+b5UrNevXpq1qyZBg0apNWrV/u8Bv7btGmTbrrpJjVt2lQNGzbUlVdeqeXLl9f1sEIS7v0o+b/9w7kfa+L9WZvCvR937dql//iP/1Dnzp0VHx+vhIQE9ezZU7/97W89/azs2NeycePGSk1N1dtvv+3j6P23fft25ebmqmfPnmratKkSExOVlpamZcuW+VI/pN8hh+Kxxx7T2WefrYMHD+r9999Xfn6+3nnnHX322Wdq2LChX4upMbfddpvS09ODpvXq1cuXmmVlZdqwYYNmzpypvn37as2aNerataun2jVl+/bt6tWrl+rXr68HHnhAcXFxKigo0IABA/Tuu++qT58+dT3EkIRrP9bk9g/HfqxQE+/P2hSu/fivf/1LO3bs0E033aR27drpp59+0l/+8hdlZmbqyy+/1BNPPFHt2ldffbXuuOMOmZm2bt2q/Px8XX/99Vq8eLEGDhzo41r456233tKUKVM0ePBgDRs2TIcPH9bvfvc7XX311XrllVc0fPhwbwswjwoKCkySrVmzJmj6fffdZ5Js7ty5x33uvn37vC7ezMySk5Nt2LBh1Xruli1bTJI9/fTTvozlRDUXL15skuyuu+7ybVlV8bI9Ro0aZZGRkbZ+/frAtP3791tSUpJdfPHFPo2w5oR7P9bE9g/nfqyJ92dtCvd+PJ7rrrvO4uLi7PDhw9V6viS7++67g6Z9/vnnJskGDRrkxxCPKzU11VJTU6v13M8++8yKioqCph08eNDOPfdca9u2reex1dh3yP369ZMkbdmyRZKUmZmpRo0aafPmzUpPT1d8fLyGDh0q6cjv+PLy8tSlSxc1aNBArVq1UlZWln744Ydj//OgyZMnq23btmrYsKH69u2rf/7zn1Uuf/Pmzdq8efMpjXn//v01eqWb3r17S1KlcRUXF2vs2LFKSkpSTEyMOnTooClTpqi8vDxovqlTp+ryyy9X8+bNFRsbqx49euiNN94Iadmhbo+VK1fqoosuUufOnQPTGjZsqIyMDK1du1YbN24MaXmuCZd+rM3tHw79eLSafn/WpnDpx+NJSUnRgQMHfH09zjvvPCUmJlYa16FDhzRx4kR16NBBMTExSkpKUnZ2dqWrgRUUFKhfv35q2bKlYmJidP755ys/Pz+kZW/btk3r168/6XxdunRRYmJi0LSYmBilp6drx44dnq9U59sh62NVbNTmzZsHph0+fFgDBw7UlVdeqalTpwYO1WRlZWn27NkaPny47rnnHm3ZskUzZszQP/7xD61atUpRUVGSpEcffVSTJ09Wenq60tPTtXbtWg0YMKDKpujfv7+kI99BhSI3N1cPPPCAIiIi1KNHDz3++OMaMGCAl01QScVYmjZtGph24MABpaamaufOncrKylK7du30wQcfaPz48fr666+Vl5cXmPe5555TRkaGhg4dqtLSUs2bN08333yzFi1apGuvvfaEyw51exw6dChofBUqXquPPvpIHTt2DGFt3RIu/Vib2z8c+rFCbbw/a1O49GOFH3/8Ufv379e+ffv03nvvqaCgQL169VJsbKyXzRCkpKREP/zwg9q3bx+YVl5eroyMDL3//vsaOXKkzjvvPH366aeaNm2aNmzYoPnz5wfmzc/PV5cuXZSRkaHIyEgtXLhQo0aNUnl5ue6+++4TLvuOO+7Qe++9V+3vxb/55hs1bNjQ+9cPXj9iVxySWbZsmRUVFdn27dtt3rx51rx5c4uNjbUdO3aYmdmwYcNMko0bNy7o+StXrjRJNmfOnKDpS5YsCZq+e/dui46OtmuvvdbKy8sD8z300EMmqdIhmeTkZEtOTj7p+Ldu3WoDBgyw/Px8W7BggeXl5Vm7du2sXr16tmjRompskf87zJabm2tFRUX2zTff2MqVK+3SSy81SfaHP/whMO+kSZMsLi7ONmzYEFRj3LhxVr9+fdu2bVtg2oEDB4LmKS0ttQsuuMD69esXNL2qQ1Shbo/rr7/eEhISbM+ePUHTe/XqZZJs6tSpJ61Rl8K9H2ti+4dzP9bE+7M2hXs/VnjyySdNUuDRv3//oF44VZJsxIgRVlRUZLt377YPP/zQrrnmmkpfT7z66qtWr149W7lyZdDzX3jhBZNkq1atCkw7th/NzAYOHGjnnHNO0LSqDlmnpqZadeNw48aN1qBBA/vP//zPaj3/aL4F8rGP5ORkW7JkSWC+iobbunVr0PPvuecea9Kkie3evduKioqCHo0aNbI777zTzMzmzp1rkoJqmh1pxKoazovvvvvOWrVqZZ07d67W8yt2gMc+GjVqZM8880zQvN26dbNrrrmm0rovW7bMJNlrr71W5TK+//57KyoqsrvuussSEhKC/ublO6N33nkn8D3O2rVr7csvv7QxY8ZYVFSUSbJJkyZVq25tCfd+rIntH879WBWv78/aFO79WKGwsND+8pe/2Ny5c+3222+3/v3725dfflntelVtk6ioKMvOzraysrLAfBkZGdalS5dK675hwwaTZJMnT66yfnFxsRUVFdkTTzxhkqy4uDjwNy/fIR9r//791r17d2vatKnt3LnTcz3fDlk///zz6tSpkyIjI9WqVSt17ty50nU7IyMjK11ce+PGjSopKVHLli2rrLt7925J0tatWyWp0uG6Fi1aVHmIz4tmzZpp+PDheuqpp7Rjx45qX6B+5MiRuvnmm3Xw4EH99a9/1a9//etKd4nauHGjPvnkE7Vo0aLKGhXrL0mLFi3S5MmTtW7duqDvT/y8fu6gQYM0ffp0jRs3ThdffLEkqUOHDnr88ceVnZ2tRo0a+basmhSu/ViT2z8c+7Eqfr0/a1O49mOF5ORkJScnSzpyxvvIkSN11VVX6csvv6z2YesbbrhBo0ePVmlpqdasWaMnnnhCBw4cCNouGzdu1BdffBFSP65atUoTJ07U6tWrK92FraSkRE2aNKnWOI+nrKxMt956qz7//HMtXrxYbdq08VzTt0Du2bOnLrnkkhPOExMTU6kJy8vL1bJlS82ZM6fK5xzvhahpFXdv+f7776v9hu/YsaOuuuoqSdJ1112n+vXra9y4cerbt29gW5WXl+vqq69WdnZ2lTU6deok6cjJPhkZGerTp49mzpyp1q1bKyoqSgUFBZo7d261xnc8o0eP1vDhw/XJJ58oOjpa3bt318svvxw0HteFcz/W1PYP136sih/vz9oUzv1YlZtuukmzZs3S3/72t2r/RKlt27aBfkxPT1diYqJGjx6tvn376sYbb5R0ZP27du2qZ599tsoaFX2wefNm9e/fX+eee66effZZJSUlKTo6Wu+8846mTZtW6YREP/zyl7/UokWLNGfOnMBJel7V2EldoWrfvr2WLVumK6644oT/06r439nGjRt1zjnnBKYXFRVVOtvQD1999ZUkfxv+4Ycf1qxZszRhwgQtWbJE0pH137dvX6Axj+ePf/yjGjRooKVLlyomJiYwvaCgwLfxHS0uLi7od57Lli1TbGysrrjiihpZnitc6cfa2P7h1I/Hqon3p4tc6cdj/fjjj5KOfPL0S1ZWlqZNm6YJEyZoyJAhioiIUPv27fXxxx+rf//+JzzysnDhQh06dEgLFixQu3btAtNr6oJGDzzwgAoKCpSXl6fbbrvNt7p1funMW265RWVlZZo0aVKlvx0+fFjFxcWSpKuuukpRUVGaPn160JlwR5/1ebRQT+svKiqqNG3nzp165ZVX1K1bN7Vu3Tq0FQlBQkKCsrKytHTpUq1bt07SkfVfvXq1li5dWmn+4uJiHT58WJJUv359RUREBB1iLCwsDDrL8ES8/Mzhgw8+0JtvvqkRI0b4ftjHNXXdj1Wpqe0fDv1Ym+9PF9V1P1a1/SXp5ZdfVkREROBrFT9ERkbq/vvv1xdffKG33npL0pH137lzp2bNmlVp/oozv6Uj/SgpaN1LSkpC/g9iqD97kqSnn35aU6dO1UMPPaQxY8aE9JyQef0S+ng/fD/WsGHDLC4ursq/ZWVlBU5kmTZtms2YMcPGjBljbdq0CToDdPz48SbJ0tPTbcaMGTZixAhr06aNJSYmVvsswszMTOvdu7fl5OTYiy++aA899JA1b97coqOjbfny5VWua0FBwQlrnuhiBjt37rTo6Gj7+c9/bmZHTgq4+OKLLTIy0u68807Lz8+3qVOnBrZXxY/Q3333XZNkvXv3tvz8fMvNzbWWLVtat27dKp0d6OWs1sLCQuvZs6dNnjzZXnrpJbv33nstNjbWLrrookpn/roo3PvxVLb/mdCPp/L+dFG49+OYMWPskksusQkTJtiLL75oTz31VODs/P/6r/8Kmnf58uUmySZOnHjSuqriwiBmR86UTkxMtMsuu8zMzMrKyiw9Pd0iIiLs1ltvtenTp1teXp796le/smbNmgW26/r16y06Otq6du1qM2bMsKeeesrat29vF154oUmyLVu2BJbh5SzrN9980yRZx44d7dVXX630+Oabb05a40ScCGQzsxdffNF69OhhsbGxFh8fb127drXs7GzbtWtXYJ6ysjLLzc211q1bW2xsrKWlpdlnn33m6Q0/d+5c69Onj7Vo0cIiIyMtMTHRhgwZYh999FGleadPn17lmYzHOtnVhTIzM61+/fq2adMmMzPbu3evjR8/3jp06GDR0dGWmJhol19+uU2dOtVKS0sDz3v55ZetY8eOFhMTY+eee64VFBTYxIkTfd0Bfv/993bDDTfYWWedZdHR0Xb22Wfbgw8+GBZhbBb+/Xgq2/9M6MdTeX+6KNz78c9//rNdd9111qZNG4uKirL4+Hi74oorrKCgIOjnVWZmCxcuNEn2wgsvnLTu8QLZzCwnJ8ckBf7DVVpaalOmTLEuXbpYTEyMNW3a1Hr06GG5ublWUlISeN6CBQusW7du1qBBA0tJSbEpU6bYK6+84msgV/T38R5e/5MYYebhCuFnmFtuuUWFhYX6+9//XtdDAehHOCU7O1uvv/66Nm3aFHReAUJX5yd1hQsz04oVK/Taa6/V9VAA+hHOWb58uR555BHC2AM+IQMA4IA6P8saAAAQyAAAOIFABgDAAQQyAAAOCOks6/Lycu3atUvx8fE1fuF4hB8z0969e9WmTZtK1+KtCfQjToR+hEtOpR9DCuRdu3YFLuINHM/27dtr5UL/9CNCQT/CJaH0Y0iBHB8fHyjYuHFj7yPz4Hh3PTkVTz75pOca27dv91zDj2356aefeq6RkJDg6fl79uxRUlJSoE9qmkv9+OCDD3qu4cdr6IqhQ4fWeY1w7ceVK1d6Hsvtt9/uuYYfRo0a5bnG+PHjfRhJ3TuVfgwpkCsOwzRu3LjOd4DVvffm0WrjMFYo/Di85cfr4ddrWluH61zqRz8ughAZefpcn8eP9+eZ2o9xcXG+jaWu+fG+qOv3tt9CeW3cSCYAAM5wBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDggFq979vYsWM913juuec810hNTfVcY/DgwZ5r+LEuhYWFnmt0797dc41wVFxc7LnG/PnzPdfIycnxXCMlJcVzDT+4Mo5wtGLFCs81SkpKvA/EB7m5uZ5r+LGPDbd9G5+QAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOiKzNha1bt642F3dcCQkJnmv4cWP6Cy+80HONcLsBt0v86Mfi4mLPNWbPnu25hh99kJOT47mGH++tM9XYsWM91/DjNfSjp1NSUjzXKCws9Fwj3PaPfEIGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADggsjYXNnjwYM81/Lip/FtvveW5RnJysucafqwLwl9aWprnGn70UmZmpuca8+fP91zjTJWQkFDXQ5Dkzzj86Oni4mLPNcINn5ABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA6IrM2FjR071nONwYMHe64xe/ZszzVyc3M91/DjZu5+bI8zlR83US8sLPRcw5Ub06ekpHiusWLFCs81/HhdUH3r1q3zXMOPPsjLy/NcI9zwCRkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4IDIuh7AqfLjJup+1PCDKzemR/WdTq9hWlqa5xp+3Jjej3GcqfzY/oMHD/Zco6SkxHMNP/ogJyfHc43MzEzPNULFJ2QAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMi63oAp6qwsNBzjby8PM81UlNTPdfgRuzhz4+bl/vRjwkJCZ5rpKSkeK6B6ps9e7bnGsOHD/c+EB80adLEc43u3bs7UaM28QkZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOCAyLoewKny42buH3/8secaP/zwg+caCH8JCQmeazRt2tT7QHzgx03l58+f730gZ6ji4mLPNZKTkz3X6N69u+ca9EH18AkZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcENL9kM1MkrRnz54aHUwoDh06VNdDkOTPtqhX7/T4/1DFtqjok5pGP9YMP16//fv3e67h9XUN1348ePCg57GUl5d7rvHTTz95ruHCe9MVp9KPERbCXDt27FBSUpL3keG0tn37drVt27bGl0M/IhT0I1wSSj+GFMjl5eXatWuX4uPjFRER4dsAcXowM+3du1dt2rSplU/99CNOhH6ES06lH0MKZAAAULNOjy8xAQAIcwQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAH/H/UKE5lQfyE9QAAAABJRU5ErkJggg==", "text/plain": [ "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAH4CAYAAACbup4ZAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAQShJREFUeJzt3Xt8FPW9//F3IBdCCAQIIBxCotxUBFGUIyokgIJEjWDVqpwjoVhSkSOoPyMoSiJ44SFKLEisqKFVkNNai4ACLRYsIucUpXipIhcJV5VUTbgJkeTz+4NH9rAkwJKZJN+F1/Px2D+YzH7mO7OfnTc7OzsTYWYmAABQp+rV9QAAAACBDACAEwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHDAaRHIKSkpyszMrOthOIPtUbfY/sHS0tKUlpZW18M4Y9GPwVzeHp4Defbs2YqIiAg8GjRooE6dOmn06NH69ttv/Rhjjdu0aZNuuukmNW3aVA0bNtSVV16p5cuXV7teYWFh0DapV6+emjVrpkGDBmn16tU+jtx/x4796Me8efPqengnFe79uH79emVnZ6t79+6Kj49X69atde211+rDDz/0VPfY17Jx48ZKTU3V22+/7dPIa05+fr5uvvlmtWvXThEREc7uTKsS7v0osX88Wk3vHyN9GKMk6bHHHtPZZ5+tgwcP6v3331d+fr7eeecdffbZZ2rYsKFfi/Hd9u3b1atXL9WvX18PPPCA4uLiVFBQoAEDBujdd99Vnz59ql37tttuU3p6usrKyrRhwwbNnDlTffv21Zo1a9S1a1cf18J/FWM/Wq9evepoNKcuXPvxpZde0ssvv6yf/exnGjVqlEpKSvSb3/xGl112mZYsWaKrrrqq2rWvvvpq3XHHHTIzbd26Vfn5+br++uu1ePFiDRw40Me18NeUKVO0d+9e9ezZU19//XVdD6dawrUf2T9Wrcb2j+ZRQUGBSbI1a9YETb/vvvtMks2dO/e4z923b5/XxZuZWXJysg0bNqxazx01apRFRkba+vXrA9P2799vSUlJdvHFF1er5pYtW0ySPf3000HTFy9ebJLsrrvuqlbdUHnZHscbe7gI93788MMPbe/evUHT/vWvf1mLFi3siiuuqPaYJNndd98dNO3zzz83STZo0KBq1w1FamqqpaamVvv5hYWFVl5ebmZmcXFx1d62dSHc+5H9Y7Ca3j/W2HfI/fr1kyRt2bJFkpSZmalGjRpp8+bNSk9PV3x8vIYOHSpJKi8vV15enrp06aIGDRqoVatWysrK0g8//HDsfx40efJktW3bVg0bNlTfvn31z3/+s8rlb968WZs3bz7pOFeuXKmLLrpInTt3Dkxr2LChMjIytHbtWm3cuLFa61+V3r17B8Z2tOLiYo0dO1ZJSUmKiYlRhw4dNGXKFJWXlwfNN3XqVF1++eVq3ry5YmNj1aNHD73xxhshLTvU7XG0/fv3q7S09JSe46pw6ccePXqoUaNGQdOaN2+u3r1764svvjjl9T6R8847T4mJiZXGdejQIU2cOFEdOnRQTEyMkpKSlJ2drUOHDgXNV1BQoH79+qlly5aKiYnR+eefr/z8/JCWvW3bNq1fvz6keZOTkxURERHaSoWJcOlH9o/HVxP7R98OWR+rYuWaN28emHb48GENHDhQV155paZOnRo4VJOVlaXZs2dr+PDhuueee7RlyxbNmDFD//jHP7Rq1SpFRUVJkh599FFNnjxZ6enpSk9P19q1azVgwIAqN0r//v0lHTnmfyKHDh1S06ZNK02vGNtHH32kjh07nvoGqELFWI5e3oEDB5SamqqdO3cqKytL7dq10wcffKDx48fr66+/Vl5eXmDe5557ThkZGRo6dKhKS0s1b9483XzzzVq0aJGuvfbaEy471O1RITc3Vw888IAiIiLUo0cPPf744xowYMApra9LwqUfj+ebb75RYmJitZ57PCUlJfrhhx/Uvn37wLTy8nJlZGTo/fff18iRI3Xeeefp008/1bRp07RhwwbNnz8/MG9+fr66dOmijIwMRUZGauHChRo1apTKy8t19913n3DZd9xxh9577z3ZGXr313DpR/aPVaux/aPXj9gVh2SWLVtmRUVFtn37dps3b541b97cYmNjbceOHWZmNmzYMJNk48aNC3r+ypUrTZLNmTMnaPqSJUuCpu/evduio6Pt2muvDRy+MjN76KGHTFKlQxDJycmWnJx80vFff/31lpCQYHv27Ama3qtXL5NkU6dODXVTBFQc1sjNzbWioiL75ptvbOXKlXbppZeaJPvDH/4QmHfSpEkWFxdnGzZsCKoxbtw4q1+/vm3bti0w7cCBA0HzlJaW2gUXXGD9+vULml7VIZlQt8fWrVttwIABlp+fbwsWLLC8vDxr166d1atXzxYtWhTiFqg74d6PVfnb3/5mERER9sgjj1Tr+WZHDlmPGDHCioqKbPfu3fbhhx/aNddcU+nw26uvvmr16tWzlStXBj3/hRdeMEm2atWqwLRj+9HMbODAgXbOOecETavqkHVqaqpVZ/cTroesw7Uf2T8Gq+n9o2+BfOwjOTnZlixZEpivouG2bt0a9Px77rnHmjRpYrt377aioqKgR6NGjezOO+80M7O5c+eapKCaZkcasaqGC9U777wT+B5t7dq19uWXX9qYMWMsKirKJNmkSZNOuWZFwx37aNSokT3zzDNB83br1s2uueaaSuu+bNkyk2SvvfZalcv4/vvvraioyO666y5LSEgI+puX70iq8t1331mrVq2sc+fOvtWsKeHej8f69ttvrW3btnbOOedU+m75VFS1TaKioiw7O9vKysoC82VkZFiXLl0qrfuGDRtMkk2ePLnK+sXFxVZUVGRPPPGESbLi4uLA37x+h3y0cA3kcO1H9o8n5+f+0bdD1s8//7w6deqkyMhItWrVSp07d1a9esFfUUdGRqpt27ZB0zZu3KiSkhK1bNmyyrq7d++WJG3dulWSKh0eadGiRZWHVEI1aNAgTZ8+XePGjdPFF18sSerQoYMef/xxZWdnV/o+71SMHDlSN998sw4ePKi//vWv+vWvf62ysrKgeTZu3KhPPvlELVq0qLJGxfpL0qJFizR58mStW7cu6Pu8mv5+rVmzZho+fLieeuop7dixo9Jr6KJw7cej7d+/X9ddd5327t2r999/31MvStINN9yg0aNHq7S0VGvWrNETTzyhAwcOBG2XjRs36osvvgipH1etWqWJEydq9erVOnDgQNB8JSUlatKkiafxnk7CtR/ZP56cn/tH3wK5Z8+euuSSS044T0xMTKUmLC8vV8uWLTVnzpwqn3O8F8JPo0eP1vDhw/XJJ58oOjpa3bt318svvyxJ6tSpU7XrduzYMfAzleuuu07169fXuHHj1Ldv38C2Ki8v19VXX63s7Owqa1Qsf+XKlcrIyFCfPn00c+ZMtW7dWlFRUSooKNDcuXOrPcZQJSUlSZK+//77sAjkcO5HSSotLdWNN96oTz75REuXLtUFF1zguWbbtm0D/Zienq7ExESNHj1affv21Y033ijpyPp37dpVzz77bJU1Kvpg8+bN6t+/v84991w9++yzSkpKUnR0tN555x1Nmzat0gk3Z7pw7kf2jyfn1/6xxk7qClX79u21bNkyXXHFFYqNjT3ufMnJyZKO/I/pnHPOCUwvKiqqdLZhdcTFxQX9jmzZsmWKjY3VFVdc4bl2hYcfflizZs3ShAkTtGTJEklH1n/fvn0n/X3pH//4RzVo0EBLly5VTExMYHpBQYFv4zuRr776SlLtBVJdcaEfy8vLdccdd+jdd9/V73//e6WmpnqqdzxZWVmaNm2aJkyYoCFDhigiIkLt27fXxx9/rP79+5/wk8XChQt16NAhLViwQO3atQtM93LBCFTmQj9K7B9Pxq/9Y51fOvOWW25RWVmZJk2aVOlvhw8fVnFxsSTpqquuUlRUlKZPnx50ZubRZ9kdrTqnsVf44IMP9Oabb2rEiBG+HnZLSEhQVlaWli5dqnXr1kk6sv6rV6/W0qVLK81fXFysw4cPS5Lq16+viIiIoEM6hYWFQWe9nkio26OoqKjStJ07d+qVV15Rt27d1Lp165CWF65c6Mf/+q//0n//939r5syZgU+uNSEyMlL333+/vvjiC7311luSjqz/zp07NWvWrErz//jjj9q/f7+kI/0oKWjdS0pKQt4BnsrPns5kLvTjsdg/BvN1/+j1S+jj/fD9WMOGDbO4uLgq/5aVlRU4cWDatGk2Y8YMGzNmjLVp0ybojLvx48ebJEtPT7cZM2bYiBEjrE2bNpaYmFjts+YKCwutZ8+eNnnyZHvppZfs3nvvtdjYWLvooosqnVlYsa4FBQUnrHmiH4/v3LnToqOj7ec//7mZHfmR/cUXX2yRkZF25513Wn5+vk2dOjWwvYqKiszM7N133zVJ1rt3b8vPz7fc3Fxr2bKldevWrdLZql7OIszMzLTevXtbTk6Ovfjii/bQQw9Z8+bNLTo62pYvX37S59e1cO/HadOmmSTr1auXvfrqq5UeR18sYvny5SbJJk6ceNK6quLCIGZHzkxNTEy0yy67zMzMysrKLD093SIiIuzWW2+16dOnW15env3qV7+yZs2aBbbr+vXrLTo62rp27WozZsywp556ytq3b28XXnihSbItW7YEluH1LOsFCxbYpEmTbNKkSRYdHW0XXXRR4N8ff/xxSDXqSrj3I/vHYDW9f3QikM3MXnzxRevRo4fFxsZafHy8de3a1bKzs23Xrl2BecrKyiw3N9dat25tsbGxlpaWZp999pmnDfz999/bDTfcYGeddZZFR0fb2WefbQ8++GClZjMzmz59epVnMh7rZFdzyczMtPr169umTZvMzGzv3r02fvx469Chg0VHR1tiYqJdfvnlNnXqVCstLQ087+WXX7aOHTtaTEyMnXvuuVZQUGATJ070teHmzp1rffr0sRYtWlhkZKQlJibakCFD7KOPPjrpc10Q7v1Ycbbt8R5HB93ChQtNkr3wwgsnrXu8QDYzy8nJMUmBHUppaalNmTLFunTpYjExMda0aVPr0aOH5ebmWklJSeB5CxYssG7dulmDBg0sJSXFpkyZYq+88orvgXyibXKynX9dC/d+ZP8YrKb3jxFmZ+gv86vhlltuUWFhof7+97/X9VAAZWdn6/XXX9emTZuCvjcD6gL7R+/q/KSucGFmWrFihV577bW6Hgog6cgJVI888ghhjDrH/tEffEIGAMABdX6WNQAAIJABAHACgQwAgAMIZAAAHBDSWdbl5eXatWuX4uPjT7sbhcM7M9PevXvVpk2bStfirQn0I06EfoRLTqUfQwrkXbt2BS6eDRzP9u3ba+XGE/QjQkE/wiWh9GNIgRwfHx8o2LhxY+8j8+DBBx/0XOPTTz/1XGPbtm2ea7z99tuea1RcVL4u7dmzR0lJSYE+qWku9aMfd2G68sorPdd44YUXPNc4XYRrP37yySeexzJu3DjPNfzgx/4xPz/fc43evXt7ruHVqfRjSIFccRimcePGdb4D9OMiCJGR3q+H4sehMD92GHX9ehyttg7XudSPfvRBdHS05xp1vR1cFG796PV+15I/+zY/+PG+iIuL81zDpfdFKP3ISV0AADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAc4Ma9uk7BunXrPNfIzMz0XCMlJcWJGqhbxcXFnmv40dMIf37sD/zox7y8PM81/JCWllbXQ6h1fEIGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADggsq4HUBcGDx7suUZhYaHnGqhbfvRBSUmJ5xoff/yx5xp+9GNKSornGqi+devWea5RXFzsuYYfEhIS6noIYYlPyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAAB0TW9QBOVffu3T3XaNq0qfeB+CA1NdVzjRUrVngfyBkqJyfHcw0/bsT+29/+1nMNP25un5KS4rkGqq+4uNhzDT/60Y/3RWFhoecaaWlpnmvMnj3bc43axCdkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADIszMTjbTnj171KRJE5WUlKhx48a1Ma7jWrFiRZ0uv4IfN8/2o0ZmZmad16jt/nCpH/2QkpLiucaZeDP346EfTw+DBw92okZt7h/5hAwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcEBkXQ/gVPlxI3ZX5OTkOFHD6w244Y0fPb1ixQrPNQCXjB071nONcHtf8AkZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOCAyLoewKny46bVKSkpnmt0797dc43Zs2d7rpGQkOC5BqrPlZuob9261XONwYMHO1EjMzPTc40zlR/7lLS0NM81/NjH+vG+CDd8QgYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOCCyrgdQF/Ly8jzX8OOG8MnJyZ5r+LEuqL5169Z5ruFHL/nhrbfecqKG12166NAhz2OoC4WFhZ5r+LE/GD58uOcartiyZUtdD+GU8AkZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcENL9kM1MkrRnz54aHUwo/LjXaXl5uQ8j8c6PcRw4cMBzDa+va8XzK/qkprnUj4cPH67rIZx2vL7HS0tLJYVfP+7du9fzWMrKyjzXOJ34sU1rc/8YYSHMtWPHDiUlJXkaFE5/27dvV9u2bWt8OfQjQkE/wiWh9GNIgVxeXq5du3YpPj5eERERvg0Qpwcz0969e9WmTRvVq1fz34LQjzgR+hEuOZV+DCmQAQBAzeKkLgAAHEAgAwDggNMikFNSUpSZmVnXw3AG26Nusf2DpaWlKS0tra6HccaiH4O5vD08B/Ls2bMVEREReDRo0ECdOnXS6NGj9e233/oxxhr39ddfa+TIkTr77LMVGxur9u3b67777tN3331XrXqFhYVB26RevXpq1qyZBg0apNWrV/s8+pqxefNm3X777WrZsqViY2PVsWNHPfzww3U9rJM6Hfrx8ccfV0ZGhlq1aqWIiAjl5OR4rnn0NomIiFDjxo2Vmpqqt99+2/uAa1hJSYmys7PVsWNHxcbGKjk5WSNGjNC2bdvqemgnFe79eOy+7OjHvHnzfKkZbvtHv/PiaCH9DjkUjz32mM4++2wdPHhQ77//vvLz8/XOO+/os88+U8OGDf1ajO/27dunXr16af/+/Ro1apSSkpL08ccfa8aMGVq+fLk++uijap+pedtttyk9PV1lZWXasGGDZs6cqb59+2rNmjXq2rWrz2vin3Xr1iktLU3/9m//pvvvv1/NmzfXtm3btH379roeWsjCtR8lacKECTrrrLN00UUXaenSpb7Vvfrqq3XHHXfIzLR161bl5+fr+uuv1+LFizVw4EDfluOn8vJyXX311fr88881atQoderUSZs2bdLMmTO1dOlSffHFF4qPj6/rYZ5UOPej9H/7sqP16tXLl5rhtH+sybyQJJlHBQUFJsnWrFkTNP2+++4zSTZ37tzjPnffvn1eF29mZsnJyTZs2LBqPXfOnDkmyRYtWhQ0/dFHHzVJtnbt2lOuuWXLFpNkTz/9dND0xYsXmyS76667qjXWUHnZHmVlZXbBBRfYv//7v9uBAwf8HVgtCPd+NDvSP2ZmRUVFJskmTpzoeUyS7O677w6a9vnnn5skGzRokOf6J5KammqpqanVeu6qVatMks2YMSNo+iuvvGKS7M033/RhhDUn3PvxePsyL8J5/1gTeXG0GvsOuV+/fpKkLVu2SJIyMzPVqFEjbd68Wenp6YqPj9fQoUMlHflfcF5enrp06aIGDRqoVatWysrK0g8//HDsfx40efJktW3bVg0bNlTfvn31z3/+s8rlb968WZs3bz7pOCuuotKqVaug6a1bt5YkxcbGnsJan1jv3r0DYztacXGxxo4dq6SkJMXExKhDhw6aMmVKpSt5TZ06VZdffrmaN2+u2NhY9ejRQ2+88UZIyw51e/z5z3/WZ599pokTJyo2NlYHDhw4La7+Ey79KB35jqs2nHfeeUpMTKw0rkOHDmnixInq0KGDYmJilJSUpOzs7EpX0CooKFC/fv3UsmVLxcTE6Pzzz1d+fn5Iy962bZvWr19/0vlq8/1Zm8KpHyvs378/cBW0mhAO+8ea7kffDlkfq2LlmjdvHph2+PBhDRw4UFdeeaWmTp0aOFSTlZWl2bNna/jw4brnnnu0ZcsWzZgxQ//4xz+0atUqRUVFSZIeffRRTZ48Wenp6UpPT9fatWs1YMCAKpukf//+ko58X3Eiffr0Ub169TRmzBg988wzatu2rT755BM9/vjjGjx4sM4991w/NkfQWJo2bRqYduDAAaWmpmrnzp3KyspSu3bt9MEHH2j8+PH6+uuvlZeXF5j3ueeeU0ZGhoYOHarS0lLNmzdPN998sxYtWqRrr732hMsOdXssW7ZMkhQTE6NLLrlEH330kaKjozVkyBDNnDlTzZo1O/UVd0C49GNtKikp0Q8//KD27dsHppWXlysjI0Pvv/++Ro4cqfPOO0+ffvqppk2bpg0bNmj+/PmBefPz89WlSxdlZGQoMjJSCxcu1KhRo1ReXq677777hMu+44479N577530coKXXHKJ4uLi9Mgjj6hZs2bq3LmzNm3apOzsbF166aW66qqrPG2DuhJu/Zibm6sHHnhAERER6tGjhx5//HENGDDAyyaoJBz2jzWeF54+X9v/HZJZtmyZFRUV2fbt223evHnWvHlzi42NtR07dpiZ2bBhw0ySjRs3Luj5K1euNEk2Z86coOlLliwJmr57926Ljo62a6+91srLywPzPfTQQyap0iGI5ORkS05ODmkdXnrpJUtISDBJgcewYcPsp59+OsWtcUTFIZnc3FwrKiqyb775xlauXGmXXnqpSbI//OEPgXknTZpkcXFxtmHDhqAa48aNs/r169u2bdsC0449hFxaWmoXXHCB9evXL2h6VYdkQt0eGRkZJsmaN29uQ4cOtTfeeMMeeeQRi4yMtMsvvzxo27vodOjHCn4fsh4xYoQVFRXZ7t277cMPP7Rrrrmm0qHDV1991erVq2crV64Mev4LL7xgkmzVqlWBaVV9pTFw4EA755xzgqZVdcg6NTXVQt39LFq0yFq3bh30/hw4cKDt3bs3pOfXpXDvx61bt9qAAQMsPz/fFixYYHl5edauXTurV69epcO2oQrn/aOZ/3lxNN8C+dhHcnKyLVmyJDBfRcNt3bo16Pn33HOPNWnSxHbv3m1FRUVBj0aNGtmdd95pZmZz5841SUE1zY40YlUNdyoWL15sAwYMsLy8PPvTn/5k9913n0VGRtr9999frXoVDXfso1GjRvbMM88EzdutWze75pprKq37smXLTJK99tprVS7j+++/t6KiIrvrrrssISEh6G9eviPp16+fSbJrrrkmaPqTTz5pkuwvf/lLterWltOhHyv4HcjHPqKioiw7O9vKysoC82VkZFiXLl0qrfuGDRtMkk2ePLnK+sXFxVZUVGRPPPGESbLi4uLA37x8h2xm9r//+7+Wnp5ujz/+uM2fP99ycnKsYcOGdtNNN1W7Zm05nfqxwnfffWetWrWyzp07V+v54bx/NPM/L47m2yHr559/Xp06dVJkZKRatWqlzp07VzrbLDIystLFtTdu3KiSkhK1bNmyyrq7d++WJG3dulWS1LFjx6C/t2jRIugQx6latWqVrrvuOv3P//yPLrnkEknS4MGD1bhxY+Xm5uoXv/iFzj///GrVHjlypG6++WYdPHhQf/3rX/XrX/+60vexGzdu1CeffKIWLVpUWaNi/SVp0aJFmjx5statWxf0fZ6f18+t+A7ktttuC5p+++23a/z48frggw/C4jBhuPZjTbrhhhs0evRolZaWas2aNXriiSd04MCBoO2yceNGffHFFyH146pVqzRx4kStXr260l3HSkpK1KRJE89j/uqrr9S3b1/97ne/089+9rPAelT8lnTx4sUaNGiQ5+XUtNOpH5s1a6bhw4frqaee0o4dO6p9A49w3D/WZF5IPn6H3LNnz8AAjycmJqZSE5aXl6tly5aaM2dOlc853gvhl9/85jdq1apVpbFnZGQoJydHH3zwQbU3cMeOHQPhdd1116l+/foaN26c+vbtG1hexc86srOzq6zRqVMnSdLKlSuVkZGhPn36aObMmWrdurWioqJUUFCguXPnVmt8VWnTpo2kyictVOwQjj2RxFXh2o81qW3btoF+TE9PV2JiokaPHq2+ffvqxhtvlHRk/bt27apnn322yhoVdzXavHmz+vfvr3PPPVfPPvuskpKSFB0drXfeeUfTpk3z7Rans2fP1sGDB3XdddcFTc/IyJB0ZAcZDoF8uvVjRR98//331Q7kcNw/1mReSDV4Uleo2rdvr2XLlumKK6444RlqycnJko78j+mcc84JTC8qKvIUEt9++22VZxH/9NNPkvy93+3DDz+sWbNmacKECVqyZImkI+u/b9++k37q/OMf/6gGDRpo6dKliomJCUwvKCjwbXyS1KNHD82aNUs7d+4Mmr5r1y5J4R1IoajrfqxNWVlZmjZtmiZMmKAhQ4YoIiJC7du318cff6z+/fuf8JPFwoULdejQIS1YsEDt2rULTF++fLmvY/z2229lZpXeozXx/nSRq/341VdfSfJ3fxAO+8eazos6v3TmLbfcorKyMk2aNKnS3w4fPqzi4mJJ0lVXXaWoqChNnz496MzMo8+yO1qop7F36tRJ3377rVasWBE0/fXXX5ckXXTRRaGtSAgSEhKUlZWlpUuXat26dZKOrP/q1aurvABEcXFx4AWuX7++IiIigpqhsLAw6KzXEwl1e9xwww2KiYlRQUFB0Kecl156SdKRi0uczuq6H2tTZGSk7r//fn3xxRd66623JB1Z/507d2rWrFmV5v/xxx+1f/9+SUf6UQq+6XpJSUnIO8BQf/bUqVMnmZl+//vfB02vifeni+q6H4uKiipN27lzp1555RV169Yt8HMfP4TD/rHG88Lrl9DH++H7sYYNG2ZxcXFV/i0rKytwgYJp06bZjBkzbMyYMdamTZugM+7Gjx9vkiw9Pd1mzJhhI0aMsDZt2lhiYmK1z5pbv369xcXFWaNGjWz8+PH2wgsv2G233WaS7Oqrr65yXQsKCk5Y80Q/pt+5c6dFR0fbz3/+czMz279/v1188cUWGRlpd955p+Xn59vUqVMD26uoqMjMzN59912TZL1797b8/HzLzc21li1bWrdu3Sqdrer1LMLHHnsssP7PP/+8jRw50iIiIuy2224L6fl1Kdz70czsd7/7nU2aNClQv2/fvjZp0iSbNGmSFRYWBuZbvnx5yCd9qYoLg5gdOTM1MTHRLrvsMjM7cmGY9PR0i4iIsFtvvdWmT59ueXl59qtf/cqaNWsW2K7r16+36Oho69q1q82YMcOeeuopa9++vV144YUmKXBxEzNvZ1n/61//srPOOsuio6Ptnnvusd/85jeWlZVl9evXty5dutihQ4dOWqMuhXs/ZmZmWu/evS0nJ8defPFFe+ihh6x58+YWHR1ty5cvr3JdT+f946nkRXU4EchmZi+++KL16NHDYmNjLT4+3rp27WrZ2dm2a9euwDxlZWWWm5trrVu3ttjYWEtLS7PPPvvMcwCtX7/ebrrpJktKSrKoqChLTk62//f//p/t378/aL7p06dXeSbjsU52dZvMzEyrX7++bdq0yczM9u7da+PHj7cOHTpYdHS0JSYm2uWXX25Tp0610tLSwPNefvll69ixo8XExNi5555rBQUFNnHiRN8Duby83KZPn26dOnWyqKgoS0pKsgkTJgSNxVWnQz9WhFVVj6N3ggsXLjRJ9sILL5y05vEC2cwsJycnqHZpaalNmTLFunTpYjExMda0aVPr0aOH5ebmWklJSeB5CxYssG7dulmDBg0sJSXFpkyZEriCll+BbGa2Y8cO+8UvfmFnn322RUdHW+vWre2Xv/xlYGfssnDvx7lz51qfPn2sRYsWFhkZaYmJiTZkyBD76KOPKs17puwfQ82L6ogwO8kv8xFwyy23qLCwUH//+9/reiiAsrOz9frrr2vTpk1B35sBdYH9o3d1flJXuDAzrVixQq+99lpdDwWQdOQEqkceeYQwRp1j/+gPPiEDAOCAOj/LGgAAEMgAADiBQAYAwAEEMgAADgjpLOvy8nLt2rVL8fHxvl6oG6cHM9PevXvVpk2bStfirQn0I06EfoRLTqUfQwrkXbt2BS4mDhzP9u3bq32h+VNBPyIU9CNcEko/hhTI8fHxgYKNGzf2PjIPZs6c6bnGk08+6bnG9u3bPdc4XezZs0dJSUmBPqlpLvXjokWLPNcYN26c5xolJSWea/hxV5zevXt7ruHVmdyPV1xxhecafvTS0Tccqa5PP/3Ucw0XevpU+jGkQK44DNO4ceM6b7gGDRp4ruHHYaW63g4uqq3DdS71Y8OGDT3X8OOwqh/bPi4uznONun49jnYm9mPFTT+88KMfIyO9X3PqdOvpUNaHk7oAAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4wPs9smpZTk5OXQ8BCFi3bp3nGlu3bvU+EB9kZmZ6rlFYWOi5Bqrv448/9lzjT3/6kw8j8W7IkCGeaxQXF3sfSC3iEzIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAGRtbkwP27mXlJS4rlGamqq5xorVqzwXKN79+6eayQkJHiugerz4zV0xeDBg+t6CPDowgsv9FyjsLDQc42cnBzPNZYvX+65RlpamucatYlPyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAAB0TW5sLWrVtXm4s7rvfee89zjb59+/owEu/+9Kc/ea7Bjemrz49t16RJE881SkpKPNfw46byqFspKSmea9x7772ea2zZssVzDT/WJdzwCRkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4IDI2lxYYWFhbS7uuKZNm+a5RmZmpucaftyAe926dZ5rDB482HONM1VxcbHnGiUlJZ5rXHjhhZ5rJCQkeK6B6vNj/+hHjWHDhnmusWLFCs81/NjHhhs+IQMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHBBZmwtz5QboY8eOreshSPLnxvRpaWneB4Jq8+Mm6k2aNPFco7i42HON+fPne64xePBgzzXOVLNnz/ZcIycnx3MNP/YpKSkpnmv48d4KN3xCBgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4ILI2F+bHzcvvvfdezzXy8vI811i3bp3nGqmpqZ5r+HEzcVRfcXGx5xrdu3f3XMOPPvDjhvCFhYWeayQkJHiuEY786AM/9kt+9FJJSYnnGmciPiEDAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABwQWZsLS0lJ8Vxj+fLlnmsMHjzYcw0/1mX27Nmea6BujR071nONIUOGeK7x3nvvea7hh+LiYs81EhISPNcIR2lpaZ5rzJ8/33ONpk2beq4xceJEzzXORHxCBgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAAB4R0P2QzkyTt2bOnRgcTiv3793uuUbE+XpSVlXmusW/fPs81XHhNKsbgx3YNhUv9eODAgboeglP27t3ruYbX1zVc+9GPfi4tLfVcww+HDh3yXMOF97cfTqUfIyyEuXbs2KGkpCTvI8Npbfv27Wrbtm2NL4d+RCjoR7gklH4MKZDLy8u1a9cuxcfHKyIiwrcB4vRgZtq7d6/atGmjevVq/lsQ+hEnQj/CJafSjyEFMgAAqFmc1AUAgAMIZAAAHHBaBHJKSooyMzPrehjOYHvULbZ/MLZH3WL7B0tLS1NaWlpdD6NKngN59uzZioiICDwaNGigTp06afTo0fr222/9GGONKiwsDBr/0Y958+b5UrNevXpq1qyZBg0apNWrV/u8Bv7btGmTbrrpJjVt2lQNGzbUlVdeqeXLl9f1sEIS7v0o+b/9w7kfa+L9WZvCvR937dql//iP/1Dnzp0VHx+vhIQE9ezZU7/97W89/azs2NeycePGSk1N1dtvv+3j6P23fft25ebmqmfPnmratKkSExOVlpamZcuW+VI/pN8hh+Kxxx7T2WefrYMHD+r9999Xfn6+3nnnHX322Wdq2LChX4upMbfddpvS09ODpvXq1cuXmmVlZdqwYYNmzpypvn37as2aNerataun2jVl+/bt6tWrl+rXr68HHnhAcXFxKigo0IABA/Tuu++qT58+dT3EkIRrP9bk9g/HfqxQE+/P2hSu/fivf/1LO3bs0E033aR27drpp59+0l/+8hdlZmbqyy+/1BNPPFHt2ldffbXuuOMOmZm2bt2q/Px8XX/99Vq8eLEGDhzo41r456233tKUKVM0ePBgDRs2TIcPH9bvfvc7XX311XrllVc0fPhwbwswjwoKCkySrVmzJmj6fffdZ5Js7ty5x33uvn37vC7ezMySk5Nt2LBh1Xruli1bTJI9/fTTvozlRDUXL15skuyuu+7ybVlV8bI9Ro0aZZGRkbZ+/frAtP3791tSUpJdfPHFPo2w5oR7P9bE9g/nfqyJ92dtCvd+PJ7rrrvO4uLi7PDhw9V6viS7++67g6Z9/vnnJskGDRrkxxCPKzU11VJTU6v13M8++8yKioqCph08eNDOPfdca9u2reex1dh3yP369ZMkbdmyRZKUmZmpRo0aafPmzUpPT1d8fLyGDh0q6cjv+PLy8tSlSxc1aNBArVq1UlZWln744Ydj//OgyZMnq23btmrYsKH69u2rf/7zn1Uuf/Pmzdq8efMpjXn//v01eqWb3r17S1KlcRUXF2vs2LFKSkpSTEyMOnTooClTpqi8vDxovqlTp+ryyy9X8+bNFRsbqx49euiNN94Iadmhbo+VK1fqoosuUufOnQPTGjZsqIyMDK1du1YbN24MaXmuCZd+rM3tHw79eLSafn/WpnDpx+NJSUnRgQMHfH09zjvvPCUmJlYa16FDhzRx4kR16NBBMTExSkpKUnZ2dqWrgRUUFKhfv35q2bKlYmJidP755ys/Pz+kZW/btk3r168/6XxdunRRYmJi0LSYmBilp6drx44dnq9U59sh62NVbNTmzZsHph0+fFgDBw7UlVdeqalTpwYO1WRlZWn27NkaPny47rnnHm3ZskUzZszQP/7xD61atUpRUVGSpEcffVSTJ09Wenq60tPTtXbtWg0YMKDKpujfv7+kI99BhSI3N1cPPPCAIiIi1KNHDz3++OMaMGCAl01QScVYmjZtGph24MABpaamaufOncrKylK7du30wQcfaPz48fr666+Vl5cXmPe5555TRkaGhg4dqtLSUs2bN08333yzFi1apGuvvfaEyw51exw6dChofBUqXquPPvpIHTt2DGFt3RIu/Vib2z8c+rFCbbw/a1O49GOFH3/8Ufv379e+ffv03nvvqaCgQL169VJsbKyXzRCkpKREP/zwg9q3bx+YVl5eroyMDL3//vsaOXKkzjvvPH366aeaNm2aNmzYoPnz5wfmzc/PV5cuXZSRkaHIyEgtXLhQo0aNUnl5ue6+++4TLvuOO+7Qe++9V+3vxb/55hs1bNjQ+9cPXj9iVxySWbZsmRUVFdn27dtt3rx51rx5c4uNjbUdO3aYmdmwYcNMko0bNy7o+StXrjRJNmfOnKDpS5YsCZq+e/dui46OtmuvvdbKy8sD8z300EMmqdIhmeTkZEtOTj7p+Ldu3WoDBgyw/Px8W7BggeXl5Vm7du2sXr16tmjRompskf87zJabm2tFRUX2zTff2MqVK+3SSy81SfaHP/whMO+kSZMsLi7ONmzYEFRj3LhxVr9+fdu2bVtg2oEDB4LmKS0ttQsuuMD69esXNL2qQ1Shbo/rr7/eEhISbM+ePUHTe/XqZZJs6tSpJ61Rl8K9H2ti+4dzP9bE+7M2hXs/VnjyySdNUuDRv3//oF44VZJsxIgRVlRUZLt377YPP/zQrrnmmkpfT7z66qtWr149W7lyZdDzX3jhBZNkq1atCkw7th/NzAYOHGjnnHNO0LSqDlmnpqZadeNw48aN1qBBA/vP//zPaj3/aL4F8rGP5ORkW7JkSWC+iobbunVr0PPvuecea9Kkie3evduKioqCHo0aNbI777zTzMzmzp1rkoJqmh1pxKoazovvvvvOWrVqZZ07d67W8yt2gMc+GjVqZM8880zQvN26dbNrrrmm0rovW7bMJNlrr71W5TK+//57KyoqsrvuussSEhKC/ublO6N33nkn8D3O2rVr7csvv7QxY8ZYVFSUSbJJkyZVq25tCfd+rIntH879WBWv78/aFO79WKGwsND+8pe/2Ny5c+3222+3/v3725dfflntelVtk6ioKMvOzraysrLAfBkZGdalS5dK675hwwaTZJMnT66yfnFxsRUVFdkTTzxhkqy4uDjwNy/fIR9r//791r17d2vatKnt3LnTcz3fDlk///zz6tSpkyIjI9WqVSt17ty50nU7IyMjK11ce+PGjSopKVHLli2rrLt7925J0tatWyWp0uG6Fi1aVHmIz4tmzZpp+PDheuqpp7Rjx45qX6B+5MiRuvnmm3Xw4EH99a9/1a9//etKd4nauHGjPvnkE7Vo0aLKGhXrL0mLFi3S5MmTtW7duqDvT/y8fu6gQYM0ffp0jRs3ThdffLEkqUOHDnr88ceVnZ2tRo0a+basmhSu/ViT2z8c+7Eqfr0/a1O49mOF5ORkJScnSzpyxvvIkSN11VVX6csvv6z2YesbbrhBo0ePVmlpqdasWaMnnnhCBw4cCNouGzdu1BdffBFSP65atUoTJ07U6tWrK92FraSkRE2aNKnWOI+nrKxMt956qz7//HMtXrxYbdq08VzTt0Du2bOnLrnkkhPOExMTU6kJy8vL1bJlS82ZM6fK5xzvhahpFXdv+f7776v9hu/YsaOuuuoqSdJ1112n+vXra9y4cerbt29gW5WXl+vqq69WdnZ2lTU6deok6cjJPhkZGerTp49mzpyp1q1bKyoqSgUFBZo7d261xnc8o0eP1vDhw/XJJ58oOjpa3bt318svvxw0HteFcz/W1PYP136sih/vz9oUzv1YlZtuukmzZs3S3/72t2r/RKlt27aBfkxPT1diYqJGjx6tvn376sYbb5R0ZP27du2qZ599tsoaFX2wefNm9e/fX+eee66effZZJSUlKTo6Wu+8846mTZtW6YREP/zyl7/UokWLNGfOnMBJel7V2EldoWrfvr2WLVumK6644oT/06r439nGjRt1zjnnBKYXFRVVOtvQD1999ZUkfxv+4Ycf1qxZszRhwgQtWbJE0pH137dvX6Axj+ePf/yjGjRooKVLlyomJiYwvaCgwLfxHS0uLi7od57Lli1TbGysrrjiihpZnitc6cfa2P7h1I/Hqon3p4tc6cdj/fjjj5KOfPL0S1ZWlqZNm6YJEyZoyJAhioiIUPv27fXxxx+rf//+JzzysnDhQh06dEgLFixQu3btAtNr6oJGDzzwgAoKCpSXl6fbbrvNt7p1funMW265RWVlZZo0aVKlvx0+fFjFxcWSpKuuukpRUVGaPn160JlwR5/1ebRQT+svKiqqNG3nzp165ZVX1K1bN7Vu3Tq0FQlBQkKCsrKytHTpUq1bt07SkfVfvXq1li5dWmn+4uJiHT58WJJUv359RUREBB1iLCwsDDrL8ES8/Mzhgw8+0JtvvqkRI0b4ftjHNXXdj1Wpqe0fDv1Ym+9PF9V1P1a1/SXp5ZdfVkREROBrFT9ERkbq/vvv1xdffKG33npL0pH137lzp2bNmlVp/oozv6Uj/SgpaN1LSkpC/g9iqD97kqSnn35aU6dO1UMPPaQxY8aE9JyQef0S+ng/fD/WsGHDLC4ursq/ZWVlBU5kmTZtms2YMcPGjBljbdq0CToDdPz48SbJ0tPTbcaMGTZixAhr06aNJSYmVvsswszMTOvdu7fl5OTYiy++aA899JA1b97coqOjbfny5VWua0FBwQlrnuhiBjt37rTo6Gj7+c9/bmZHTgq4+OKLLTIy0u68807Lz8+3qVOnBrZXxY/Q3333XZNkvXv3tvz8fMvNzbWWLVtat27dKp0d6OWs1sLCQuvZs6dNnjzZXnrpJbv33nstNjbWLrrookpn/roo3PvxVLb/mdCPp/L+dFG49+OYMWPskksusQkTJtiLL75oTz31VODs/P/6r/8Kmnf58uUmySZOnHjSuqriwiBmR86UTkxMtMsuu8zMzMrKyiw9Pd0iIiLs1ltvtenTp1teXp796le/smbNmgW26/r16y06Otq6du1qM2bMsKeeesrat29vF154oUmyLVu2BJbh5SzrN9980yRZx44d7dVXX630+Oabb05a40ScCGQzsxdffNF69OhhsbGxFh8fb127drXs7GzbtWtXYJ6ysjLLzc211q1bW2xsrKWlpdlnn33m6Q0/d+5c69Onj7Vo0cIiIyMtMTHRhgwZYh999FGleadPn17lmYzHOtnVhTIzM61+/fq2adMmMzPbu3evjR8/3jp06GDR0dGWmJhol19+uU2dOtVKS0sDz3v55ZetY8eOFhMTY+eee64VFBTYxIkTfd0Bfv/993bDDTfYWWedZdHR0Xb22Wfbgw8+GBZhbBb+/Xgq2/9M6MdTeX+6KNz78c9//rNdd9111qZNG4uKirL4+Hi74oorrKCgIOjnVWZmCxcuNEn2wgsvnLTu8QLZzCwnJ8ckBf7DVVpaalOmTLEuXbpYTEyMNW3a1Hr06GG5ublWUlISeN6CBQusW7du1qBBA0tJSbEpU6bYK6+84msgV/T38R5e/5MYYebhCuFnmFtuuUWFhYX6+9//XtdDAehHOCU7O1uvv/66Nm3aFHReAUJX5yd1hQsz04oVK/Taa6/V9VAA+hHOWb58uR555BHC2AM+IQMA4IA6P8saAAAQyAAAOIFABgDAAQQyAAAOCOks6/Lycu3atUvx8fE1fuF4hB8z0969e9WmTZtK1+KtCfQjToR+hEtOpR9DCuRdu3YFLuINHM/27dtr5UL/9CNCQT/CJaH0Y0iBHB8fHyjYuHFj7yPz4Hh3PTkVTz75pOca27dv91zDj2356aefeq6RkJDg6fl79uxRUlJSoE9qmkv9+OCDD3qu4cdr6IqhQ4fWeY1w7ceVK1d6Hsvtt9/uuYYfRo0a5bnG+PHjfRhJ3TuVfgwpkCsOwzRu3LjOd4DVvffm0WrjMFYo/Di85cfr4ddrWluH61zqRz8ughAZefpcn8eP9+eZ2o9xcXG+jaWu+fG+qOv3tt9CeW3cSCYAAM5wBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDggFq979vYsWM913juuec810hNTfVcY/DgwZ5r+LEuhYWFnmt0797dc41wVFxc7LnG/PnzPdfIycnxXCMlJcVzDT+4Mo5wtGLFCs81SkpKvA/EB7m5uZ5r+LGPDbd9G5+QAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOiKzNha1bt642F3dcCQkJnmv4cWP6Cy+80HONcLsBt0v86Mfi4mLPNWbPnu25hh99kJOT47mGH++tM9XYsWM91/DjNfSjp1NSUjzXKCws9Fwj3PaPfEIGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADggsjYXNnjwYM81/Lip/FtvveW5RnJysucafqwLwl9aWprnGn70UmZmpuca8+fP91zjTJWQkFDXQ5Dkzzj86Oni4mLPNcINn5ABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA6IrM2FjR071nONwYMHe64xe/ZszzVyc3M91/DjZu5+bI8zlR83US8sLPRcw5Ub06ekpHiusWLFCs81/HhdUH3r1q3zXMOPPsjLy/NcI9zwCRkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4IDIuh7AqfLjJup+1PCDKzemR/WdTq9hWlqa5xp+3Jjej3GcqfzY/oMHD/Zco6SkxHMNP/ogJyfHc43MzEzPNULFJ2QAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOAAAhkAAAcQyAAAOIBABgDAAQQyAAAOIJABAHAAgQwAgAMi63oAp6qwsNBzjby8PM81UlNTPdfgRuzhz4+bl/vRjwkJCZ5rpKSkeK6B6ps9e7bnGsOHD/c+EB80adLEc43u3bs7UaM28QkZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcQCADAOCAyLoewKny42buH3/8secaP/zwg+caCH8JCQmeazRt2tT7QHzgx03l58+f730gZ6ji4mLPNZKTkz3X6N69u+ca9EH18AkZAAAHEMgAADiAQAYAwAEEMgAADiCQAQBwAIEMAIADCGQAABxAIAMA4AACGQAABxDIAAA4gEAGAMABBDIAAA4gkAEAcACBDACAAwhkAAAcENL9kM1MkrRnz54aHUwoDh06VNdDkOTPtqhX7/T4/1DFtqjok5pGP9YMP16//fv3e67h9XUN1348ePCg57GUl5d7rvHTTz95ruHCe9MVp9KPERbCXDt27FBSUpL3keG0tn37drVt27bGl0M/IhT0I1wSSj+GFMjl5eXatWuX4uPjFRER4dsAcXowM+3du1dt2rSplU/99CNOhH6ES06lH0MKZAAAULNOjy8xAQAIcwQyAAAOIJABAHAAgQwAgAMIZAAAHEAgAwDgAAIZAAAH/H/UKE5lQfyE9QAAAABJRU5ErkJggg==\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -876,32 +876,20 @@ }, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1mModel: \"sequential_24\"\u001b[0m\n" - ], "text/html": [ "
