From a64f014b7173457eeb13f1303171e9dd8392da71 Mon Sep 17 00:00:00 2001
From: 272burger <shuriforyou@gmail.com>
Date: Thu, 10 Mar 2022 00:11:11 +0900
Subject: [PATCH] DL develop assginment done

---
 .DS_Store                                     | Bin 6148 -> 8196 bytes
 ...] DLdevelop\352\263\274\354\240\234.ipynb" |   1 +
 RNN/.DS_Store                                 | Bin 0 -> 6148 bytes
 3 files changed, 1 insertion(+)
 create mode 100644 "DLdevelop/[6\352\270\260 \354\265\234\354\227\260\354\210\230] DLdevelop\352\263\274\354\240\234.ipynb"
 create mode 100644 RNN/.DS_Store

diff --git a/.DS_Store b/.DS_Store
index b90fc7772b7b4f9cdcc2b86c922439961d81c9f8..1c45605d0fd688345e4f49758ca30089a3127397 100644
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delta 112
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diff --git "a/DLdevelop/[6\352\270\260 \354\265\234\354\227\260\354\210\230] DLdevelop\352\263\274\354\240\234.ipynb" "b/DLdevelop/[6\352\270\260 \354\265\234\354\227\260\354\210\230] DLdevelop\352\263\274\354\240\234.ipynb"
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+++ "b/DLdevelop/[6\352\270\260 \354\265\234\354\227\260\354\210\230] DLdevelop\352\263\274\354\240\234.ipynb"	
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{"id":"xIqvhvTOM1m4"},"source":["DSL 5기 한영웅님의 CNN 1강 코드를 일부 수정하였음을 알려드립니다.\n","\n","(이 노트북은 최건호, 파이토치 첫걸음 , 한빛미디어 ,2019 를 참고하여 초기 제작 되었습니다)"]},{"cell_type":"markdown","metadata":{"id":"SEplwfV2eh2x"},"source":["## 1. 모듈 불러오기"]},{"cell_type":"code","execution_count":2,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T06:15:42.158515Z","start_time":"2022-02-22T06:15:38.021263Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"PUAoW-0PemWc","outputId":"578a29c9-be59-4008-f7a8-4825c206ebe8","executionInfo":{"status":"ok","timestamp":1646837664457,"user_tz":-540,"elapsed":413,"user":{"displayName":"최연수","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiHNQ4D4codbEtGmD1WaZlhEtN4obyrS6hLi68QGg=s64","userId":"16480553800951077652"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["1.10.0+cu111\n"]}],"source":["import torch \n","import torch.nn as nn # neural network가 담겨있는 모듈\n","import torch.optim as optim  # optimizer가 담겨있는 모듈\n","import torch.nn.init as init # neural network를 초기화 하는 모듈. \n","from torch.utils.data import DataLoader # DataLoader는 데이터를 batch_size만큼 불러오는 역할 등을 수행 \n","\n","import torchvision \n","import torchvision.datasets as dset # 사진 등의 vision 데이터가 담겨있는 모듈\n","import torchvision.transforms as transforms # 데이터를 자르고, 반전시키고 tensor로 변환해주기 등의 데이터 변환 함수를 불러오는 모듈\n","import numpy as np\n","\n","import matplotlib.pyplot as plt\n","\n","print(torch.__version__)\n"]},{"cell_type":"markdown","metadata":{"id":"MH4mk5jEgAX4"},"source":["## 2. 데이터 다운 "]},{"cell_type":"code","execution_count":3,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T06:15:46.182111Z","start_time":"2022-02-22T06:15:45.957191Z"},"id":"q70XVvuLgNhu","outputId":"a1d41c58-f9fe-49dd-cdda-7db611a0dc76","colab":{"base_uri":"https://localhost:8080/","height":423,"referenced_widgets":["4867fa7e848a407282293ff03947bf66","0ff7045d0c8e4529b0affaf229d7b0dd","920ae72a865940839866bf06250c9357","ccae4f95ae5e44a096331065254908f4","430d7b91a51c44678149cf8f9a2b7113","1d3aab5d65124fe0b2ee505a0cf5ecc9","f3211147b44e4f6ca4560682cdfd058f","4c25cd24b0d04e48a71f96b3022b996b","8c810cc336ba4c5497bbe8d27e97defb","753a0753067a4c12ac6e57ba39092942