PyTorch_RandomErasing.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"PyTorch_CIFAR10_with_RandomErasing.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"9_4mVKJel0VU","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":306},"outputId":"85120fc6-3516-446e-a0d7-d3af0bd14221","executionInfo":{"status":"ok","timestamp":1562488863882,"user_tz":-540,"elapsed":2870,"user":{"displayName":"中嶋拓郎","photoUrl":"","userId":"11259832953657969381"}}},"source":["!nvidia-smi"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Sun Jul  7 08:41:02 2019       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 418.67       Driver Version: 410.79       CUDA Version: 10.0     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|===============================+======================+======================|\n","|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   30C    P8    25W / 149W |      0MiB / 11441MiB |      0%      Default |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                       GPU Memory |\n","|  GPU       PID   Type   Process name                             Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"_k8u3RdK5Nn-","colab_type":"code","colab":{}},"source":["import torch\n","import torchvision\n","import torchvision.transforms as transforms"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4uzh4w8zURRX","colab_type":"code","colab":{}},"source":["%matplotlib inline\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from PIL import Image"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cpDHCVJsXq7s","colab_type":"code","colab":{}},"source":["BATCH_SIZE = 96"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZLYvfeWDW_Vj","colab_type":"code","colab":{}},"source":["class RandomErasing(object):\n","    def __init__(self, p=0.5, sl=0.02, sh=0.4, r1=0.3, r2=3):\n","        self.p = p\n","        self.sl = sl\n","        self.sh = sh\n","        self.r1 = r1\n","        self.r2 = r2\n","\n","    def __call__(self, img):\n","        \n","        if np.random.rand() > self.p:\n","            return img\n","        \n","        img = np.array(img)\n","        \n","        while True:\n","            img_h, img_w, img_c = img.shape\n","\n","            img_area = img_h * img_w\n","            mask_area = np.random.uniform(self.sl, self.sh) * img_area\n","            mask_aspect_ratio = np.random.uniform(self.r1, self.r2)\n","            mask_w = int(np.sqrt(mask_area / mask_aspect_ratio))\n","            mask_h = int(np.sqrt(mask_area * mask_aspect_ratio))\n","            \n","            mask = np.random.rand(mask_h, mask_w, img_c) * 255\n","\n","            left = np.random.randint(0, img_w)\n","            top = np.random.randint(0, img_h)\n","            right = left + mask_w\n","            bottom = top + mask_h\n","        \n","            if right <= img_w and bottom <= img_h:\n","                break\n","        \n","        img[top:bottom, left:right, :] = mask\n","        \n","        return Image.fromarray(img)\n","        "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QANOPvH969Xs","colab_type":"code","outputId":"3ac0a23a-d5d5-4deb-e1d7-a202a61c2c7f","executionInfo":{"status":"ok","timestamp":1562488871659,"user_tz":-540,"elapsed":3230,"user":{"displayName":"中嶋拓郎","photoUrl":"","userId":"11259832953657969381"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["train_transform = transforms.Compose(\n","    [transforms.RandomHorizontalFlip(),\n","     RandomErasing(p=0.5, sh=0.3),\n","     transforms.ToTensor()]\n",")\n","\n","test_transform = transforms.Compose(\n","    [transforms.RandomHorizontalFlip(),\n","     transforms.ToTensor()]\n",")\n","\n","trainval_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=train_transform)\n","test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=test_transform)\n","train_size = int(len(trainval_dataset) * 0.8)\n","val_size = len(trainval_dataset)- train_size\n","train_dataset, val_dataset = torch.utils.data.random_split(trainval_dataset, [train_size, val_size])\n","\n","print(\"train data num:\", len(train_dataset))\n","print(\"val    data num:\", len(val_dataset))\n","print(\"test data num:\", len(test_dataset))\n","\n","train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)\n","val_loader    = torch.utils.data.DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)\n","test_loader  = torch.utils.data.DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)\n","\n","\n","classes = ('plane', 'car', 'bird', 'cat',\n","           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Files already downloaded and verified\n","Files already downloaded and verified\n","train data num: 40000\n","val    data num: 10000\n","test data num: 10000\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Nk5qtY6aYEkq","colab_type":"code","colab":{}},"source":["def count_dataset(dataset, n_classes, normalize=False):\n","    counter = [0] * n_classes\n","    for _, label in dataset:\n","        counter[label] += 1\n","     \n","    if normalize:\n","        sum_cnt = sum(counter)\n","        normalized_counter = [c / sum_cnt for c in counter]\n","        return normalized_counter\n","    else:\n","        return counter\n","    \n","def show_bar(val, labels, title=''):\n","    left = [i for i in range(len(labels))]\n","    plt.title(title)\n","    plt.bar(left, val, tick_label=labels, align=\"center\")\n","    plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zQ_uV1VKYlhq","colab_type":"code","colab":{}},"source":["train_cnt = count_dataset(train_dataset, len(classes), normalize=False)\n","val_cnt = count_dataset(val_dataset, len(classes), normalize=False)\n","test_cnt = count_dataset(test_dataset, len(classes), normalize=False)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lricFt8pNeKA","colab_type":"code","outputId":"45cab389-c8d8-416d-af7c-9feeb0f11d49","executionInfo":{"status":"ok","timestamp":1562488885890,"user_tz":-540,"elapsed":15787,"user":{"displayName":"中嶋拓郎","photoUrl":"","userId":"11259832953657969381"}},"colab":{"base_uri":"https://localhost:8080/","height":809}},"source":["show_bar(train_cnt, classes, title='train dataset')\n","show_bar(val_cnt, classes, title='val dataset')\n","show_bar(test_cnt, classes, title='test dataset')"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYAAAAEICAYAAABWJCMKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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QmCC\n","text/plain":["<Figure size 432x288 with 1 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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Lt_mVMqO7DmU","colab_type":"code","outputId":"d4cbb175-04da-4025-a92f-0b4d075c6f6b","executionInfo":{"status":"ok","timestamp":1562488886936,"user_tz":-540,"elapsed":15538,"user":{"displayName":"中嶋拓郎","photoUrl":"","userId":"11259832953657969381"}},"colab":{"base_uri":"https://localhost:8080/","height":787}},"source":["def show_img(img, figsize=(5,5)):\n","    plt.figure(figsize=figsize)\n","    if hasattr(img, 'numpy'):\n","        img = img.numpy()\n","    img = img.transpose((1, 2, 0)) # C H W -> H W C\n","    img = np.squeeze(img)\n","    plt.imshow(img)\n","    plt.axis('off')\n","    plt.show()\n","    \n","dataiter = iter(train_loader)\n","imgs, labels = dataiter.next()\n","\n","imgs = imgs[:25]\n","labels = labels[:25]\n","grid = torchvision.utils.make_grid(imgs, nrow=5, padding=1)\n","show_img(grid, figsize=(12, 12))\n","\n","class_list = [classes[labels[j]] for j in range(25)]\n","class_list = np.array(class_list)\n","print(class_list.reshape(5,5))"],"execution_count":10,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAArQAAAKtCAYAAADW5/2JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nt7m+mNy3zBd5nm699TauK60+Pe8dNm89HnWlLM8qpOjI9uV5zapWeW/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7vq7P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5LI8362zXm/Pz/LzHujp9mkxuME4+uBuRp/I8ttZTT9Kr8vp8z6N3Zh/raXRK64nt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\n","text/plain":["<Figure size 864x864 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["[['frog' 'bird' 'cat' 'bird' 'cat']\n"," ['horse' 'truck' 'truck' 'cat' 'truck']\n"," ['deer' 'plane' 'deer' 'frog' 'ship']\n"," ['dog' 'car' 'horse' 'cat' 'deer']\n"," ['ship' 'ship' 'plane' 'horse' 'frog']]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"lTobLyqQQr7j","colab_type":"code","colab":{}},"source":["import torch.nn as nn\n","import torch.nn.functional as F\n","\n","class Flatten(nn.Module):\n","    def forward(self, x):\n","        return x.view(x.size(0), -1)\n","\n","    \n","class Network(nn.Module):\n","    \n","    def __init__(self, num_classes=10):\n","        super(Network, self).__init__()\n","        \n","        self.features = nn.Sequential(\n","            nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),\n","            nn.ReLU(inplace=True),\n","            nn.MaxPool2d(kernel_size=2, stride=2),\n","\n","            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),\n","            nn.ReLU(inplace=True),\n","            nn.MaxPool2d(kernel_size=2, stride=2),\n","\n","            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),\n","            nn.ReLU(inplace=True),\n","            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),\n","            nn.ReLU(inplace=True),\n","            nn.MaxPool2d(kernel_size=2, stride=2),\n","        )\n","        \n","        self.flatten = Flatten()\n","        \n","        self.classifier = nn.Sequential(\n","            nn.Linear(128 * 4 * 4, 512),\n","            nn.ReLU(inplace=True),\n","            nn.Dropout(),\n","            nn.Linear(512, num_classes),\n","        )\n","        \n","    def forward(self, x):\n","        x = self.features(x)\n","        x = self.flatten(x)\n","        x = self.classifier(x)\n","        return x\n","    \n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NWIeNd2cLDwz","colab_type":"code","colab":{}},"source":["def train(model, device, optimizer, criterion, train_loader, preprocess=None):\n","    model.train()\n","    running_loss = 0\n","    correct = 0\n","    \n","    for batch_idx, (data, labels) in enumerate(train_loader):\n","        \n","        if preprocess:\n","            data = preprocess(data)\n","        \n","        data, labels = data.to(device), labels.to(device)\n","        optimizer.zero_grad()\n","        outputs = model(data)\n","    \n","        loss = criterion(outputs, labels)\n","        loss.backward()\n","        \n","        optimizer.step()\n","\n","        pred = outputs.argmax(dim=1)\n","        correct += (pred == labels).sum().item()\n","        running_loss += loss.item()\n","\n","    train_acc = correct / len(train_loader.dataset)\n","    train_loss = running_loss / len(train_loader)\n","\n","    return {'acc':train_acc, 'loss':train_loss}"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"MsKx5EC2LHJM","colab_type":"code","colab":{}},"source":["def validation(model, device, criterion, val_loader, preprocess=None):\n","    model.eval()\n","    runnning_loss = 0\n","    correct = 0\n","    with torch.no_grad():\n","        for data, labels in val_loader:\n","            if preprocess:\n","                data = preprocess(data)\n","                \n","            data, labels = data.to(device), labels.to(device)\n","            \n","            output = model(data)\n","            \n","            pred = output.argmax(dim=1) \n","            correct +=  (pred == labels).sum().item()\n","            runnning_loss += criterion(output, labels).item()\n","\n","    val_acc = correct / len(val_loader.dataset)\n","    val_loss = runnning_loss / len(val_loader)\n","\n","    return {'acc':val_acc, 'loss':val_loss}"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"45IUwOPRLJBS","colab_type":"code","colab":{}},"source":["def run(epochs, model, device, optimizer, criterion, train_loader, val_loader, preprocess=None):\n","    history = {'train_acc':[], 'train_loss':[], 'val_acc':[], 'val_loss':[]}\n","\n","    for epoch in range(1, epochs+1):\n","        train_ret = train(model, device, optimizer, criterion, train_loader, preprocess=preprocess)\n","        val_ret = validation(model, device, criterion, val_loader, preprocess=preprocess)\n","        \n","        print(\"Epoch:{} train_acc:{:.4f}% train_loss:{:.4f} val_acc:{:.4f}% val_loss:{:.4f}\".format(\n","            epoch, train_ret['acc'], train_ret['loss'], val_ret['acc'], val_ret['loss']))\n","\n","        history['train_acc'].append(train_ret['acc'])\n","        history['train_loss'].append(train_ret['loss'])\n","        history['val_acc'].append(val_ret['acc'])\n","        history['val_loss'].append(val_ret['loss'])\n","    \n","    return history"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cGrsOr8olNCQ","colab_type":"code","outputId":"3754a217-d977-46a6-b132-aa100e40e8d8","executionInfo":{"status":"ok","timestamp":1562490268798,"user_tz":-540,"elapsed":1377410,"user":{"displayName":"中嶋拓郎","photoUrl":"","userId":"11259832953657969381"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["import torch.optim as optim\n","\n","model = Network()\n","from torchsummary import summary\n","summary(model, input_size=(3, 32, 32),  device=\"cpu\")\n","\n","criterion = nn.CrossEntropyLoss()\n","optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n","\n","epochs = 100\n","\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(\"device:\",device)\n","\n","model = model.to(device)\n","history = run(epochs, model, device, optimizer, criterion, train_loader, val_loader)"],"execution_count":15,"outputs":[{"output_type":"stream","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1           [-1, 32, 32, 32]             896\n","              ReLU-2           [-1, 32, 32, 32]               0\n","         MaxPool2d-3           [-1, 32, 16, 16]               0\n","            Conv2d-4           [-1, 64, 16, 16]          18,496\n","              ReLU-5           [-1, 64, 16, 16]               0\n","         MaxPool2d-6             [-1, 64, 8, 8]               0\n","            Conv2d-7            [-1, 128, 8, 8]          73,856\n","              ReLU-8            [-1, 128, 8, 8]               0\n","            Conv2d-9            [-1, 128, 8, 8]         147,584\n","             ReLU-10            [-1, 128, 8, 8]               0\n","        MaxPool2d-11            [-1, 128, 4, 4]               0\n","          Flatten-12                 [-1, 2048]               0\n","           Linear-13                  [-1, 512]       1,049,088\n","             ReLU-14                  [-1, 512]               0\n","          Dropout-15                  [-1, 512]               0\n","           Linear-16                   [-1, 10]           5,130\n","================================================================\n","Total params: 1,295,050\n","Trainable params: 1,295,050\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.01\n","Forward/backward pass size (MB): 1.14\n","Params size (MB): 4.94\n","Estimated Total Size (MB): 6.09\n","----------------------------------------------------------------\n","device: cuda\n","Epoch:1 train_acc:0.1595% train_loss:2.2099 val_acc:0.2519% val_loss:2.0627\n","Epoch:2 train_acc:0.3022% train_loss:1.9043 val_acc:0.3865% val_loss:1.6920\n","Epoch:3 train_acc:0.3971% train_loss:1.6448 val_acc:0.4234% val_loss:1.5583\n","Epoch:4 