Model: \"sequential_24\"\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_24\"\u001b[0m\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ dense_76 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_77 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_78 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", - "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" - ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
               "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
@@ -913,52 +901,64 @@
               "│ dense_78 (Dense)                │ (None, 10)             │           650 │\n",
               "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
               "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ dense_76 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_77 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_78 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" - ], "text/html": [ "
 Total params: 8,970 (35.04 KB)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" - ], "text/html": [ "
 Trainable params: 8,970 (35.04 KB)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ], "text/html": [ "
 Non-trainable params: 0 (0.00 B)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/5\n", "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.1468 - loss: 4.7815 - val_accuracy: 0.2257 - val_loss: 2.4367\n", @@ -1036,32 +1036,20 @@ }, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1mModel: \"sequential_25\"\u001b[0m\n" - ], "text/html": [ "
Model: \"sequential_25\"\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_25\"\u001b[0m\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ dense_79 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_80 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_81 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", - "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" - ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
               "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
@@ -1073,52 +1061,64 @@
               "│ dense_81 (Dense)                │ (None, 10)             │           650 │\n",
               "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
               "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ dense_79 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_80 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_81 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" - ], "text/html": [ "
 Total params: 8,970 (35.04 KB)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" - ], "text/html": [ "
 Trainable params: 8,970 (35.04 KB)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ], "text/html": [ "
 Non-trainable params: 0 (0.00 B)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/5\n", "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.1276 - loss: 7.8350 - val_accuracy: 0.1771 - val_loss: 2.0756\n", @@ -1189,32 +1189,20 @@ }, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1mModel: \"sequential_26\"\u001b[0m\n" - ], "text/html": [ "
Model: \"sequential_26\"\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_26\"\u001b[0m\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ dense_82 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_83 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_84 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", - "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" - ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
               "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
@@ -1226,52 +1214,64 @@
               "│ dense_84 (Dense)                │ (None, 10)             │           650 │\n",
               "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
               "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ dense_82 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_83 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_84 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" - ], "text/html": [ "
 Total params: 8,970 (35.04 KB)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" - ], "text/html": [ "
 Trainable params: 8,970 (35.04 KB)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ], "text/html": [ "
 Non-trainable params: 0 (0.00 B)\n",
               "
\n" - ] + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/5\n", "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4978 - loss: 2.4525 - val_accuracy: 0.9097 - val_loss: 0.2519\n", @@ -1357,32 +1357,20 @@ }, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1mModel: \"sequential_27\"\u001b[0m\n" - ], "text/html": [ "
Model: \"sequential_27\"\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_27\"\u001b[0m\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ dense_85 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_86 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_87 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", - "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" - ], "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
               "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
@@ -1394,52 +1382,64 @@
               "│ dense_87 (Dense)                │ (None, 10)             │           650 │\n",
               "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
               "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ dense_85 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_86 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_87 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" - ], "text/html": [ "
 Total params: 8,970 (35.04 KB)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" - ], "text/html": [ "
 Trainable params: 8,970 (35.04 KB)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ], "text/html": [ "
 Non-trainable params: 0 (0.00 B)\n",
               "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/5\n", "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - accuracy: 0.3018 - loss: 3.8960 - val_accuracy: 0.7049 - val_loss: 0.9948\n", @@ -1511,8 +1511,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/5\n", "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - accuracy: 0.3106 - loss: 2.1019 - val_accuracy: 0.8229 - val_loss: 0.6544\n", @@ -1579,12 +1579,12 @@ }, { "cell_type": "markdown", - "source": [ - "Observation: Using a learning rate that is too high or too low for basic SGD can significantly hurt performance. Adding momentum to SGD or using an adaptive optimizer like Adam can improve the training process and lead to better accuracy. Adding more layers can improve performance but it's not guaranteed." - ], "metadata": { "id": "38wTiOfoul0I" - } + }, + "source": [ + "Observation: Using a learning rate that is too high or too low for basic SGD can significantly hurt performance. Adding momentum to SGD or using an adaptive optimizer like Adam can improve the training process and lead to better accuracy. Adding more layers can improve performance but it's not guaranteed." + ] }, { "cell_type": "markdown", @@ -1612,7 +1612,6 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 112, "metadata": {}, - "execution_count": 112 + "output_type": "execute_result" } ], "source": [ @@ -1644,16 +1644,16 @@ }, { "cell_type": "code", + "execution_count": 113, + "metadata": { + "id": "AGh4OFE1wpkt" + }, + "outputs": [], "source": [ "# Each row in the tensor corresponds to one image. There are 10 columns, and each column represents one of the possible digit classes (0 through 9). The value in each cell is the probability that the model assigned to that particular image belonging to that specific digit class.\n", "# Example: [6.0732709e-06, 2.9026708e-04, 7.9058309e-06, 6.2368972e-06, 4.0833003e-04, 2.5095069e-04, 9.9806643e-01, 1.1068019e-05, 9.2542445e-04, 2.7402937e-05]\n", "# The highest probability is 9.9806643e-01 (which is very close to 1), located at the 6th index. This means the model is highly confident that the first image is a '6'." - ], - "metadata": { - "id": "AGh4OFE1wpkt" - }, - "execution_count": 113, - "outputs": [] + ] }, { "cell_type": "code", @@ -1668,14 +1668,14 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "(tensorflow.python.framework.ops.EagerTensor, TensorShape([360, 10]))" ] }, + "execution_count": 114, "metadata": {}, - "execution_count": 114 + "output_type": "execute_result" } ], "source": [ @@ -1707,7 +1707,6 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 115, "metadata": {}, - "execution_count": 115 + "output_type": "execute_result" } ], "source": [ @@ -1759,14 +1759,14 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 116, "metadata": {}, - "execution_count": 116 + "output_type": "execute_result" } ], "source": [ @@ -1801,14 +1801,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1908,7 +1908,6 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[,\n", @@ -1916,8 +1915,9 @@ " ]" ] }, + "execution_count": 119, "metadata": {}, - "execution_count": 119 + "output_type": "execute_result" } ], "source": [ @@ -1946,7 +1946,6 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, + "execution_count": 120, "metadata": {}, - "execution_count": 120 + "output_type": "execute_result" } ], "source": [ @@ -1988,7 +1988,6 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "array([[ 0.00015817, -0.01590087, 0.00103594, ..., 0.00962818,\n", @@ -2006,8 +2005,9 @@ " -0.00714749, -0.00868595]], dtype=float32)" ] }, + "execution_count": 121, "metadata": {}, - "execution_count": 121 + "output_type": "execute_result" } ], "source": [ @@ -2028,14 +2028,14 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "np.float32(0.008835949)" ] }, + "execution_count": 122, "metadata": {}, - "execution_count": 122 + "output_type": "execute_result" } ], "source": [ @@ -2059,7 +2059,6 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", @@ -2068,8 +2067,9 @@ " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)" ] }, + "execution_count": 123, "metadata": {}, - "execution_count": 123 + "output_type": "execute_result" } ], "source": [ @@ -2090,8 +2090,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/15\n", "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.1310 - loss: 2.2985\n", @@ -2129,14 +2129,14 @@ ] }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -2156,15 +2156,15 @@ }, { "cell_type": "code", - "source": [ - "# The plot visually shows training loss decreases over the epochs.\n", - "# A decreasing loss indicates that the model is learning and improving its performance on the training data with this specific initialization." - ], + "execution_count": 125, "metadata": { "id": "Uz53lEX81iFN" }, - "execution_count": 125, - "outputs": [] + "outputs": [], + "source": [ + "# The plot visually shows training loss decreases over the epochs.