","51514f1c59954dd2b502f1e905931147","5139cea0d09e4307ab6b64a9a9c21700","193169ff22904ef99ad59faacc42c3d1","96fa6597341d493096d03fdfe71824bd","9aee1d84c03547ad9b44c61c652f5092","6216b1ef45764252b123fd1ba6c97c85","53ffa47f895646a3830a80c0f6c2644a","c83ac0de76374e6081f97d61acee2b4f","7368d6ec28634d319d48c2e7e1af81d4","34fa7e02020e4717a0553c8025df3417","b2849963365441a4a131cf24ea11715a","1897944c826a45ae9e384ac682a64286","0fffa57c2639490c8548b9668a2e84c9","bfd7e931c35d41c9bfcfe79d1263f7cd","da3b5352b0574e8ab5b165a9b59d9321","e2396d647ef24d72ab8c05a418035e28","e4dd2de0b83d4441b6f421f0e3d3c2b5","df1ae4cd28174fffb4fa1f613be2bd52","0d7d890f722145bd87fabf8dbd09ed52","73749722b2f54ef9abd10a55e42b7ded","f339f721c08940d7a77b3cbe7d062f76","421262437c514ecdbdd245291d2a91fe","d10c239c23e4430e9f347dfd5a93c35d","f1f1125d7f8a40a4aa8f55e4480965d2","a4d5223d845847039a2165e9520277b3","b103173f5c33442f9b5360ab090b3731","b0df74fc60df461fa509d5b62e4c373f","5bb169f709d94c1882ed02d27fc4fca6","b94095eaa0f54ef690bf1e94a8b77f95","ba164e57cbbd4b9c89770be9c85d1f1c","f1c8d7cd68834558a07c7f8dca354aef","84bfde66a2f0406ea4700f640891d87d","61bda71fcc234e358218c15181534a13","dfb47c5f336542829da54f9918e8b57e"]},"executionInfo":{"status":"ok","timestamp":1646837668947,"user_tz":-540,"elapsed":2751,"user":{"displayName":"최연수","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiHNQ4D4codbEtGmD1WaZlhEtN4obyrS6hLi68QGg=s64","userId":"16480553800951077652"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../MNIST/raw/train-images-idx3-ubyte.gz\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4867fa7e848a407282293ff03947bf66","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/9912422 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Extracting ../MNIST/raw/train-images-idx3-ubyte.gz to ../MNIST/raw\n","\n","Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../MNIST/raw/train-labels-idx1-ubyte.gz\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5139cea0d09e4307ab6b64a9a9c21700","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/28881 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Extracting ../MNIST/raw/train-labels-idx1-ubyte.gz to ../MNIST/raw\n","\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../MNIST/raw/t10k-images-idx3-ubyte.gz\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0fffa57c2639490c8548b9668a2e84c9","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/1648877 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Extracting ../MNIST/raw/t10k-images-idx3-ubyte.gz to ../MNIST/raw\n","\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../MNIST/raw/t10k-labels-idx1-ubyte.gz\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f1f1125d7f8a40a4aa8f55e4480965d2","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/4542 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Extracting ../MNIST/raw/t10k-labels-idx1-ubyte.gz to ../MNIST/raw\n","\n"]}],"source":["#모듈을 통해 torchvision에서 제공하는 데이터를 쉽게 다운받을 수 있습니다. \n","#root는 불러들인 데이터가 저장되는 곳, train은 학습데이터인지에 대한 여부, transform은 데이터 변환함수를 넣는 인자, \n","#target_transform은 label(정답)데이터 또한 변환할 것인지에 대한 여부 \n","#download는 지정된 경로에 데이터가 없다면 다운받을 것인지에 대한 여부를 묻습니다.