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374 changes: 374 additions & 0 deletions docs/user_guide/Getting_Started.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Install the required packages\n",
"[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Bergschaf/visualime_guide/blob/master/Get_Started.ipynb)\n"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"!pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu\n",
"!pip3 install numpy\n",
"!pip3 install matplotlib"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"# Import the required packages"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
"from torchvision import datasets, transforms"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"# Define the transformations to prepare the data"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"transform = transforms.Compose([transforms.ToTensor(),\n",
" transforms.Normalize((0.5,), (0.5,)),\n",
" ])"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"1. ```transforms.ToTensor()``` converts the image to a tensor\n",
"2. ```transforms.Normalize((0.5,), (0.5,))``` normalizes the image"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"# Download the dataset"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"!mkdir -p data\n",
"\n",
"testset = datasets.MNIST('data', download=True, train=False, transform=transform)\n",
"\n",
"testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"# Analyze the dataset"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"dataiter = iter(testloader)\n",
"images, labels = next(dataiter)\n",
"images, labels = next(dataiter)\n",
"\n",
"print(images.shape)\n",
"print(labels.shape)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"The batch size is 64 and the image size is 28x28 and the number of channels is 1 (grayscale)\n",
"The labels are the corresponding numbers for the images"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"# Download the model"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"!wget https://github.com/Bergschaf/visualime_guide/raw/master/models/mnist_model.pt\n",
"model = torch.load(\"mnist_model.pt\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"# Test the model on a single image"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"img = images[0]\n",
"img = img.view(1, 784)\n",
"with torch.no_grad():\n",
" logps = model(img)\n",
"\n",
"ps = torch.exp(logps)\n",
"probab = list(ps.numpy()[0])\n",
"print(\"Predicted Digit =\", probab.index(max(probab)))\n",
"\n",
"plt.imshow(img.resize_(1, 28, 28).numpy().squeeze(), cmap='Greys_r')"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"# Explain the classification with visualime"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Install and import visuallime"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"!pip3 install visualime\n",
"from visualime.explain import explain_classification, render_explanation"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Define helper Functions"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"def to_visualime(image: np.ndarray):\n",
" \"\"\"\n",
" Converts an image of the shape [1,28,28] to the shape [28,28,3]\n",
" \"\"\"\n",
" image = image.squeeze()\n",
" image = np.stack((image, image, image), axis=2)\n",
" return image"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"```to_visualime()``` converts the image to the shape [28,28,3] which is required by visualime"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"def predict(imgs: np.ndarray):\n",
" \"\"\"\n",
" :param image: visualime RGB image of the shape [num_samples, 28,28,3]\n",
" :return:\n",
" \"\"\"\n",
" imgs = imgs[:, :, :, 0]\n",
" predictions = np.zeros((imgs.shape[0], 10))\n",
" for i in range(imgs.shape[0]):\n",
" image = imgs[i]\n",
" # convert the image to a tensor\n",
" image = torch.from_numpy(image)\n",
" image = image.view(1, 784)\n",
" with torch.no_grad():\n",
" prediction = model(image)\n",
" predictions[i] = torch.exp(prediction).numpy()[0]\n",
" return predictions\n"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"```predict()``` takes an image of the shape [num_samples, 28,28,3] (an array of visualime images) and returns the predictions of the model\n",
"This is required to explain the classification with visualime"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Explain the classification"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"img = images[5] # Choose an image"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"print(\"The network predicts: \", np.argmax(predict(np.array([to_visualime(img)]))))\n",
"\n",
"segment_mask, segment_weights = explain_classification(image=to_visualime(img), predict_fn=predict, num_of_samples=512)\n",
"\n",
"explanation = render_explanation(\n",
" to_visualime(img),\n",
" segment_mask,\n",
" segment_weights,\n",
" positive=\"green\",\n",
" negative=\"red\",\n",
" coverage=0.5,\n",
" opacity=1,\n",
" )\n",
"\n",
"plt.imshow(explanation)\n"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
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"nbformat_minor": 0
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