\n", + "# A decreasing loss indicates that the model is learning and improving its performance on the training data with this specific initialization." + ] }, { "cell_type": "markdown", @@ -2188,7 +2188,6 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, + "execution_count": 126, "metadata": {}, - "execution_count": 126 + "output_type": "execute_result" } ], "source": [ @@ -2268,9 +2268,11 @@ }, { "cell_type": "code", + "execution_count": 127, "metadata": { "id": "be46c3cc" }, + "outputs": [], "source": [ "from tensorflow.keras import initializers\n", "from tensorflow.keras import optimizers\n", @@ -2292,12 +2294,11 @@ "\n", "model.compile(optimizer=optimizers.SGD(learning_rate=0.1),\n", " loss='categorical_crossentropy', metrics=['accuracy'])" - ], - "execution_count": 127, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 128, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2306,24 +2307,10 @@ "id": "5fd830dd", "outputId": "b0898510-5fdd-4e5e-b956-88f75015d97b" }, - "source": [ - "# Training\n", - "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", - "\n", - "plt.figure(figsize=(12, 4))\n", - "plt.plot(history.history['loss'], label=\"Truncated Normal init (stddev=1e-3)\")\n", - "plt.legend();\n", - "\n", - "loss, accuracy = model.evaluate(X_test, y_test)\n", - "\n", - "print(f'Loss: {loss:.2f}')\n", - "print(f'Accuracy: {accuracy*100:.2f}%')" - ], - "execution_count": 128, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/15\n", "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.0954 - loss: 2.3030\n", @@ -2361,15 +2348,28 @@ ] }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "# Training\n", + "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", + "\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(history.history['loss'], label=\"Truncated Normal init (stddev=1e-3)\")\n", + "plt.legend();\n", + "\n", + "loss, accuracy = model.evaluate(X_test, y_test)\n", + "\n", + "print(f'Loss: {loss:.2f}')\n", + "print(f'Accuracy: {accuracy*100:.2f}%')" ] }, { @@ -2383,9 +2383,11 @@ }, { "cell_type": "code", + "execution_count": 129, "metadata": { "id": "7cc0e652" }, + "outputs": [], "source": [ "from tensorflow.keras import initializers\n", "from tensorflow.keras import optimizers\n", @@ -2407,12 +2409,11 @@ "\n", "model.compile(optimizer=optimizers.SGD(learning_rate=0.1),\n", " loss='categorical_crossentropy', metrics=['accuracy'])" - ], - "execution_count": 129, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 130, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2421,24 +2422,10 @@ "id": "R0RNJQ3U3VlI", "outputId": "57857ca3-7b9f-4f66-f968-f735fea9dfc7" }, - "source": [ - "# Training\n", - "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", - "\n", - "plt.figure(figsize=(12, 4))\n", - "plt.plot(history.history['loss'], label=\"Truncated Normal init (stddev=1.0)\")\n", - "plt.legend();\n", - "\n", - "loss, accuracy = model.evaluate(X_test, y_test)\n", - "\n", - "print(f'Loss: {loss:.2f}')\n", - "print(f'Accuracy: {accuracy*100:.2f}%')" - ], - "execution_count": 130, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/15\n", "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.2127 - loss: 5.7789\n", @@ -2476,15 +2463,28 @@ ] }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "# Training\n", + "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", + "\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(history.history['loss'], label=\"Truncated Normal init (stddev=1.0)\")\n", + "plt.legend();\n", + "\n", + "loss, accuracy = model.evaluate(X_test, y_test)\n", + "\n", + "print(f'Loss: {loss:.2f}')\n", + "print(f'Accuracy: {accuracy*100:.2f}%')" ] }, { @@ -2508,9 +2508,11 @@ }, { "cell_type": "code", + "execution_count": 131, "metadata": { "id": "a9ab0c1e" }, + "outputs": [], "source": [ "from tensorflow.keras import initializers\n", "from tensorflow.keras import optimizers\n", @@ -2532,12 +2534,11 @@ "\n", "model.compile(optimizer=optimizers.SGD(learning_rate=0.1),\n", " loss='categorical_crossentropy', metrics=['accuracy'])" - ], - "execution_count": 131, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 132, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2546,24 +2547,10 @@ "id": "R8O-GQuw3jnI", "outputId": "3e9b70a8-3f3f-4345-d31a-d3ea47ad8e52" }, - "source": [ - "# Training\n", - "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", - "\n", - "plt.figure(figsize=(12, 4))\n", - "plt.plot(history.history['loss'], label=\"Truncated Normal init (stddev=10.0)\")\n", - "plt.legend();\n", - "\n", - "loss, accuracy = model.evaluate(X_test, y_test)\n", - "\n", - "print(f'Loss: {loss:.2f}')\n", - "print(f'Accuracy: {accuracy*100:.2f}%')" - ], - "execution_count": 132, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/15\n", "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.1196 - loss: 111.4976\n", @@ -2601,15 +2588,28 @@ ] }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "# Training\n", + "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", + "\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(history.history['loss'], label=\"Truncated Normal init (stddev=10.0)\")\n", + "plt.legend();\n", + "\n", + "loss, accuracy = model.evaluate(X_test, y_test)\n", + "\n", + "print(f'Loss: {loss:.2f}')\n", + "print(f'Accuracy: {accuracy*100:.2f}%')" ] }, { @@ -2623,9 +2623,11 @@ }, { "cell_type": "code", + "execution_count": 133, "metadata": { "id": "a85c147d" }, + "outputs": [], "source": [ "from tensorflow.keras import initializers\n", "from tensorflow.keras import optimizers\n", @@ -2647,12 +2649,11 @@ "\n", "model.compile(optimizer=optimizers.SGD(learning_rate=0.1),\n", " loss='categorical_crossentropy', metrics=['accuracy'])" - ], - "execution_count": 133, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 134, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2661,24 +2662,10 @@ "id": "gcC8ElI34AWK", "outputId": "d9b43402-26f8-46f8-c587-120c42817b65" }, - "source": [ - "# Training the model for 15 epochs using the fit method and plots the training loss over epochs.\n", - "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", - "\n", - "plt.figure(figsize=(12, 4))\n", - "plt.plot(history.history['loss'], label=\"Zero init\")\n", - "plt.legend();\n", - "\n", - "loss, accuracy = model.evaluate(X_test, y_test)\n", - "\n", - "print(f'Loss: {loss:.2f}')\n", - "print(f'Accuracy: {accuracy*100:.2f}%')" - ], - "execution_count": 134, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/15\n", "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.0986 - loss: 2.3029\n", @@ -2716,15 +2703,28 @@ ] }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "# Training the model for 15 epochs using the fit method and plots the training loss over epochs.\n", + "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", + "\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(history.history['loss'], label=\"Zero init\")\n", + "plt.legend();\n", + "\n", + "loss, accuracy = model.evaluate(X_test, y_test)\n", + "\n", + "print(f'Loss: {loss:.2f}')\n", + "print(f'Accuracy: {accuracy*100:.2f}%')" ] }, { @@ -2738,6 +2738,7 @@ }, { "cell_type": "code", + "execution_count": 135, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2746,43 +2747,10 @@ "id": "e7881215", "outputId": "91ac9efe-76d1-48bd-b1ee-e3c37df464aa" }, - "source": [ - "from tensorflow.keras.optimizers import Adam\n", - "\n", - "# Modifying the optimizer in this cell to use Adam()\n", - "model.compile(\n", - " loss='categorical_crossentropy', # Loss function\n", - " optimizer=Adam(), # Optimizer\n", - " metrics=['accuracy'] # Metrics to evaluate the model\n", - ")\n", - "\n", - "# Training the model\n", - "history_adam = model.fit(\n", - " X_train, # Training data\n", - " y_train, # Training labels\n", - " epochs=15, # Number of epochs\n", - " batch_size=32, # Number of samples per batch\n", - " validation_split=0.2 # Use 20% of the data for validation\n", - ")\n", - "\n", - "plt.figure(figsize=(12, 4))\n", - "plt.plot(history_adam.history['loss'], label=\"Adam\")\n", - "plt.legend();\n", - "plt.title(\"Training Loss with Adam Optimizer\")\n", - "plt.xlabel(\"Epoch\")\n", - "plt.ylabel(\"Loss\")\n", - "plt.show()\n", - "\n", - "loss, accuracy = model.evaluate(X_test, y_test)\n", - "\n", - "print(f'Loss: {loss:.2f}')\n", - "print(f'Accuracy: {accuracy*100:.2f}%')" - ], - "execution_count": 135, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/15\n", "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - accuracy: 0.1173 - loss: 2.3009 - val_accuracy: 0.1076 - val_loss: 2.3042\n", @@ -2817,59 +2785,48 @@ ] }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.0740 - loss: 2.3120 \n", "Loss: 2.31\n", "Accuracy: 7.78%\n" ] } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 999 - }, - "id": "56cdf9e7", - "outputId": "bb602ddc-ee65-4def-d6b2-6f0224498436" - }, + ], "source": [ - "from tensorflow.keras.optimizers import SGD\n", + "from tensorflow.keras.optimizers import Adam\n", "\n", - "# Modifying the optimizer in the compilation cell to use SGD with momentum\n", + "# Modifying the optimizer in this cell to use Adam()\n", "model.compile(\n", - " loss='categorical_crossentropy', # Loss function\n", - " optimizer=SGD(learning_rate=0.01, momentum=0.9), # Optimizer with momentum\n", - " metrics=['accuracy'] # Metrics to evaluate the model\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=Adam(), # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", ")\n", "\n", "# Training the model\n", - "history_sgd_momentum = model.fit(\n", - " X_train, # Training data\n", - " y_train, # Training labels\n", - " epochs=15, # Number of epochs\n", - " batch_size=32, # Number of samples per batch\n", - " validation_split=0.2 # Use 20% of the data for validation\n", + "history_adam = model.fit(\n", + " X_train, # Training data\n", + " y_train, # Training labels\n", + " epochs=15, # Number of epochs\n", + " batch_size=32, # Number of samples per batch\n", + " validation_split=0.2 # Use 20% of the data for validation\n", ")\n", "\n", "plt.figure(figsize=(12, 4))\n", - "plt.plot(history_sgd_momentum.history['loss'], label=\"SGD with Momentum (stddev=0.01 init)\")\n", + "plt.plot(history_adam.history['loss'], label=\"Adam\")\n", "plt.legend();\n", - "plt.title(\"Training Loss with SGD with Momentum and stddev=0.01 Initialization\")\n", + "plt.title(\"Training Loss with Adam Optimizer\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.show()\n", @@ -2878,12 +2835,23 @@ "\n", "print(f'Loss: {loss:.2f}')\n", "print(f'Accuracy: {accuracy*100:.2f}%')" - ], + ] + }, + { + "cell_type": "code", "execution_count": 136, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 999 + }, + "id": "56cdf9e7", + "outputId": "bb602ddc-ee65-4def-d6b2-6f0224498436" + }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/15\n", "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.1192 - loss: 2.2995 - val_accuracy: 0.1076 - val_loss: 2.3082\n", @@ -2918,28 +2886,63 @@ ] }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.1054 - loss: 2.3136 \n", "Loss: 2.31\n", "Accuracy: 9.72%\n" ] } + ], + "source": [ + "from tensorflow.keras.optimizers import SGD\n", + "\n", + "# Modifying the optimizer in the compilation cell to use SGD with momentum\n", + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=SGD(learning_rate=0.01, momentum=0.9), # Optimizer with momentum\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")\n", + "\n", + "# Training the model\n", + "history_sgd_momentum = model.fit(\n", + " X_train, # Training data\n", + " y_train, # Training labels\n", + " epochs=15, # Number of epochs\n", + " batch_size=32, # Number of samples per batch\n", + " validation_split=0.2 # Use 20% of the data for validation\n", + ")\n", + "\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(history_sgd_momentum.history['loss'], label=\"SGD with Momentum (stddev=0.01 init)\")\n", + "plt.legend();\n", + "plt.title(\"Training Loss with SGD with Momentum and stddev=0.01 Initialization\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.show()\n", + "\n", + "loss, accuracy = model.evaluate(X_test, y_test)\n", + "\n", + "print(f'Loss: {loss:.2f}')\n", + "print(f'Accuracy: {accuracy*100:.2f}%')" ] }, { "cell_type": "markdown", + "metadata": { + "id": "CQzQuR3XuBkx" + }, "source": [ "| # | Optimizer | LR | Initialization / Config | Train Loss | Train Acc (%) | Test Loss | Test Acc (%) |Observation | Rating |\n", "|---|--------------------|------|---------------------------------|-------------|----------------|-------------|----------------|-----------------------------------------------------------|-----------|\n", @@ -2954,16 +2957,23 @@ "| 9 | SGD | 0.01 | Truncated Normal (std=10.0) | 7.7802 | 20.23 | 7.89 | 24.72 | Erratic loss; exploding gradients | Poor |\n", "| 10| SGD | 0.1 | Zero Initialization | 2.3033 | 11.12 | 2.31 | 7.78 | No learning; symmetry kept all weights identical | Failed |\n", "| 11| SGD (momentum=0.9) | 0.01 | Truncated Normal (std=0.01) | 2.3021 | 11.37 | 2.31 | 9.72 | Momentum couldn’t overcome bad initialization; stagnant | Failed |\n" - ], - "metadata": { - "id": "CQzQuR3XuBkx" - } + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "End of Lab 1." + ] } ], "metadata": { + "colab": { + "provenance": [] + }, "file_extension": ".py", "kernelspec": { - "display_name": ".venv", + "display_name": "dsi_participant", "language": "python", "name": "python3" }, @@ -2977,7 +2987,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.12.3" }, "mimetype": "text/x-python", "name": "python", @@ -2996,11 +3006,8 @@ "toc_section_display": true, "toc_window_display": false }, - "version": 3, - "colab": { - "provenance": [] - } + "version": 3 }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/01_materials/labs/lab_2.ipynb b/01_materials/labs/lab_2.ipynb index 5d6c2c0b9..c8afa705a 100644 --- a/01_materials/labs/lab_2.ipynb +++ b/01_materials/labs/lab_2.ipynb @@ -56,14 +56,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -149,14 +149,14 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "array([0., 0., 0., 1., 0., 0., 0., 0., 0., 0.])" ] }, + "execution_count": 5, "metadata": {}, - "execution_count": 5 + "output_type": "execute_result" } ], "source": [ @@ -177,7 +177,6 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", @@ -186,8 +185,9 @@ " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]])" ] }, + "execution_count": 6, "metadata": {}, - "execution_count": 6 + "output_type": "execute_result" } ], "source": [ @@ -257,8 +257,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n" ] @@ -290,8 +290,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "[[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n", " [2.47262316e-03 9.97527377e-01 1.38536042e-11]]\n" @@ -325,8 +325,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "1.0\n" ] @@ -348,8 +348,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "softmax of 2 vectors:\n", "[[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n", @@ -385,8 +385,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "[1. 