\n","\n","mnist_train=dset.MNIST(root='../',train=True, transform=transforms.ToTensor(), target_transform=None, download=True)\n","mnist_test=dset.MNIST(root='../',train=False, transform=transforms.ToTensor(), target_transform=None, download=True)"]},{"cell_type":"code","execution_count":4,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T06:25:27.290155Z","start_time":"2022-02-22T06:25:27.275193Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"bwuITE6Xhdqf","outputId":"658b6f58-c8c5-4b5b-82f0-0980b90be47b","executionInfo":{"status":"ok","timestamp":1646837671117,"user_tz":-540,"elapsed":414,"user":{"displayName":"최연수","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiHNQ4D4codbEtGmD1WaZlhEtN4obyrS6hLi68QGg=s64","userId":"16480553800951077652"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([1, 28, 28]) 60000\n","torch.Size([1, 28, 28]) 10000\n","60000 10000\n"]}],"source":["# 데이터 셋 체크 getitem을 써도 되고, 인덱싱을 통해서 확인도 가능\n","# 데이터 하나가 가진 크기 확인 필수! 입력의 사이즈를 알아야 그에 맞게 모델 구현 가능\n","print(mnist_train.__getitem__(0)[0].size(), mnist_train.__len__())\n","print(mnist_test.__getitem__(0)[0].size(), mnist_test.__len__())\n","\n","print(len(mnist_train),len(mnist_test))\n","# print(mnist_train[0])"]},{"cell_type":"markdown","metadata":{"id":"w_Q6-cabhwsP"},"source":["## 3. 하이퍼 파라미터 "]},{"cell_type":"code","execution_count":5,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T06:18:05.562438Z","start_time":"2022-02-22T06:18:05.545485Z"},"id":"lmlwfoYyhz6f","executionInfo":{"status":"ok","timestamp":1646837673041,"user_tz":-540,"elapsed":2,"user":{"displayName":"최연수","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiHNQ4D4codbEtGmD1WaZlhEtN4obyrS6hLi68QGg=s64","userId":"16480553800951077652"}}},"outputs":[],"source":["# CNN의 대표적인 하이퍼 파라미터 num_epoch, batch_size, learnin_rate\n","\n","num_epoch=10\n","batch_size=256\n","learning_rate= 0.0002"]},{"cell_type":"markdown","metadata":{"id":"HlEXO2ntiD2L"},"source":["## 4. 데이터 로더 정의하기 "]},{"cell_type":"markdown","metadata":{"id":"MY-vzNXMiKnA"},"source":["- 사용할 데이터\n","- 배치 사이즈 (batch_size)\n","- 섞을지 여부 (shuffle)\n","- 사용할 프로세스 개수 (num_workers)\n","- 마지막에 남는 데이터의 처리 여부 (drop_last)"]},{"cell_type":"code","execution_count":6,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T06:19:16.713142Z","start_time":"2022-02-22T06:19:16.699176Z"},"id":"wavj9j6OiDZs","executionInfo":{"status":"ok","timestamp":1646837676127,"user_tz":-540,"elapsed":2,"user":{"displayName":"최연수","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiHNQ4D4codbEtGmD1WaZlhEtN4obyrS6hLi68QGg=s64","userId":"16480553800951077652"}}},"outputs":[],"source":["# 위에서 데이터 전부를 정의해 mnist_train에는 모든 학습 데이터가 들어있는 상태\n","# 모든 학습 데이터를 한 번에 학습하면 과부하가 발생\n","# 그래서 데이터를 조금씩 조금씩 쪼개서 학습하기 위해 DataLodaer 사용\n","\n","train_loader=DataLoader(dataset=mnist_train, batch_size=batch_size, shuffle=True, num_workers=2, drop_last=True)\n","test_loader=DataLoader(dataset=mnist_test, batch_size=batch_size, shuffle=False, num_workers=2, drop_last=True)"]},{"cell_type":"markdown","metadata":{"id":"7_BVY0-gjIW5"},"source":["## 5. 모델 정의하기 "]},{"cell_type":"markdown","metadata":{"id":"4o0SSaiiCWOn"},"source":["Conv(1,16,5)->ReLu->Conv(16,32,5)->ReLu->MaxPool2d(2,2)->Conv2d(32,64,5)->ReLu->MaxPool2d(2,2)->fc"]},{"cell_type":"markdown","source":["dropout 적용"],"metadata":{"id":"8F1mRDvBLkUH"}},{"cell_type":"code","execution_count":7,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T06:55:26.565352Z","start_time":"2022-02-22T06:55:26.546402Z"},"id":"u07CLv7wjBFZ","executionInfo":{"status":"ok","timestamp":1646837802508,"user_tz":-540,"elapsed":525,"user":{"displayName":"최연수","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiHNQ4D4codbEtGmD1WaZlhEtN4obyrS6hLi68QGg=s64","userId":"16480553800951077652"}}},"outputs":[],"source":["# 모델 정의 시 유념해야할 건 각 층의 결과물의 사이즈\n","# 모델이 복잡해지면 복잡해질수록 이는 더욱 중요해지므로 레이어별로 도출되는 데이터의 크기를 써넣는 것을 추천\n","# 해당 모델은 임의로 만든 모델로 성능을 보장하지 X\n","\n","class CNN(nn.Module):\n","    def __init__(self):\n","        super(CNN,self).