1.]\n" ] @@ -425,8 +425,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "0.01005033585350145\n" ] @@ -472,8 +472,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "4.605170185988091\n" ] @@ -504,8 +504,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "0.010050335853503449\n" ] @@ -640,14 +640,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -693,8 +693,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Average NLL over the last 100 samples at step 100: 261\n", "Average NLL over the last 100 samples at step 200: 216\n", @@ -761,14 +761,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -815,14 +815,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -991,14 +991,14 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "np.float64(3513.8555345759937)" ] }, + "execution_count": 24, "metadata": {}, - "execution_count": 24 + "output_type": "execute_result" } ], "source": [ @@ -1017,14 +1017,14 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "np.float64(0.11263916175507531)" ] }, + "execution_count": 25, "metadata": {}, - "execution_count": 25 + "output_type": "execute_result" } ], "source": [ @@ -1044,14 +1044,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1080,8 +1080,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Random init: train loss: 3513.85553, train acc: 0.113, test acc: 0.122\n", "Epoch #1, train loss: 2637.79246, train acc: 0.445, test acc: 0.404\n", @@ -1135,14 +1135,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1163,14 +1163,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1194,14 +1194,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1225,6 +1225,7 @@ }, { "cell_type": "code", + "execution_count": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1233,81 +1234,80 @@ "id": "5a1df71d", "outputId": "1d0ca064-e5be-49c5-f42b-c7aa0b58455b" }, - "source": [ - "# Calculate the loss for each test sample\n", - "test_losses = []\n", - "for i in range(len(X_test)):\n", - " loss = model.loss(X_test[i:i+1], y_test[i:i+1])\n", - " test_losses.append(loss)\n", - "\n", - "# Find the indices of the samples with the highest loss (worst predictions)\n", - "worst_predictions_indices = np.argsort(test_losses)[-5:] # Get the top 5 worst predictions\n", - "\n", - "print(\"Worst predictions:\", worst_predictions_indices)\n", - "\n", - "# Plot the worst predictions\n", - "for idx in worst_predictions_indices:\n", - " plot_prediction(model, sample_idx=idx)" - ], - "execution_count": 31, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Worst predictions: [170 69 244 99 107]\n" ] }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "# Calculate the loss for each test sample\n", + "test_losses = []\n", + "for i in range(len(X_test)):\n", + " loss = model.loss(X_test[i:i+1], y_test[i:i+1])\n", + " test_losses.append(loss)\n", + "\n", + "# Find the indices of the samples with the highest loss (worst predictions)\n", + "worst_predictions_indices = np.argsort(test_losses)[-5:] # Get the top 5 worst predictions\n", + "\n", + "print(\"Worst predictions:\", worst_predictions_indices)\n", + "\n", + "# Plot the worst predictions\n", + "for idx in worst_predictions_indices:\n", + " plot_prediction(model, sample_idx=idx)" ] }, { @@ -1327,9 +1327,11 @@ }, { "cell_type": "code", + "execution_count": 32, "metadata": { "id": "9aab447a" }, + "outputs": [], "source": [ "class NeuralNet():\n", " \"\"\"MLP with 2 hidden layers with sigmoid activation\"\"\"\n", @@ -1455,12 +1457,11 @@ " raise NotImplementedError(\"You need to correctly implement the NeuralNet class.\")\n", "except Exception as e:\n", " print(f\"An unexpected error occurred during testing: {e}\")" - ], - "execution_count": 32, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1468,6 +1469,15 @@ "id": "0b5feca4", "outputId": "436fec29-ca83-4a63-f726-ccd6188aaa56" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'learning_rates': [0.001, 0.01, 0.1], 'hidden_layer_sizes': [{'hidden_size1': 32, 'hidden_size2': 0}, {'hidden_size1': 64, 'hidden_size2': 0}, {'hidden_size1': 32, 'hidden_size2': 16}, {'hidden_size1': 32, 'hidden_size2': 32}, {'hidden_size1': 64, 'hidden_size2': 32}, {'hidden_size1': 64, 'hidden_size2': 64}], 'epochs': [10, 15, 20]}\n" + ] + } + ], "source": [ "# Hyper parameters settings\n", "# Adjusting: learning rates, sizes of hidden layers and epochs.\n", @@ -1486,20 +1496,11 @@ "}\n", "\n", "print(hyperparameters)" - ], - "execution_count": 33, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "{'learning_rates': [0.001, 0.01, 0.1], 'hidden_layer_sizes': [{'hidden_size1': 32, 'hidden_size2': 0}, {'hidden_size1': 64, 'hidden_size2': 0}, {'hidden_size1': 32, 'hidden_size2': 16}, {'hidden_size1': 32, 'hidden_size2': 32}, {'hidden_size1': 64, 'hidden_size2': 32}, {'hidden_size1': 64, 'hidden_size2': 64}], 'epochs': [10, 15, 20]}\n" - ] - } ] }, { "cell_type": "code", + "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1507,43 +1508,10 @@ "id": "c8e4f681", "outputId": "5759fe51-1233-455d-9f5e-7b734a512ab3" }, - "source": [ - "# with each combination and evaluating its performance on the test set.\n", - "# Iterating through the different hyperparameter combinations training a new NeuralNet model\n", - "\n", - "results = []\n", - "\n", - "for learning_rate in hyperparameters[\"learning_rates\"]:\n", - " for hidden_layer_config in hyperparameters[\"hidden_layer_sizes\"]:\n", - " for epochs in hyperparameters[\"epochs\"]:\n", - " hidden_size1 = hidden_layer_config[\"hidden_size1\"]\n", - " hidden_size2 = hidden_layer_config[\"hidden_size2\"]\n", - "\n", - " print(f\"Training with: Learning Rate = {learning_rate}, Hidden Layers = ({hidden_size1}, {hidden_size2}), Epochs = {epochs}\")\n", - "\n", - " if hidden_size2 == 0:\n", - " model = NeuralNet(n_features, hidden_size1, 0, n_classes)\n", - " else:\n", - " model = NeuralNet(n_features, hidden_size1, hidden_size2, n_classes)\n", - "\n", - " for epoch in range(epochs):\n", - " for i, (x, y) in enumerate(zip(X_train, y_train)):\n", - " model.train(x, y, learning_rate)\n", - "\n", - " test_accuracy = model.accuracy(X_test, y_test)\n", - " results.append({\n", - " \"learning_rate\": learning_rate,\n", - " \"hidden_layer_sizes\": (hidden_size1, hidden_size2),\n", - " \"epochs\": epochs,\n", - " \"test_accuracy\": test_accuracy\n", - " })\n", - " print(f\"Test Accuracy: {test_accuracy:.3f}\")\n" - ], - "execution_count": 34, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Training with: Learning Rate = 0.001, Hidden Layers = (32, 0), Epochs = 10\n", "Test Accuracy: 0.063\n", @@ -1655,10 +1623,43 @@ "Test Accuracy: 0.641\n" ] } + ], + "source": [ + "# with each combination and evaluating its performance on the test set.\n", + "# Iterating through the different hyperparameter combinations training a new NeuralNet model\n", + "\n", + "results = []\n", + "\n", + "for learning_rate in hyperparameters[\"learning_rates\"]:\n", + " for hidden_layer_config in hyperparameters[\"hidden_layer_sizes\"]:\n", + " for epochs in hyperparameters[\"epochs\"]:\n", + " hidden_size1 = hidden_layer_config[\"hidden_size1\"]\n", + " hidden_size2 = hidden_layer_config[\"hidden_size2\"]\n", + "\n", + " print(f\"Training with: Learning Rate = {learning_rate}, Hidden Layers = ({hidden_size1}, {hidden_size2}), Epochs = {epochs}\")\n", + "\n", + " if hidden_size2 == 0:\n", + " model = NeuralNet(n_features, hidden_size1, 0, n_classes)\n", + " else:\n", + " model = NeuralNet(n_features, hidden_size1, hidden_size2, n_classes)\n", + "\n", + " for epoch in range(epochs):\n", + " for i, (x, y) in enumerate(zip(X_train, y_train)):\n", + " model.train(x, y, learning_rate)\n", + "\n", + " test_accuracy = model.accuracy(X_test, y_test)\n", + " results.append({\n", + " \"learning_rate\": learning_rate,\n", + " \"hidden_layer_sizes\": (hidden_size1, hidden_size2),\n", + " \"epochs\": epochs,\n", + " \"test_accuracy\": test_accuracy\n", + " })\n", + " print(f\"Test Accuracy: {test_accuracy:.3f}\")\n" ] }, { "cell_type": "code", + "execution_count": 37, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1667,74 +1668,13 @@ "id": "2efea145", "outputId": "b01418d0-bbe7-49dd-8c5f-0092827b912d" }, - "source": [ - "import pandas as pd\n", - "\n", - "results_df = pd.DataFrame(results)\n", - "display(results_df.sort_values(by='test_accuracy', ascending=False))" - ], - "execution_count": 37, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - " learning_rate hidden_layer_sizes epochs test_accuracy\n", - "34 0.010 (64, 64) 15 0.977778\n", - "35 0.010 (64, 64) 20 0.974074\n", - "31 0.010 (64, 32) 15 0.966667\n", - "32 0.010 (64, 32) 20 0.966667\n", - "29 0.010 (32, 32) 20 0.959259\n", - "33 0.010 (64, 64) 10 0.955556\n", - "25 0.010 (32, 16) 15 0.944444\n", - "30 0.010 (64, 32) 10 0.940741\n", - "28 0.010 (32, 32) 15 0.937037\n", - "24 0.010 (32, 16) 10 0.937037\n", - "26 0.010 (32, 16) 20 0.933333\n", - "27 0.010 (32, 32) 10 0.925926\n", - "50 0.100 (64, 32) 20 0.785185\n", - "17 0.001 (64, 64) 20 0.722222\n", - "44 0.100 (32, 16) 20 0.688889\n", - "47 0.100 (32, 32) 20 0.685185\n", - "43 0.100 (32, 16) 15 0.659259\n", - "48 0.100 (64, 32) 10 0.644444\n", - "53 0.100 (64, 64) 20 0.640741\n", - "51 0.100 (64, 64) 10 0.637037\n", - "14 0.001 (64, 32) 20 0.633333\n", - "16 0.001 (64, 64) 15 0.548148\n", - "45 0.100 (32, 32) 10 0.544444\n", - "49 0.100 (64, 32) 15 0.522222\n", - "46 0.100 (32, 32) 15 0.488889\n", - "13 0.001 (64, 32) 15 0.466667\n", - "11 0.001 (32, 32) 20 0.451852\n", - "52 0.100 (64, 64) 15 0.448148\n", - "42 0.100 (32, 16) 10 0.400000\n", - "15 0.001 (64, 64) 10 0.392593\n", - "8 0.001 (32, 16) 20 0.296296\n", - "10 0.001 (32, 32) 15 0.251852\n", - "7 0.001 (32, 16) 15 0.229630\n", - "12 0.001 (64, 32) 10 0.214815\n", - "38 0.100 (32, 0) 20 0.088889\n", - "39 0.100 (64, 0) 10 0.088889\n", - "37 0.100 (32, 0) 15 0.088889\n", - "36 0.100 (32, 0) 10 0.088889\n", - "40 0.100 (64, 0) 15 0.088889\n", - "41 0.100 (64, 0) 20 0.088889\n", - "9 0.001 (32, 32) 10 0.066667\n", - "19 0.010 (32, 0) 15 0.062963\n", - "21 0.010 (64, 0) 10 0.062963\n", - "20 0.010 (32, 0) 20 0.062963\n", - "4 0.001 (64, 0) 15 0.062963\n", - "5 0.001 (64, 0) 20 0.062963\n", - "2 0.001 (32, 0) 20 0.062963\n", - "1 0.001 (32, 0) 15 0.062963\n", - "0 0.001 (32, 0) 10 0.062963\n", - "18 0.010 (32, 0) 10 0.062963\n", - "6 0.001 (32, 16) 10 0.062963\n", - "3 0.001 (64, 0) 10 0.062963\n", - "22 0.010 (64, 0) 15 0.062963\n", - "23 0.010 (64, 0) 20 0.062963" - ], + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"display(results_df\",\n \"rows\": 54,\n \"fields\": [\n {\n \"column\": \"learning_rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.04511871134249678,\n \"min\": 0.001,\n \"max\": 0.1,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.01,\n 0.1,\n 0.001\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hidden_layer_sizes\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 6,\n \"samples\": [\n [\n 64,\n 64\n ],\n [\n 64,\n 32\n ],\n [\n 64,\n 0\n ]\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"epochs\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4,\n \"min\": 10,\n \"max\": 20,\n \"num_unique_values\": 3,\n \"samples\": [\n 15,\n 20,\n 10\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"test_accuracy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3524008491749039,\n \"min\": 0.06296296296296296,\n \"max\": 0.9777777777777777,\n \"num_unique_values\": 35,\n \"samples\": [\n 0.4,\n 0.6851851851851852,\n 0.45185185185185184\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe" + }, "text/html": [ "\n", "
\n", @@ -2354,17 +2294,78 @@ "
\n", " \n" ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "summary": "{\n \"name\": \"display(results_df\",\n \"rows\": 54,\n \"fields\": [\n {\n \"column\": \"learning_rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.04511871134249678,\n \"min\": 0.001,\n \"max\": 0.1,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.01,\n 0.1,\n 0.001\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hidden_layer_sizes\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 6,\n \"samples\": [\n [\n 64,\n 64\n ],\n [\n 64,\n 32\n ],\n [\n 64,\n 0\n ]\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"epochs\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4,\n \"min\": 10,\n \"max\": 20,\n \"num_unique_values\": 3,\n \"samples\": [\n 15,\n 20,\n 10\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"test_accuracy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3524008491749039,\n \"min\": 0.06296296296296296,\n \"max\": 0.9777777777777777,\n \"num_unique_values\": 35,\n \"samples\": [\n 0.4,\n 0.6851851851851852,\n 0.45185185185185184\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } + "text/plain": [ + " learning_rate hidden_layer_sizes epochs test_accuracy\n", + "34 0.010 (64, 64) 15 0.977778\n", + "35 0.010 (64, 64) 20 0.974074\n", + "31 0.010 (64, 32) 15 0.966667\n", + "32 0.010 (64, 32) 20 0.966667\n", + "29 0.010 (32, 32) 20 0.959259\n", + "33 0.010 (64, 64) 10 0.955556\n", + "25 0.010 (32, 16) 15 0.944444\n", + "30 0.010 (64, 32) 10 0.940741\n", + "28 0.010 (32, 32) 15 0.937037\n", + "24 0.010 (32, 16) 10 0.937037\n", + "26 0.010 (32, 16) 20 0.933333\n", + "27 0.010 (32, 32) 10 0.925926\n", + "50 0.100 (64, 32) 20 0.785185\n", + "17 0.001 (64, 64) 20 0.722222\n", + "44 0.100 (32, 16) 20 0.688889\n", + "47 0.100 (32, 32) 20 0.685185\n", + "43 0.100 (32, 16) 15 0.659259\n", + "48 0.100 (64, 32) 10 0.644444\n", + "53 0.100 (64, 64) 20 0.640741\n", + "51 0.100 (64, 64) 10 0.637037\n", + "14 0.001 (64, 32) 20 0.633333\n", + "16 0.001 (64, 64) 15 0.548148\n", + "45 0.100 (32, 32) 10 0.544444\n", + "49 0.100 (64, 32) 15 0.522222\n", + "46 0.100 (32, 32) 15 0.488889\n", + "13 0.001 (64, 32) 15 0.466667\n", + "11 0.001 (32, 32) 20 0.451852\n", + "52 0.100 (64, 64) 15 0.448148\n", + "42 0.100 (32, 16) 10 0.400000\n", + "15 0.001 (64, 64) 10 0.392593\n", + "8 0.001 (32, 16) 20 0.296296\n", + "10 0.001 (32, 32) 15 0.251852\n", + "7 0.001 (32, 16) 15 0.229630\n", + "12 0.001 (64, 32) 10 0.214815\n", + "38 0.100 (32, 0) 20 0.088889\n", + "39 0.100 (64, 0) 10 0.088889\n", + "37 0.100 (32, 0) 15 0.088889\n", + "36 0.100 (32, 0) 10 0.088889\n", + "40 0.100 (64, 0) 15 0.088889\n", + "41 0.100 (64, 0) 20 0.088889\n", + "9 0.001 (32, 32) 10 0.066667\n", + "19 0.010 (32, 0) 15 0.062963\n", + "21 0.010 (64, 0) 10 0.062963\n", + "20 0.010 (32, 0) 20 0.062963\n", + "4 0.001 (64, 0) 15 0.062963\n", + "5 0.001 (64, 0) 20 0.062963\n", + "2 0.001 (32, 0) 20 0.062963\n", + "1 0.001 (32, 0) 15 0.062963\n", + "0 0.001 (32, 0) 10 0.062963\n", + "18 0.010 (32, 0) 10 0.062963\n", + "6 0.001 (32, 16) 10 0.062963\n", + "3 0.001 (64, 0) 10 0.062963\n", + "22 0.010 (64, 0) 15 0.062963\n", + "23 0.010 (64, 0) 20 0.062963" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "import pandas as pd\n", + "\n", + "results_df = pd.DataFrame(results)\n", + "display(results_df.sort_values(by='test_accuracy', ascending=False))" ] }, { "cell_type": "code", + "execution_count": 38, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2372,6 +2373,16 @@ "id": "cbcaa2bf", "outputId": "0c215033-38d2-4faa-ee7d-b6ff9cda5dbf" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Test Accuracy: 0.9777777777777777\n", + "Best Hyperparameters: {'learning_rate': 0.01, 'hidden_layer_sizes': (64, 64), 'epochs': 15, 'test_accuracy': np.float64(0.9777777777777777)}\n" + ] + } + ], "source": [ "# Determining the combination of hyperparameters that resulted in the best test accuracy.\n", "best_accuracy = 0\n", @@ -2384,21 +2395,11 @@ "\n", "print(\"Best Test Accuracy:\", best_accuracy)\n", "print(\"Best Hyperparameters:\", best_hyperparameters)" - ], - "execution_count": 38, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Best Test Accuracy: 0.9777777777777777\n", - "Best Hyperparameters: {'learning_rate': 0.01, 'hidden_layer_sizes': (64, 64), 'epochs': 15, 'test_accuracy': np.float64(0.9777777777777777)}\n" - ] - } ] }, { "cell_type": "code", + "execution_count": 40, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2406,19 +2407,10 @@ "id": "18067a28", "outputId": "379593da-d211-440d-8437-43df0d43cc46" }, - "source": [ - "# Best test accuracy achieved and the corresponding hyperparameters.\n", - "print(f\"The best test accuracy achieved was: {best_accuracy:.4f}\")\n", - "print(\"The corresponding hyperparameters were:\")\n", - "print(f\" Learning Rate: {best_hyperparameters['learning_rate']}\")\n", - "print(f\" Hidden Layer Sizes: {best_hyperparameters['hidden_layer_sizes']}\")\n", - "print(f\" Epochs: {best_hyperparameters['epochs']}\")" - ], - "execution_count": 40, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "The best test accuracy achieved was: 0.9778\n", "The corresponding hyperparameters were:\n", @@ -2427,10 +2419,28 @@ " Epochs: 15\n" ] } + ], + "source": [ + "# Best test accuracy achieved and the corresponding hyperparameters.\n", + "print(f\"The best test accuracy achieved was: {best_accuracy:.4f}\")\n", + "print(\"The corresponding hyperparameters were:\")\n", + "print(f\" Learning Rate: {best_hyperparameters['learning_rate']}\")\n", + "print(f\" Hidden Layer Sizes: {best_hyperparameters['hidden_layer_sizes']}\")\n", + "print(f\" Epochs: {best_hyperparameters['epochs']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "End of Lab 2." ] } ], "metadata": { + "colab": { + "provenance": [] + }, "kernelspec": { "display_name": ".venv", "language": "python", @@ -2447,11 +2457,8 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.12" - }, - "colab": { - "provenance": [] } }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/01_materials/labs/lab_3.ipynb b/01_materials/labs/lab_3.ipynb index 84647f6c4..6d9d51141 100644 --- a/01_materials/labs/lab_3.ipynb +++ b/01_materials/labs/lab_3.ipynb @@ -69,24 +69,12 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": [ - " user_id item_id rating timestamp\n", - "0 196 242 3 881250949\n", - "1 186 302 3 891717742\n", - "2 22 377 1 878887116\n", - "3 244 51 2 880606923\n", - "4 166 346 1 886397596\n", - "... ... ... ... ...\n", - "99995 880 476 3 880175444\n", - "99996 716 204 5 879795543\n", - "99997 276 1090 1 874795795\n", - "99998 13 225 2 882399156\n", - "99999 12 203 3 879959583\n", - "\n", - "[100000 rows x 4 columns]" - ], + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"raw_ratings\",\n \"rows\": 100000,\n \"fields\": [\n {\n \"column\": \"user_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 266,\n \"min\": 1,\n \"max\": 943,\n \"num_unique_values\": 943,\n \"samples\": [\n 262,\n 136,\n 821\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"item_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 330,\n \"min\": 1,\n \"max\": 1682,\n \"num_unique_values\": 1682,\n \"samples\": [\n 1557,\n 808,\n 1618\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 1,\n 5,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"timestamp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5343856,\n \"min\": 874724710,\n \"max\": 893286638,\n \"num_unique_values\": 49282,\n \"samples\": [\n 889728713,\n 888443306,\n 880605158\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "raw_ratings" + }, "text/html": [ "\n", "
\n", @@ -461,14 +449,26 @@ "
\n", " \n" ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "raw_ratings", - "summary": "{\n \"name\": \"raw_ratings\",\n \"rows\": 100000,\n \"fields\": [\n {\n \"column\": \"user_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 266,\n \"min\": 1,\n \"max\": 943,\n \"num_unique_values\": 943,\n \"samples\": [\n 262,\n 136,\n 821\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"item_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 330,\n \"min\": 1,\n \"max\": 1682,\n \"num_unique_values\": 1682,\n \"samples\": [\n 1557,\n 808,\n 1618\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 1,\n 5,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"timestamp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5343856,\n \"min\": 874724710,\n \"max\": 893286638,\n \"num_unique_values\": 49282,\n \"samples\": [\n 889728713,\n 888443306,\n 880605158\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } + "text/plain": [ + " user_id item_id rating timestamp\n", + "0 196 242 3 881250949\n", + "1 186 302 3 891717742\n", + "2 22 377 1 878887116\n", + "3 244 51 2 880606923\n", + "4 166 346 1 886397596\n", + "... ... ... ... ...\n", + "99995 880 476 3 880175444\n", + "99996 716 204 5 879795543\n", + "99997 276 1090 1 874795795\n", + "99998 13 225 2 882399156\n", + "99999 12 203 3 879959583\n", + "\n", + "[100000 rows x 4 columns]" + ] }, + "execution_count": 2, "metadata": {}, - "execution_count": 2 + "output_type": "execute_result" } ], "source": [ @@ -481,6 +481,7 @@ }, { "cell_type": "code", + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -488,21 +489,20 @@ "id": "660ebd35", "outputId": "bc2af33b-36a0-462a-c754-30fe977cf7d4" }, - "source": [ - "if ML_100K_FOLDER.exists():\n", - " print(f\"{ML_100K_FOLDER} exists, the zip file was likely extracted.\")\n", - "else:\n", - " print(f\"{ML_100K_FOLDER} does not exist, the zip file may not have been extracted.\")" - ], - "execution_count": 3, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "ml-100k exists, the zip file was likely extracted.\n" ] } + ], + "source": [ + "if ML_100K_FOLDER.exists():\n", + " print(f\"{ML_100K_FOLDER} exists, the zip file was likely extracted.\")\n", + "else:\n", + " print(f\"{ML_100K_FOLDER} does not exist, the zip file may not have been extracted.\")" ] }, { @@ -531,37 +531,12 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": [ - " item_id title release_date \\\n", - "0 1 Toy Story (1995) 01-Jan-1995 \n", - "1 2 GoldenEye (1995) 01-Jan-1995 \n", - "2 3 Four Rooms (1995) 01-Jan-1995 \n", - "3 4 Get Shorty (1995) 01-Jan-1995 \n", - "4 5 Copycat (1995) 01-Jan-1995 \n", - "... ... ... ... \n", - "1677 1678 Mat' i syn (1997) 06-Feb-1998 \n", - "1678 1679 B. Monkey (1998) 06-Feb-1998 \n", - "1679 1680 Sliding Doors (1998) 01-Jan-1998 \n", - "1680 1681 You So Crazy (1994) 01-Jan-1994 \n", - "1681 1682 Scream of Stone (Schrei aus Stein) (1991) 08-Mar-1996 \n", - "\n", - " video_release_date imdb_url \n", - "0 NaN http://us.imdb.com/M/title-exact?Toy%20Story%2... \n", - "1 NaN http://us.imdb.com/M/title-exact?GoldenEye%20(... \n", - "2 NaN http://us.imdb.com/M/title-exact?Four%20Rooms%... \n", - "3 NaN http://us.imdb.com/M/title-exact?Get%20Shorty%... \n", - "4 NaN http://us.imdb.com/M/title-exact?Copycat%20(1995) \n", - "... ... ... \n", - "1677 NaN http://us.imdb.com/M/title-exact?Mat%27+i+syn+... \n", - "1678 NaN http://us.imdb.com/M/title-exact?B%2E+Monkey+(... \n", - "1679 NaN http://us.imdb.com/Title?Sliding+Doors+(1998) \n", - "1680 NaN http://us.imdb.com/M/title-exact?You%20So%20Cr... \n", - "1681 NaN http://us.imdb.com/M/title-exact?Schrei%20aus%... \n", - "\n", - "[1682 rows x 5 columns]" - ], + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"items\",\n \"rows\": 1682,\n \"fields\": [\n {\n \"column\": \"item_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 485,\n \"min\": 1,\n \"max\": 1682,\n \"num_unique_values\": 1682,\n \"samples\": [\n 1394,\n 744,\n 1606\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1664,\n \"samples\": [\n \"Madame Butterfly (1995)\",\n \"Wrong Trousers, The (1993)\",\n \"Breaking the Waves (1996)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release_date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 240,\n \"samples\": [\n \"01-Jan-1996\",\n \"05-Feb-1996\",\n \"22-Aug-1997\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"video_release_date\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"imdb_url\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1660,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "items" + }, "text/html": [ "\n", "
\n", @@ -948,14 +923,39 @@ "
\n", " \n" ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "items", - "summary": "{\n \"name\": \"items\",\n \"rows\": 1682,\n \"fields\": [\n {\n \"column\": \"item_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 485,\n \"min\": 1,\n \"max\": 1682,\n \"num_unique_values\": 1682,\n \"samples\": [\n 1394,\n 744,\n 1606\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1664,\n \"samples\": [\n \"Madame Butterfly (1995)\",\n \"Wrong Trousers, The (1993)\",\n \"Breaking the Waves (1996)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release_date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 240,\n \"samples\": [\n \"01-Jan-1996\",\n \"05-Feb-1996\",\n \"22-Aug-1997\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"video_release_date\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"imdb_url\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1660,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } + "text/plain": [ + " item_id title release_date \\\n", + "0 1 Toy Story (1995) 01-Jan-1995 \n", + "1 2 GoldenEye (1995) 01-Jan-1995 \n", + "2 3 Four Rooms (1995) 01-Jan-1995 \n", + "3 4 Get Shorty (1995) 01-Jan-1995 \n", + "4 5 Copycat (1995) 01-Jan-1995 \n", + "... ... ... ... \n", + "1677 1678 Mat' i syn (1997) 06-Feb-1998 \n", + "1678 1679 B. Monkey (1998) 06-Feb-1998 \n", + "1679 1680 Sliding Doors (1998) 01-Jan-1998 \n", + "1680 1681 You So Crazy (1994) 01-Jan-1994 \n", + "1681 1682 Scream of Stone (Schrei aus Stein) (1991) 08-Mar-1996 \n", + "\n", + " video_release_date imdb_url \n", + "0 NaN http://us.imdb.com/M/title-exact?Toy%20Story%2... \n", + "1 NaN http://us.imdb.com/M/title-exact?GoldenEye%20(... \n", + "2 NaN http://us.imdb.com/M/title-exact?Four%20Rooms%... \n", + "3 NaN http://us.imdb.com/M/title-exact?Get%20Shorty%... \n", + "4 NaN http://us.imdb.com/M/title-exact?Copycat%20(1995) \n", + "... ... ... \n", + "1677 NaN http://us.imdb.com/M/title-exact?Mat%27+i+syn+... \n", + "1678 NaN http://us.imdb.com/M/title-exact?B%2E+Monkey+(... \n", + "1679 NaN http://us.imdb.com/Title?Sliding+Doors+(1998) \n", + "1680 NaN