__init__()\n","        self.layer = nn.Sequential(\n","            nn.Conv2d(in_channels=1,out_channels=16,kernel_size=5),             # [batch_size,1,28,28] -> [batch_size,16,24,24]\n","            nn.ReLU(),\n","            nn.Dropout2d(0.2),                                                         # 필터의 개수(16개) 임의로 설정 \n","            nn.Conv2d(in_channels=16,out_channels=32,kernel_size=5),            # [batch_size,16,24,24] -> [batch_size,32,20,20]\n","            nn.ReLU(),\n","            nn.Dropout2d(0.2),\n","            nn.MaxPool2d(kernel_size=2,stride=2),                               # [batch_size,32,20,20] -> [batch_size,32,10,10]\n","            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5),          # [batch_size,32,10,10] -> [batch_size,64,6,6]\n","            nn.ReLU(),\n","            nn.Dropout2d(0.2),\n","            nn.MaxPool2d(kernel_size=2,stride=2)                                # [batch_size,64,6,6] -> [batch_size,64,3,3]\n","        )\n","        self.fc_layer = nn.Sequential(                                          \n","            nn.Linear(64*3*3,100),                                              # [batch_size,64*3*3] -> [batch_size,100]\n","            nn.ReLU(),\n","            nn.Dropout2d(0.2),\n","            nn.Linear(100,10)                                                   # [batch_size,100] -> [batch_size,10]\n","        )       \n","        \n","    def forward(self,x):\n","        out = self.layer(x)                                                     # self.layer에 정의한 Sequential의 연산을 차례로 수행\n","        out = out.view(out.size(0),-1)                                           # view 함수를 이용해 텐서를 flatten \n","                                                                                # ex) 2x3 텐서를 .view(1,-1) 해주면 1x6으로, view(3,-1)이면 3x2로\n","                                                                                # !전체 텐서의 크기가 batch_size로 나누어 떨어지지 않으면 오류!\n","        out = self.fc_layer(out)\n","        return out"]},{"cell_type":"markdown","metadata":{"id":"CmYd0BSlPbgz"},"source":["파이토치 - torch.nn (Conv2d 설명 확인)\n","https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.Conv2d\n","\n","파이토치 - TORCH.TENSOR\n","https://pytorch.org/docs/stable/tensors.html?highlight=view#torch.Tensor.view"]},{"cell_type":"markdown","metadata":{"id":"gFI_EbhklAJE"},"source":["## 6. 손실함수와 최적화 함수 정의하기 "]},{"cell_type":"code","execution_count":8,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T06:55:28.898300Z","start_time":"2022-02-22T06:55:28.883340Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"yyAD69AflC24","outputId":"85f8d506-dc2b-4c3b-ccbf-13735e568d4b","executionInfo":{"status":"ok","timestamp":1646837950289,"user_tz":-540,"elapsed":9525,"user":{"displayName":"최연수","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiHNQ4D4codbEtGmD1WaZlhEtN4obyrS6hLi68QGg=s64","userId":"16480553800951077652"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["cuda:0\n"]}],"source":["# gpu나 cpu를 device로 불러 device에서 학습이 진행되게끔 모델을 device에 올리기\n","# loss function과 optimizer도 정의해야 \n","\n","device=torch.device(\"cuda:0\" if torch.cuda.is_available() else 'cpu')  \n","\n","print(device)\n","\n","model=CNN().to(device)\n","\n","loss_func=nn.CrossEntropyLoss()\n","\n","optimizer=torch.optim.Adam(model.parameters(),lr=learning_rate, weight_decay=0.01) # L2 regularization"]},{"cell_type":"code","execution_count":null,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T06:34:52.456732Z","start_time":"2022-02-22T06:34:45.687608Z"},"id":"H2PsPJHpPbgz","outputId":"d67d6366-e5a5-4865-99e9-a5bc453b0389"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: torchsummary in c:\\users\\82105\\anaconda3\\lib\\site-packages (1.5.1)\n"]},{"name":"stderr","output_type":"stream","text":["WARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.\n","You should consider upgrading via the 'c:\\users\\82105\\anaconda3\\python.exe -m pip install --upgrade pip' command.\n"]}],"source":["!pip install torchsummary"]},{"cell_type":"code","execution_count":null,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T06:55:30.609883Z","start_time":"2022-02-22T06:55:30.571984Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"VXSyhRs_ROhe","outputId":"4a52d286-2b1e-417d-e961-e614caa316b1","scrolled":false},"outputs":[{"name":"stdout","output_type":"stream","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1          [256, 16, 24, 24]             416\n","              ReLU-2          [256, 16, 24, 24]               0\n","            Conv2d-3          [256, 32, 20, 20]          12,832\n","              ReLU-4          [256, 32, 20, 20]               0\n","         MaxPool2d-5          [256, 32, 10, 10]               0\n","            Conv2d-6            [256, 64, 6, 6]          51,264\n","              ReLU-7            [256, 64, 6, 6]               0\n","         MaxPool2d-8            [256, 64, 3, 3]               0\n","            Linear-9                 [256, 100]          57,700\n","             ReLU-10                 [256, 100]               0\n","           Linear-11                  [256, 10]           1,010\n","================================================================\n","Total params: 123,222\n","Trainable params: 123,222\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.77\n","Forward/backward pass size (MB): 102.79\n","Params size (MB): 0.47\n","Estimated Total Size (MB): 104.02\n","----------------------------------------------------------------\n"]}],"source":["from torchsummary import summary\n","summary(model,(1,28,28), batch_size=256)          # 파이토치는 입력을 (channel, width, height) 순으로"]},{"cell_type":"markdown","metadata":{"id":"_DPFr5TZl_6I"},"source":["## 7. 학습하기 "]},{"cell_type":"code","execution_count":9,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T07:09:32.830446Z","start_time":"2022-02-22T06:57:00.464788Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"4Am3C_jDlcvf","outputId":"920e4418-84a6-40bf-cf66-7a45ba690696","executionInfo":{"status":"ok","timestamp":1646838041156,"user_tz":-540,"elapsed":71939,"user":{"displayName":"최연수","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiHNQ4D4codbEtGmD1WaZlhEtN4obyrS6hLi68QGg=s64","userId":"16480553800951077652"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor(2.3077, device='cuda:0', grad_fn=<NllLossBackward0>)\n","tensor(0.3351, device='cuda:0', grad_fn=<NllLossBackward0>)\n","tensor(0.2660, device='cuda:0', grad_fn=<NllLossBackward0>)\n","tensor(0.1801, device='cuda:0', grad_fn=<NllLossBackward0>)\n","tensor(0.1427, device='cuda:0', grad_fn=<NllLossBackward0>)\n","tensor(0.1737, device='cuda:0', grad_fn=<NllLossBackward0>)\n","tensor(0.1029, device='cuda:0', grad_fn=<NllLossBackward0>)\n","tensor(0.1480, device='cuda:0', grad_fn=<NllLossBackward0>)\n","tensor(0.0998, device='cuda:0', grad_fn=<NllLossBackward0>)\n","tensor(0.0772, device='cuda:0', grad_fn=<NllLossBackward0>)\n"]}],"source":["loss_arr=[]\n","\n","for