http://us.imdb.com/M/title-exact?You%20So%20Cr... \n", + "1681 NaN http://us.imdb.com/M/title-exact?Schrei%20aus%... \n", + "\n", + "[1682 rows x 5 columns]" + ] }, + "execution_count": 4, "metadata": {}, - "execution_count": 4 + "output_type": "execute_result" } ], "source": [ @@ -1021,30 +1021,12 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": [ - " item_id title release_date video_release_date \\\n", - "0 1 Toy Story (1995) 1995-01-01 NaN \n", - "1 1 Toy Story (1995) 1995-01-01 NaN \n", - "2 1 Toy Story (1995) 1995-01-01 NaN \n", - "3 1 Toy Story (1995) 1995-01-01 NaN \n", - "4 1 Toy Story (1995) 1995-01-01 NaN \n", - "\n", - " imdb_url release_year user_id \\\n", - "0 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 308 \n", - "1 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 287 \n", - "2 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 148 \n", - "3 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 280 \n", - "4 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 66 \n", - "\n", - " rating timestamp \n", - "0 4 887736532 \n", - "1 5 875334088 \n", - "2 4 877019411 \n", - "3 4 891700426 \n", - "4 3 883601324 " - ], + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"all_ratings\",\n \"rows\": 100000,\n \"fields\": [\n {\n \"column\": \"item_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 330,\n \"min\": 1,\n \"max\": 1682,\n \"num_unique_values\": 1682,\n \"samples\": [\n 1394,\n 744,\n 1606\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1664,\n \"samples\": [\n \"Madame Butterfly (1995)\",\n \"Wrong Trousers, The (1993)\",\n \"Breaking the Waves (1996)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release_date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"1922-01-01 00:00:00\",\n \"max\": \"1998-10-23 00:00:00\",\n \"num_unique_values\": 240,\n \"samples\": [\n \"1996-01-01 00:00:00\",\n \"1996-02-05 00:00:00\",\n \"1997-08-22 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"video_release_date\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"imdb_url\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1660,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release_year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.155522633276282,\n \"min\": 1922.0,\n \"max\": 1998.0,\n \"num_unique_values\": 71,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"user_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 266,\n \"min\": 1,\n \"max\": 943,\n \"num_unique_values\": 943,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"timestamp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5343856,\n \"min\": 874724710,\n \"max\": 893286638,\n \"num_unique_values\": 49282,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "all_ratings" + }, "text/html": [ "\n", "
\n", @@ -1351,14 +1333,32 @@ "
\n", " \n" ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "all_ratings", - "summary": "{\n \"name\": \"all_ratings\",\n \"rows\": 100000,\n \"fields\": [\n {\n \"column\": \"item_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 330,\n \"min\": 1,\n \"max\": 1682,\n \"num_unique_values\": 1682,\n \"samples\": [\n 1394,\n 744,\n 1606\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1664,\n \"samples\": [\n \"Madame Butterfly (1995)\",\n \"Wrong Trousers, The (1993)\",\n \"Breaking the Waves (1996)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release_date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"1922-01-01 00:00:00\",\n \"max\": \"1998-10-23 00:00:00\",\n \"num_unique_values\": 240,\n \"samples\": [\n \"1996-01-01 00:00:00\",\n \"1996-02-05 00:00:00\",\n \"1997-08-22 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"video_release_date\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"imdb_url\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1660,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release_year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.155522633276282,\n \"min\": 1922.0,\n \"max\": 1998.0,\n \"num_unique_values\": 71,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"user_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 266,\n \"min\": 1,\n \"max\": 943,\n \"num_unique_values\": 943,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"timestamp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5343856,\n \"min\": 874724710,\n \"max\": 893286638,\n \"num_unique_values\": 49282,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } + "text/plain": [ + " item_id title release_date video_release_date \\\n", + "0 1 Toy Story (1995) 1995-01-01 NaN \n", + "1 1 Toy Story (1995) 1995-01-01 NaN \n", + "2 1 Toy Story (1995) 1995-01-01 NaN \n", + "3 1 Toy Story (1995) 1995-01-01 NaN \n", + "4 1 Toy Story (1995) 1995-01-01 NaN \n", + "\n", + " imdb_url release_year user_id \\\n", + "0 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 308 \n", + "1 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 287 \n", + "2 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 148 \n", + "3 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 280 \n", + "4 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 66 \n", + "\n", + " rating timestamp \n", + "0 4 887736532 \n", + "1 5 875334088 \n", + "2 4 877019411 \n", + "3 4 891700426 \n", + "4 3 883601324 " + ] }, + "execution_count": 7, "metadata": {}, - "execution_count": 7 + "output_type": "execute_result" } ], "source": [ @@ -1389,29 +1389,11 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": [ - " item_id release_date video_release_date \\\n", - "count 100000.000000 99991 0.0 \n", - "mean 425.530130 1988-02-09 00:43:11.369223296 NaN \n", - "min 1.000000 1922-01-01 00:00:00 NaN \n", - "25% 175.000000 1986-01-01 00:00:00 NaN \n", - "50% 322.000000 1994-01-01 00:00:00 NaN \n", - "75% 631.000000 1996-09-28 00:00:00 NaN \n", - "max 1682.000000 1998-10-23 00:00:00 NaN \n", - "std 330.798356 NaN NaN \n", - "\n", - " release_year user_id rating timestamp \n", - "count 99991.000000 100000.00000 100000.000000 1.000000e+05 \n", - "mean 1987.956216 462.48475 3.529860 8.835289e+08 \n", - "min 1922.000000 1.00000 1.000000 8.747247e+08 \n", - "25% 1986.000000 254.00000 3.000000 8.794487e+08 \n", - "50% 1994.000000 447.00000 4.000000 8.828269e+08 \n", - "75% 1996.000000 682.00000 4.000000 8.882600e+08 \n", - "max 1998.000000 943.00000 5.000000 8.932866e+08 \n", - "std 14.155523 266.61442 1.125674 5.343856e+06 " - ], + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"all_ratings\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"item_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 35178.88603210694,\n \"min\": 1.0,\n \"max\": 100000.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 425.53013,\n 631.0,\n 100000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release_date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"1922-01-01 00:00:00\",\n \"max\": \"1998-10-23 00:00:00\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"99991\",\n \"1988-02-09 00:43:11.369223296\",\n \"1996-09-28 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"video_release_date\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release_year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 34758.03282611,\n \"min\": 14.155522633276282,\n \"max\": 99991.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 1987.9562160594453\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"user_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 35202.138180539565,\n \"min\": 1.0,\n \"max\": 100000.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 462.48475\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 35354.245317453555,\n \"min\": 1.0,\n \"max\": 100000.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 100000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"timestamp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 407843582.9913888,\n \"min\": 100000.0,\n \"max\": 893286638.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 883528851.48862\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe" + }, "text/html": [ "\n", "
\n", @@ -1736,13 +1718,31 @@ "
\n", " \n" ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "summary": "{\n \"name\": \"all_ratings\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"item_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 35178.88603210694,\n \"min\": 1.0,\n \"max\": 100000.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 425.53013,\n 631.0,\n 100000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release_date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"1922-01-01 00:00:00\",\n \"max\": \"1998-10-23 00:00:00\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"99991\",\n \"1988-02-09 00:43:11.369223296\",\n \"1996-09-28 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"video_release_date\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release_year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 34758.03282611,\n \"min\": 14.155522633276282,\n \"max\": 99991.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 1987.9562160594453\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"user_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 35202.138180539565,\n \"min\": 1.0,\n \"max\": 100000.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 462.48475\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 35354.245317453555,\n \"min\": 1.0,\n \"max\": 100000.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 100000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"timestamp\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 407843582.9913888,\n \"min\": 100000.0,\n \"max\": 893286638.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 883528851.48862\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } + "text/plain": [ + " item_id release_date video_release_date \\\n", + "count 100000.000000 99991 0.0 \n", + "mean 425.530130 1988-02-09 00:43:11.369223296 NaN \n", + "min 1.000000 1922-01-01 00:00:00 NaN \n", + "25% 175.000000 1986-01-01 00:00:00 NaN \n", + "50% 322.000000 1994-01-01 00:00:00 NaN \n", + "75% 631.000000 1996-09-28 00:00:00 NaN \n", + "max 1682.000000 1998-10-23 00:00:00 NaN \n", + "std 330.798356 NaN NaN \n", + "\n", + " release_year user_id rating timestamp \n", + "count 99991.000000 100000.00000 100000.000000 1.000000e+05 \n", + "mean 1987.956216 462.48475 3.529860 8.835289e+08 \n", + "min 1922.000000 1.00000 1.000000 8.747247e+08 \n", + "25% 1986.000000 254.00000 3.000000 8.794487e+08 \n", + "50% 1994.000000 447.00000 4.000000 8.828269e+08 \n", + "75% 1996.000000 682.00000 4.000000 8.882600e+08 \n", + "max 1998.000000 943.00000 5.000000 8.932866e+08 \n", + "std 14.155523 266.61442 1.125674 5.343856e+06 " + ] }, + "execution_count": 8, "metadata": {}, - "execution_count": 8 + "output_type": "execute_result" } ], "source": [ @@ -1783,14 +1783,14 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj8AAAGdCAYAAAD9kBJPAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAALqdJREFUeJzt3X1wVFWexvEnIenIW3cIkDRZEmBGFCLgC2joAXdXyRAhuirRVQckakpXJiAQRcgOgqJjGFhRUSGjq4ClDCO74igMYAgKKuEtgvKiARUNmnTCiEkDM+T17B8Wd6cljtB00knu91N1q+hzTt/7u6dS9uPpe2+HGWOMAAAAbCI81AUAAAA0J8IPAACwFcIPAACwFcIPAACwFcIPAACwFcIPAACwFcIPAACwFcIPAACwlYhQF9ASNDQ0qLS0VJ07d1ZYWFioywEAAGfAGKNjx44pPj5e4eFnvp5D+JFUWlqqhISEUJcBAAACcPjwYfXs2fOMxxN+JHXu3FnS95PndDpDXA0AADgTPp9PCQkJ1uf4mSL8SNZXXU6nk/ADAEArc7aXrHDBMwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsJWIUBfQ1vWesSbg9345Ny2IlQAAAImVHwAAYDOEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCshDT+9e/dWWFjYaVtWVpYk6eTJk8rKylLXrl3VqVMnpaenq7y83G8fJSUlSktLU4cOHRQbG6tp06aprq4uFKcDAABagZCGnx07dqisrMza8vPzJUk333yzJGnq1Kl66623tHLlSm3atEmlpaUaM2aM9f76+nqlpaWppqZGW7Zs0bJly7R06VLNmjUrJOcDAABavjBjjAl1EadMmTJFq1ev1sGDB+Xz+dS9e3ctX75cN910kyTp008/Vf/+/VVYWKihQ4dq7dq1uvbaa1VaWqq4uDhJUl5enqZPn64jR47I4XCc0XF9Pp9cLpeqqqrkdDqDek69Z6wJ+L1fzk0LYiUAALQtgX5+t5hrfmpqavTKK6/orrvuUlhYmIqKilRbW6uUlBRrTL9+/ZSYmKjCwkJJUmFhoQYOHGgFH0lKTU2Vz+fTvn37fvRY1dXV8vl8fhsAALCHFhN+3njjDVVWVuqOO+6QJHm9XjkcDkVHR/uNi4uLk9frtcb8ffA51X+q78fk5ubK5XJZW0JCQvBOBAAAtGgtJvy8+OKLGjVqlOLj45v8WDk5OaqqqrK2w4cPN/kxAQBAyxAR6gIk6auvvtKGDRv0+uuvW21ut1s1NTWqrKz0W/0pLy+X2+22xmzfvt1vX6fuBjs1pjFRUVGKiooK4hkAAIDWokWs/CxZskSxsbFKS/v/C3wHDx6syMhIFRQUWG3FxcUqKSmRx+ORJHk8Hu3Zs0cVFRXWmPz8fDmdTiUlJTXfCQAAgFYj5Cs/DQ0NWrJkiTIyMhQR8f/luFwuZWZmKjs7WzExMXI6nZo0aZI8Ho+GDh0qSRo5cqSSkpJ0++23a968efJ6vZo5c6aysrJY2QEAAI0KefjZsGGDSkpKdNddd53W9+STTyo8PFzp6emqrq5WamqqFi1aZPW3a9dOq1ev1oQJE+TxeNSxY0dlZGRozpw5zXkKAACgFWlRz/kJFZ7zAwBA69Pqn/MDAADQHAg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVgg/AADAVkIefr755huNGzdOXbt2Vfv27TVw4EDt3LnT6jfGaNasWerRo4fat2+vlJQUHTx40G8fR48e1dixY+V0OhUdHa3MzEwdP368uU8FAAC0AiENP999952GDRumyMhIrV27Vvv379cTTzyhLl26WGPmzZunhQsXKi8vT9u2bVPHjh2VmpqqkydPWmPGjh2rffv2KT8/X6tXr9bmzZt1zz33hOKUAABACxdmjDGhOviMGTP0wQcf6L333mu03xij+Ph43X///XrggQckSVVVVYqLi9PSpUt166236pNPPlFSUpJ27NihIUOGSJLWrVun0aNH6+uvv1Z8fPxP1uHz+eRyuVRVVSWn0xm8E5TUe8aagN/75dy0IFYCAEDbEujnd0hXft58800NGTJEN998s2JjY3XppZfqhRdesPoPHTokr9erlJQUq83lcik5OVmFhYWSpMLCQkVHR1vBR5JSUlIUHh6ubdu2Nd/JAACAViGk4eeLL77Q4sWL1bdvX61fv14TJkzQfffdp2XLlkmSvF6vJCkuLs7vfXFxcVaf1+tVbGysX39ERIRiYmKsMT9UXV0tn8/ntwEAAHuICOXBGxoaNGTIED3++OOSpEsvvVR79+5VXl6eMjIymuy4ubm5euSRR5ps/wAAoOUK6cpPjx49lJSU5NfWv39/lZSUSJLcbrckqby83G9MeXm51ed2u1VRUeHXX1dXp6NHj1pjfignJ0dVVVXWdvjw4aCcDwAAaPlCGn6GDRum4uJiv7YDBw6oV69ekqQ+ffrI7XaroKDA6vf5fNq2bZs8Ho8kyePxqLKyUkVFRdaYjRs3qqGhQcnJyY0eNyoqSk6n028DAAD2ENKvvaZOnapf/OIXevzxx/Xv//7v2r59u55//nk9//zzkqSwsDBNmTJFjz32mPr27as+ffrooYceUnx8vG644QZJ368UXXPNNbr77ruVl5en2tpaTZw4UbfeeusZ3ekFAADsJaTh5/LLL9eqVauUk5OjOXPmqE+fPnrqqac0duxYa8yDDz6oEydO6J577lFlZaWGDx+udevW6bzzzrPGvPrqq5o4caJGjBih8PBwpaena+HChaE4JQAA0MKF9Dk/LQXP+QEAoPVplc/5AQAAaG6EHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCuEHwAAYCshDT8PP/ywwsLC/LZ+/fpZ/SdPnlRWVpa6du2qTp06KT09XeXl5X77KCkpUVpamjp06KDY2FhNmzZNdXV1zX0qAACglYgIdQEXXXSRNmzYYL2OiPj/kqZOnao1a9Zo5cqVcrlcmjhxosaMGaMPPvhAklRfX6+0tDS53W5t2bJFZWVlGj9+vCIjI/X44483+7kAAICWL+ThJyIiQm63+7T2qqoqvfjii1q+fLmuvvpqSdKSJUvUv39/bd26VUOHDtXbb7+t/fv3a8OGDYqLi9Mll1yiRx99VNOnT9fDDz8sh8PR3KcDAABauJBf83Pw4EHFx8frZz/7mcaOHauSkhJJUlFRkWpra5WSkmKN7devnxITE1VYWChJKiws1MCBAxUXF2eNSU1Nlc/n0759+370mNXV1fL5fH4bAACwh5CGn+TkZC1dulTr1q3T4sWLdejQIV155ZU6duyYvF6vHA6HoqOj/d4TFxcnr9crSfJ6vX7B51T/qb4fk5ubK5fLZW0JCQnBPTEAANBihfRrr1GjRln/HjRokJKTk9WrVy+99tprat++fZMdNycnR9nZ2dZrn89HAAIAwCZC/rXX34uOjtYFF1ygzz77TG63WzU1NaqsrPQbU15ebl0j5Ha7T7v769Trxq4jOiUqKkpOp9NvAwAA9tCiws/x48f1+eefq0ePHho8eLAiIyNVUFBg9RcXF6ukpEQej0eS5PF4tGfPHlVUVFhj8vPz5XQ6lZSU1Oz1AwCAli+kX3s98MADuu6669SrVy+VlpZq9uzZateunW677Ta5XC5lZmYqOztbMTExcjqdmjRpkjwej4YOHSpJGjlypJKSknT77bdr3rx58nq9mjlzprKyshQVFRXKUwMAAC1USMPP119/rdtuu03ffvutunfvruHDh2vr1q3q3r27JOnJJ59UeHi40tPTVV1drdTUVC1atMh6f7t27bR69WpNmDBBHo9HHTt2VEZGhubMmROqUwIAAC1cmDHGhLqIUPP5fHK5XKqqqgr69T+9Z6wJ+L1fzk0LYiUAALQtgX5+t6hrfgAAAJoa4QcAANgK4QcAANgK4QcAANgK4QcAANgK4QcAANgK4QcAANgK4QcAANgK4QcAANhKQOHniy++CHYdAAAAzSKg8HP++efrqquu0iuvvKKTJ08GuyYAAIAmE1D4+fDDDzVo0CBlZ2fL7XbrP/7jP7R9+/Zg1wYAABB0AYWfSy65RE8//bRKS0v10ksvqaysTMOHD9eAAQO0YMECHTlyJNh1AgAABMU5XfAcERGhMWPGaOXKlfrd736nzz77TA888IASEhI0fvx4lZWVBatOAACAoDin8LNz5079+te/Vo8ePbRgwQI98MAD+vzzz5Wfn6/S0lJdf/31waoTAAAgKCICedOCBQu0ZMkSFRcXa/To0Xr55Zc1evRohYd/n6X69OmjpUuXqnfv3sGsFQAA4JwFFH4WL16su+66S3fccYd69OjR6JjY2Fi9+OKL51QcAABAsAUUfg4ePPiTYxwOhzIyMgLZPQAAQJMJ6JqfJUuWaOXKlae1r1y5UsuWLTvnogAAAJpKQOEnNzdX3bp1O609NjZWjz/++DkXBQAA0FQCCj8lJSXq06fPae29evVSSUnJORcFAADQVAIKP7Gxsfr4449Pa//oo4/UtWvXcy4KAACgqQQUfm677Tbdd999euedd1RfX6/6+npt3LhRkydP1q233hrsGgEAAIImoLu9Hn30UX355ZcaMWKEIiK+30VDQ4PGjx/PNT8AAKBFCyj8OBwO/fGPf9Sjjz6qjz76SO3bt9fAgQPVq1evYNcHAAAQVAGFn1MuuOACXXDBBcGqBQAAoMkFFH7q6+u1dOlSFRQUqKKiQg0NDX79GzduDEpxAAAAwRZQ+Jk8ebKWLl2qtLQ0DRgwQGFhYcGuCwAAoEkEFH5WrFih1157TaNHjw52PQAAAE0qoFvdHQ6Hzj///GDXAgAA0OQCCj/333+/nn76aRljgl0PAABAkwroa6/3339f77zzjtauXauLLrpIkZGRfv2vv/56UIoDAAAItoDCT3R0tG688cZg1wIAANDkAgo/S5YsCXYdAAAAzSKga34kqa6uThs2bNDvf/97HTt2TJJUWlqq48ePB604AACAYAto5eerr77SNddco5KSElVXV+uXv/ylOnfurN/97neqrq5WXl5esOsEAAAIioBWfiZPnqwhQ4bou+++U/v27a32G2+8UQUFBQEVMnfuXIWFhWnKlClW28mTJ5WVlaWuXbuqU6dOSk9PV3l5ud/7SkpKlJaWpg4dOig2NlbTpk1TXV1dQDUAAIC2L6CVn/fee09btmyRw+Hwa+/du7e++eabs97fjh079Pvf/16DBg3ya586darWrFmjlStXyuVyaeLEiRozZow++OADSd//zEZaWprcbre2bNmisrIyjR8/XpGRkfy6PAAAaFRAKz8NDQ2qr68/rf3rr79W586dz2pfx48f19ixY/XCCy+oS5cuVntVVZVefPFFLViwQFdffbUGDx6sJUuWaMuWLdq6dask6e2339b+/fv1yiuv6JJLLtGoUaP06KOP6rnnnlNNTU0gpwYAANq4gMLPyJEj9dRTT1mvw8LCdPz4cc2ePfusf/IiKytLaWlpSklJ8WsvKipSbW2tX3u/fv2UmJiowsJCSVJhYaEGDhyouLg4a0xqaqp8Pp/27dv3o8esrq6Wz+fz2wAAgD0E9LXXE088odTUVCUlJenkyZP61a9+pYMHD6pbt276wx/+cMb7WbFihT788EPt2LHjtD6v1yuHw6Ho6Gi/9ri4OHm9XmvM3wefU/2n+n5Mbm6uHnnkkTOuEwAAtB0BhZ+ePXvqo48+0ooVK/Txxx/r+PHjyszM1NixY/0ugP5HDh8+rMmTJys/P1/nnXdeIGUELCcnR9nZ2dZrn8+nhISEZq0BAACERkDhR5IiIiI0bty4gA9cVFSkiooKXXbZZVZbfX29Nm/erGeffVbr169XTU2NKisr/VZ/ysvL5Xa7JUlut1vbt2/32++pu8FOjWlMVFSUoqKiAq4dAAC0XgGFn5dffvkf9o8fP/4n9zFixAjt2bPHr+3OO+9Uv379NH36dCUkJCgyMlIFBQVKT0+XJBUXF6ukpEQej0eS5PF49Nvf/lYVFRWKjY2VJOXn58vpdCopKSmQUwMAAG1cQOFn8uTJfq9ra2v117/+VQ6HQx06dDij8NO5c2cNGDDAr61jx47q2rWr1Z6Zmans7GzFxMTI6XRq0qRJ8ng8Gjp0qKTvL7xOSkrS7bffrnnz5snr9WrmzJnKyspiZQcAADQqoPDz3XffndZ28OBBTZgwQdOmTTvnok558sknFR4ervT0dFVXVys1NVWLFi2y+tu1a6fVq1drwoQJ8ng86tixozIyMjRnzpyg1QAAANqWMGOMCdbOdu7cqXHjxunTTz8N1i6bhc/nk8vlUlVVlZxOZ1D33XvGmoDf++XctCBWAgBA2xLo53fAP2zamIiICJWWlgZzlwAAAEEV0Ndeb775pt9rY4zKysr07LPPatiwYUEpDAAAoCkEFH5uuOEGv9dhYWHq3r27rr76aj3xxBPBqAsAAKBJBBR+Ghoagl0HAABAswjqNT8AAAAtXUArP3//0xA/ZcGCBYEcAgAAoEkEFH527dqlXbt2qba2VhdeeKEk6cCBA2rXrp3fz1WEhYUFp0oAAIAgCSj8XHfddercubOWLVumLl26SPr+wYd33nmnrrzySt1///1BLRIAACBYArrm54knnlBubq4VfCSpS5cueuyxx7jbCwAAtGgBhR+fz6cjR46c1n7kyBEdO3bsnIsCAABoKgGFnxtvvFF33nmnXn/9dX399df6+uuv9b//+7/KzMzUmDFjgl0jAABA0AR0zU9eXp4eeOAB/epXv1Jtbe33O4qIUGZmpubPnx/UAgEAAIIpoPDToUMHLVq0SPPnz9fnn38uSfr5z3+ujh07BrU4AACAYDunhxyWlZWprKxMffv2VceOHRXEH4gHAABoEgGFn2+//VYjRozQBRdcoNGjR6usrEySlJmZyW3uAACgRQso/EydOlWRkZEqKSlRhw4drPZbbrlF69atC1pxAAAAwRbQNT9vv/221q9fr549e/q19+3bV1999VVQCgMAAGgKAa38nDhxwm/F55SjR48qKirqnIsCAABoKgGFnyuvvFIvv/yy9TosLEwNDQ2aN2+errrqqqAVBwAAEGwBfe01b948jRgxQjt37lRNTY0efPBB7du3T0ePHtUHH3wQ7BoBAACCJqCVnwEDBujAgQMaPny4rr/+ep04cUJjxozRrl279POf/zzYNQIAAATNWa/81NbW6pprrlFeXp5+85vfNEVNAAAATeasV34iIyP18ccfN0UtAAAATS6gr73GjRunF198Mdi1AAAANLmALniuq6vTSy+9pA0bNmjw4MGn/abXggULglIcAABAsJ1V+Pniiy/Uu3dv7d27V5dddpkk6cCBA35jwsLCglcdAABAkJ1V+Onbt6/Kysr0zjvvSPr+5ywWLlyouLi4JikOAAAg2M7qmp8f/mr72rVrdeLEiaAWBAAA0JQCuuD5lB+GIQAAgJburMJPWFjYadf0cI0PAABoTc7qmh9jjO644w7rx0tPnjype++997S7vV5//fXgVQgAABBEZxV+MjIy/F6PGzcuqMUAAAA0tbMKP0uWLGmqOgAAAJrFOV3wDAAA0NoQfgAAgK2ENPwsXrxYgwYNktPplNPplMfj0dq1a63+kydPKisrS127dlWnTp2Unp6u8vJyv32UlJQoLS1NHTp0UGxsrKZNm6a6urrmPhUAANBKhDT89OzZU3PnzlVRUZF27typq6++Wtdff7327dsnSZo6dareeustrVy5Ups2bVJpaanGjBljvb++vl5paWmqqanRli1btGzZMi1dulSzZs0K1SkBAIAWLsy0sCcVxsTEaP78+brpppvUvXt3LV++XDfddJMk6dNPP1X//v1VWFiooUOHau3atbr22mtVWlpq/cRGXl6epk+friNHjsjhcJzRMX0+n1wul6qqquR0OoN6Pr1nrAn4vV/OTQtiJQAAtC2Bfn63mGt+6uvrtWLFCp04cUIej0dFRUWqra1VSkqKNaZfv35KTExUYWGhJKmwsFADBw70+22x1NRU+Xw+a/WoMdXV1fL5fH4bAACwh5CHnz179qhTp06KiorSvffeq1WrVikpKUler1cOh0PR0dF+4+Pi4uT1eiVJXq/3tB9VPfX61JjG5ObmyuVyWVtCQkJwTwoAALRYIQ8/F154oXbv3q1t27ZpwoQJysjI0P79+5v0mDk5OaqqqrK2w4cPN+nxAABAy3FWDzlsCg6HQ+eff74kafDgwdqxY4eefvpp3XLLLaqpqVFlZaXf6k95ebncbrckye12a/v27X77O3U32KkxjYmKirJ+ogMAANhLyFd+fqihoUHV1dUaPHiwIiMjVVBQYPUVFxerpKREHo9HkuTxeLRnzx5VVFRYY/Lz8+V0OpWUlNTstQMAgJYvpCs/OTk5GjVqlBITE3Xs2DEtX75c7777rtavXy+Xy6XMzExlZ2crJiZGTqdTkyZNksfj0dChQyVJI0eOVFJSkm6//XbNmzdPXq9XM2fOVFZWFis7AACgUSENPxUVFRo/frzKysrkcrk0aNAgrV+/Xr/85S8lSU8++aTCw8OVnp6u6upqpaamatGiRdb727Vrp9WrV2vChAnyeDzq2LGjMjIyNGfOnFCdEgAAaOFa3HN+QoHn/AAA0Pq0+uf8AAAANAfCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsBXCDwAAsJWIUBeAH9d7xpqA3/vl3LQgVgIAQNvByg8AALAVwg8AALAVwg8AALAVwg8AALCVkIaf3NxcXX755ercubNiY2N1ww03qLi42G/MyZMnlZWVpa5du6pTp05KT09XeXm535iSkhKlpaWpQ4cOio2N1bRp01RXV9ecpwIAAFqJkIafTZs2KSsrS1u3blV+fr5qa2s1cuRInThxwhozdepUvfXWW1q5cqU2bdqk0tJSjRkzxuqvr69XWlqaampqtGXLFi1btkxLly7VrFmzQnFKAACghQszxphQF3HKkSNHFBsbq02bNumf//mfVVVVpe7du2v58uW66aabJEmffvqp+vfvr8LCQg0dOlRr167Vtddeq9LSUsXFxUmS8vLyNH36dB05ckQOh+Mnj+vz+eRyuVRVVSWn0xnUczqX29XPBbe6AwDaukA/v1vUNT9VVVWSpJiYGElSUVGRamtrlZKSYo3p16+fEhMTVVhYKEkqLCzUwIEDreAjSampqfL5fNq3b1+jx6murpbP5/PbAACAPbSY8NPQ0KApU6Zo2LBhGjBggCTJ6/XK4XAoOjrab2xcXJy8Xq815u+Dz6n+U32Nyc3NlcvlsraEhIQgnw0AAGipWkz4ycrK0t69e7VixYomP1ZOTo6qqqqs7fDhw01+TAAA0DK0iJ+3mDhxolavXq3NmzerZ8+eVrvb7VZNTY0qKyv9Vn/Ky8vldrutMdu3b/fb36m7wU6N+aGoqChFRUUF+SwAAEBrENKVH2OMJk6cqFWrVmnjxo3q06ePX//gwYMVGRmpgoICq624uFglJSXyeDySJI/Hoz179qiiosIak5+fL6fTqaSkpOY5EQAA0GqEdOUnKytLy5cv15/+9Cd17tzZukbH5XKpffv2crlcyszMVHZ2tmJiYuR0OjVp0iR5PB4NHTpUkjRy5EglJSXp9ttv17x58+T1ejVz5kxlZWWxugMAAE4T0vCzePFiSdK//uu/+rUvWbJEd9xxhyTpySefVHh4uNLT01VdXa3U1FQtWrTIGtuuXTutXr1aEyZMkMfjUceOHZWRkaE5c+Y012kAAIBWpEU95ydUeM4PAACtT5t4zg8AAEBTI/wAAABbIfwAAABbIfwAAABbIfwAAABbIfwAAABbaRE/b4HgO5db7LlNHgDQlrHyAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbIXwAwAAbCUilAffvHmz5s+fr6KiIpWVlWnVqlW64YYbrH5jjGbPnq0XXnhBlZWVGjZsmBYvXqy+fftaY44ePapJkybprbfeUnh4uNLT0/X000+rU6dOITijtqH3jDUBv/fLuWlBrAQAgOAL6crPiRMndPHFF+u5555rtH/evHlauHCh8vLytG3bNnXs2FGpqak6efKkNWbs2LHat2+f8vPztXr1am3evFn33HNPc50CAABoZUK68jNq1CiNGjWq0T5jjJ566inNnDlT119/vSTp5ZdfVlxcnN544w3deuut+uSTT7Ru3Trt2LFDQ4YMkSQ988wzGj16tP7rv/5L8fHxzXYuAACgdWix1/wcOnRIXq9XKSkpVpvL5VJycrIKCwslSYWFhYqOjraCjySlpKQoPDxc27Zta/aaAQBAyxfSlZ9/xOv1SpLi4uL82uPi4qw+r9er2NhYv/6IiAjFxMRYYxpTXV2t6upq67XP5wtW2QAAoIVrsSs/TSk3N1cul8vaEhISQl0SAABoJi02/LjdbklSeXm5X3t5ebnV53a7VVFR4ddfV1eno0ePWmMak5OTo6qqKms7fPhwkKsHAAAtVYsNP3369JHb7VZBQYHV5vP5tG3bNnk8HkmSx+NRZWWlioqKrDEbN25UQ0ODkpOTf3TfUVFRcjqdfhsAALCHkF7zc/z4cX322WfW60OHDmn37t2KiYlRYmKipkyZoscee0x9+/ZVnz599NBDDyk+Pt56FlD//v11zTXX6O6771ZeXp5qa2s1ceJE3XrrrdzpBQAAGhXS8LNz505dddVV1uvs7GxJUkZGhpYuXaoHH3xQJ06c0D333KPKykoNHz5c69at03nnnWe959VXX9XEiRM1YsQI6yGHCxcubPZzAQAArUOYMcaEuohQ8/l8crlcqqqqCvpXYOfytOTWiCc8AwCaS6Cf3y32mh8AAICm0GKf84PWid8FAwC0dKz8AAAAWyH8AAAAWyH8AAAAWyH8AAAAWyH8AAAAWyH8AAAAWyH8AAAAWyH8AAAAWyH8AAAAWyH8AAAAWyH8AAAAW+G3vdAm8JtiAIAzxcoPAACwFVZ+0GKcy+oNAABnipUfAABgK4QfAABgK4QfAABgK4QfAABgK1zwDJwDbrEHgNaHlR8AAGArhB8AAGArfO0F2+P5QgBgL6z8AAAAWyH8AAAAWyH8AAAAWyH8AAAAWyH8AAAAW+FuLyBEzvUuMx6SCACBIfwANsSTqQHYGeEHaKV4PhEABIbwA+CssGoEoLXjgmcAAGArrPwAaPNYrQLw9wg/AJoNIQRAS9Bmws9zzz2n+fPny+v16uKLL9YzzzyjK664ItRlAQgSLvAGECxtIvz88Y9/VHZ2tvLy8pScnKynnnpKqampKi4uVmxsbKjLA9CKsVoFtD1hxhgT6iLOVXJysi6//HI9++yzkqSGhgYlJCRo0qRJmjFjxk++3+fzyeVyqaqqSk6nM6i18X+rAAJBcAJ+WqCf361+5aempkZFRUXKycmx2sLDw5WSkqLCwsJG31NdXa3q6mrrdVVVlaTvJzHYGqr/GvR9Amj7EqeuDHUJrcbeR1IDfu+A2ettddy25tTn9tmu47T68POXv/xF9fX1iouL82uPi4vTp59+2uh7cnNz9cgjj5zWnpCQ0CQ1AgCajuspjmt3x44dk8vlOuPxrT78BCInJ0fZ2dnW64aGBh09elRdu3ZVWFhYUI7h8/mUkJCgw4cPB/2rtLaMeTt7zFlgmLezx5wFhnk7e2c6Z8YYHTt2TPHx8We1/1Yffrp166Z27dqpvLzcr728vFxut7vR90RFRSkqKsqvLTo6uknqczqd/LEHgHk7e8xZYJi3s8ecBYZ5O3tnMmdns+JzSqt/wrPD4dDgwYNVUFBgtTU0NKigoEAejyeElQEAgJao1a/8SFJ2drYyMjI0ZMgQXXHFFXrqqad04sQJ3XnnnaEuDQAAtDBtIvzccsstOnLkiGbNmiWv16tLLrlE69atO+0i6OYUFRWl2bNnn/b1Gv4x5u3sMWeBYd7OHnMWGObt7DX1nLWJ5/wAAACcqVZ/zQ8AAMDZIPwAAABbIfwAAABbIfwAAABbIfw0keeee069e/fWeeedp+TkZG3fvj3UJYXM5s2bdd111yk+Pl5hYWF64403/PqNMZo1a5Z69Oih9u3bKyUlRQcPHvQbc/ToUY0dO1ZOp1PR0dHKzMzU8ePHm/Esmldubq4uv/xyde7cWbGxsbrhhhtUXFzsN+bkyZPKyspS165d1alTJ6Wnp5/2sM+SkhKlpaWpQ4cOio2N1bRp01RXV9ecp9KsFi9erEGDBlkPRvN4PFq7dq3Vz5z9tLlz5yosLExTpkyx2pi30z388MMKCwvz2/r162f1M2eN++abbzRu3Dh17dpV7du318CBA7Vz506rv9k+DwyCbsWKFcbhcJiXXnrJ7Nu3z9x9990mOjralJeXh7q0kPjzn/9sfvOb35jXX3/dSDKrVq3y6587d65xuVzmjTfeMB999JH5t3/7N9OnTx/zt7/9zRpzzTXXmIsvvths3brVvPfee+b88883t912WzOfSfNJTU01S5YsMXv37jW7d+82o0ePNomJieb48ePWmHvvvdckJCSYgoICs3PnTjN06FDzi1/8wuqvq6szAwYMMCkpKWbXrl3mz3/+s+nWrZvJyckJxSk1izfffNOsWbPGHDhwwBQXF5v//M//NJGRkWbv3r3GGObsp2zfvt307t3bDBo0yEyePNlqZ95ON3v2bHPRRReZsrIyazty5IjVz5yd7ujRo6ZXr17mjjvuMNu2bTNffPGFWb9+vfnss8+sMc31eUD4aQJXXHGFycrKsl7X19eb+Ph4k5ubG8KqWoYfhp+GhgbjdrvN/PnzrbbKykoTFRVl/vCHPxhjjNm/f7+RZHbs2GGNWbt2rQkLCzPffPNNs9UeShUVFUaS2bRpkzHm+zmKjIw0K1eutMZ88sknRpIpLCw0xnwfOsPDw43X67XGLF682DidTlNdXd28JxBCXbp0Mf/93//NnP2EY8eOmb59+5r8/HzzL//yL1b4Yd4aN3v2bHPxxRc32secNW769Olm+PDhP9rfnJ8HfO0VZDU1NSoqKlJKSorVFh4erpSUFBUWFoawspbp0KFD8nq9fvPlcrmUnJxszVdhYaGio6M1ZMgQa0xKSorCw8O1bdu2Zq85FKqqqiRJMTExkqSioiLV1tb6zVu/fv2UmJjoN28DBw70e9hnamqqfD6f9u3b14zVh0Z9fb1WrFihEydOyOPxMGc/ISsrS2lpaX7zI/G39o8cPHhQ8fHx+tnPfqaxY8eqpKREEnP2Y958800NGTJEN998s2JjY3XppZfqhRdesPqb8/OA8BNkf/nLX1RfX3/a06Xj4uLk9XpDVFXLdWpO/tF8eb1excbG+vVHREQoJibGFnPa0NCgKVOmaNiwYRowYICk7+fE4XCc9oO8P5y3xub1VF9btWfPHnXq1ElRUVG69957tWrVKiUlJTFn/8CKFSv04YcfKjc397Q+5q1xycnJWrp0qdatW6fFixfr0KFDuvLKK3Xs2DHm7Ed88cUXWrx4sfr27av169drwoQJuu+++7Rs2TJJzft50CZ+3gJoy7KysrR37169//77oS6lVbjwwgu1e/duVVVV6X/+53+UkZGhTZs2hbqsFuvw4cOaPHmy8vPzdd5554W6nFZj1KhR1r8HDRqk5ORk9erVS6+99prat28fwsparoaGBg0ZMkSPP/64JOnSSy/V3r17lZeXp4yMjGathZWfIOvWrZvatWt32lX95eXlcrvdIaqq5To1J/9ovtxutyoqKvz66+rqdPTo0TY/pxMnTtTq1av1zjvvqGfPnla72+1WTU2NKisr/cb/cN4am9dTfW2Vw+HQ+eefr8GDBys3N1cXX3yxnn76aebsRxQVFamiokKXXXaZIiIiFBERoU2bNmnhwoWKiIhQXFwc83YGoqOjdcEFF+izzz7jb+1H9OjRQ0lJSX5t/fv3t74ubM7PA8JPkDkcDg0ePFgFBQVWW0NDgwoKCuTxeEJYWcvUp08fud1uv/ny+Xzatm2bNV8ej0eVlZUqKiqyxmzcuFENDQ1KTk5u9pqbgzFGEydO1KpVq7Rx40b16dPHr3/w4MGKjIz0m7fi4mKVlJT4zduePXv8/kORn58vp9N52n+A2rKGhgZVV1czZz9ixIgR2rNnj3bv3m1tQ4YM0dixY61/M28/7fjx4/r888/Vo0cP/tZ+xLBhw057ZMeBAwfUq1cvSc38eXD212vjp6xYscJERUWZpUuXmv3795t77rnHREdH+13VbyfHjh0zu3btMrt27TKSzIIFC8yuXbvMV199ZYz5/tbG6Oho86c//cl8/PHH5vrrr2/01sZLL73UbNu2zbz//vumb9++bfpW9wkTJhiXy2Xeffddv1tp//rXv1pj7r33XpOYmGg2btxodu7caTwej/F4PFb/qVtpR44caXbv3m3WrVtnunfv3qZvpZ0xY4bZtGmTOXTokPn444/NjBkzTFhYmHn77beNMczZmfr7u72MYd4ac//995t3333XHDp0yHzwwQcmJSXFdOvWzVRUVBhjmLPGbN++3URERJjf/va35uDBg+bVV181HTp0MK+88oo1prk+Dwg/TeSZZ54xiYmJxuFwmCuuuMJs3bo11CWFzDvvvGMknbZlZGQYY76/vfGhhx4ycXFxJioqyowYMcIUFxf77ePbb781t912m+nUqZNxOp3mzjvvNMeOHQvB2TSPxuZLklmyZIk15m9/+5v59a9/bbp06WI6dOhgbrzxRlNWVua3ny+//NKMGjXKtG/f3nTr1s3cf//9pra2tpnPpvncddddplevXsbhcJju3bubESNGWMHHGObsTP0w/DBvp7vllltMjx49jMPhMP/0T/9kbrnlFr/n1TBnjXvrrbfMgAEDTFRUlOnXr595/vnn/fqb6/MgzBhjznLlCgAAoNXimh8AAGArhB8AAGArhB8AAGArhB8AAGArhB8AAGArhB8AAGArhB8AAGArhB8AAGArhB8AAGArhB8AAGArhB8AAGArhB8AAGAr/wf2y70sn+W1QwAAAABJRU5ErkJggg==", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1809,14 +1809,14 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "np.int64(141)" ] }, + "execution_count": 11, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" } ], "source": [ @@ -1836,21 +1836,7 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": [ - "49 Star Wars (1977)\n", - "257 Contact (1997)\n", - "99 Fargo (1996)\n", - "180 Return of the Jedi (1983)\n", - "293 Liar Liar (1997)\n", - "285 English Patient, The (1996)\n", - "287 Scream (1996)\n", - "0 Toy Story (1995)\n", - "299 Air Force One (1997)\n", - "120 Independence Day (ID4) (1996)\n", - "Name: title, dtype: object" - ], "text/html": [ "
\n", "