i in range(num_epoch):\n","  for j,[image,label] in enumerate(train_loader):\n","    x=image.to(device)            # 학습이미지\n","    y_=label.to(device)           # 정답을 device에 올리고.\n","    optimizer.zero_grad() \n","    output=model.forward(x)       # 학습이미지를 모델에 돌린 결과\n","    loss=loss_func(output,y_)     # 결과와 정답을 loss function에 넣고 loss를 추출\n","    loss.backward()               # 추출된 loss를 통해 역전파\n","    optimizer.step()              # 역전파를 했다면 optimizer.step()를 통해 파라미터를 이동\n","    \n","    if j%1000==0:\n","      print(loss)\n","      loss_arr.append(loss.cpu().detach().numpy())"]},{"cell_type":"markdown","metadata":{"id":"Z0XBGIpgDvMK"},"source":["## 8. 손실 시각화 "]},{"cell_type":"code","execution_count":10,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T07:15:24.870373Z","start_time":"2022-02-22T07:15:24.658903Z"},"colab":{"base_uri":"https://localhost:8080/","height":265},"id":"8mJdMonFDu9a","outputId":"0bc18b6d-e2da-4839-cb95-e753418824fa","executionInfo":{"status":"ok","timestamp":1646838417484,"user_tz":-540,"elapsed":882,"user":{"displayName":"최연수","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiHNQ4D4codbEtGmD1WaZlhEtN4obyrS6hLi68QGg=s64","userId":"16480553800951077652"}}},"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}],"source":["plt.plot(loss_arr)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"YJNZDgOpDpTI"},"source":["## 9. 테스트 하기 "]},{"cell_type":"code","execution_count":11,"metadata":{"ExecuteTime":{"end_time":"2022-02-22T07:15:32.636654Z","start_time":"2022-02-22T07:15:25.525663Z"},"colab":{"base_uri":"https://localhost:8080/"},"id":"vHylngGCDpE0","outputId":"6ff22cce-b13d-4c98-c951-cd76f32548fa","executionInfo":{"status":"ok","timestamp":1646838421499,"user_tz":-540,"elapsed":1439,"user":{"displayName":"최연수","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiHNQ4D4codbEtGmD1WaZlhEtN4obyrS6hLi68QGg=s64","userId":"16480553800951077652"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy of Test Data: 96.98517608642578%\n"]}],"source":["correct=0\n","total=0\n","\n","with torch.no_grad():\n","  for image,label in test_loader:\n","    x=image.to(device)\n","    y_=label.to(device)\n","    output=model.forward(x)\n","     #https://pytorch.org/docs/stable/torch.html?highlight=max#torch.max\n","    _,output_index=torch.max(output,1)  #max함수는 max value와 max value의 index를 도출합니다. 두 번째 인자 1은 결과의 dimension을 의미합니다. \n","\n","    total+=label.size(0)\n","\n","    correct += (output_index==y_).sum().float()\n","\n","print(\"Accuracy of Test Data: {}%\".format(100*correct/total))"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"name":"220303 DLdevelop ","provenance":[{"file_id":"1xI8Om4TXA7agc9J-Gh9D9O28iyrGL8n4","timestamp":1646822091476}]},"hide_input":false,"kernelspec":{"display_name":"Python 3","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":"3.8.10"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table 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