train_acc:0.4590% train_loss:1.4848 val_acc:0.5018% val_loss:1.3592\n","Epoch:5 train_acc:0.5065% train_loss:1.3648 val_acc:0.5528% val_loss:1.2632\n","Epoch:6 train_acc:0.5502% train_loss:1.2541 val_acc:0.5814% val_loss:1.1751\n","Epoch:7 train_acc:0.5827% train_loss:1.1652 val_acc:0.6109% val_loss:1.0942\n","Epoch:8 train_acc:0.6161% train_loss:1.0822 val_acc:0.6427% val_loss:1.0019\n","Epoch:9 train_acc:0.6411% train_loss:1.0114 val_acc:0.6643% val_loss:0.9673\n","Epoch:10 train_acc:0.6644% train_loss:0.9492 val_acc:0.6610% val_loss:0.9594\n","Epoch:11 train_acc:0.6853% train_loss:0.9032 val_acc:0.6792% val_loss:0.9159\n","Epoch:12 train_acc:0.7049% train_loss:0.8492 val_acc:0.6850% val_loss:0.8901\n","Epoch:13 train_acc:0.7134% train_loss:0.8150 val_acc:0.7031% val_loss:0.8516\n","Epoch:14 train_acc:0.7284% train_loss:0.7708 val_acc:0.7160% val_loss:0.8170\n","Epoch:15 train_acc:0.7415% train_loss:0.7388 val_acc:0.6964% val_loss:0.8834\n","Epoch:16 train_acc:0.7481% train_loss:0.7167 val_acc:0.7244% val_loss:0.7940\n","Epoch:17 train_acc:0.7555% train_loss:0.6911 val_acc:0.7365% val_loss:0.7615\n","Epoch:18 train_acc:0.7709% train_loss:0.6578 val_acc:0.7417% val_loss:0.7646\n","Epoch:19 train_acc:0.7801% train_loss:0.6290 val_acc:0.7383% val_loss:0.7714\n","Epoch:20 train_acc:0.7869% train_loss:0.6088 val_acc:0.7422% val_loss:0.7552\n","Epoch:21 train_acc:0.7946% train_loss:0.5879 val_acc:0.7509% val_loss:0.7254\n","Epoch:22 train_acc:0.7975% train_loss:0.5757 val_acc:0.7469% val_loss:0.7341\n","Epoch:23 train_acc:0.8050% train_loss:0.5530 val_acc:0.7522% val_loss:0.7213\n","Epoch:24 train_acc:0.8132% train_loss:0.5310 val_acc:0.7530% val_loss:0.7293\n","Epoch:25 train_acc:0.8216% train_loss:0.5147 val_acc:0.7562% val_loss:0.7203\n","Epoch:26 train_acc:0.8278% train_loss:0.4890 val_acc:0.7637% val_loss:0.7227\n","Epoch:27 train_acc:0.8314% train_loss:0.4800 val_acc:0.7625% val_loss:0.7189\n","Epoch:28 train_acc:0.8354% train_loss:0.4673 val_acc:0.7628% val_loss:0.7309\n","Epoch:29 train_acc:0.8450% train_loss:0.4395 val_acc:0.7558% val_loss:0.7648\n","Epoch:30 train_acc:0.8454% train_loss:0.4370 val_acc:0.7688% val_loss:0.7184\n","Epoch:31 train_acc:0.8471% train_loss:0.4317 val_acc:0.7724% val_loss:0.7138\n","Epoch:32 train_acc:0.8545% train_loss:0.4157 val_acc:0.7653% val_loss:0.7514\n","Epoch:33 train_acc:0.8597% train_loss:0.3995 val_acc:0.7632% val_loss:0.7383\n","Epoch:34 train_acc:0.8630% train_loss:0.3886 val_acc:0.7771% val_loss:0.7324\n","Epoch:35 train_acc:0.8691% train_loss:0.3747 val_acc:0.7662% val_loss:0.7522\n","Epoch:36 train_acc:0.8721% train_loss:0.3626 val_acc:0.7564% val_loss:0.7847\n","Epoch:37 train_acc:0.8724% train_loss:0.3603 val_acc:0.7813% val_loss:0.7296\n","Epoch:38 train_acc:0.8771% train_loss:0.3538 val_acc:0.7721% val_loss:0.7523\n","Epoch:39 train_acc:0.8799% train_loss:0.3416 val_acc:0.7702% val_loss:0.7717\n","Epoch:40 train_acc:0.8813% train_loss:0.3368 val_acc:0.7807% val_loss:0.7476\n","Epoch:41 train_acc:0.8831% train_loss:0.3305 val_acc:0.7804% val_loss:0.7675\n","Epoch:42 train_acc:0.8865% train_loss:0.3241 val_acc:0.7743% val_loss:0.7445\n","Epoch:43 train_acc:0.8905% train_loss:0.3114 val_acc:0.7786% val_loss:0.7622\n","Epoch:44 train_acc:0.8882% train_loss:0.3188 val_acc:0.7798% val_loss:0.7811\n","Epoch:45 train_acc:0.8922% train_loss:0.3063 val_acc:0.7754% val_loss:0.7619\n","Epoch:46 train_acc:0.8943% train_loss:0.3012 val_acc:0.7741% val_loss:0.7885\n","Epoch:47 train_acc:0.8979% train_loss:0.2926 val_acc:0.7756% val_loss:0.7874\n","Epoch:48 train_acc:0.8991% train_loss:0.2895 val_acc:0.7697% val_loss:0.8102\n","Epoch:49 train_acc:0.8991% train_loss:0.2909 val_acc:0.7703% val_loss:0.7898\n","Epoch:50 train_acc:0.9020% train_loss:0.2775 val_acc:0.7766% val_loss:0.8144\n","Epoch:51 train_acc:0.9021% train_loss:0.2813 val_acc:0.7787% val_loss:0.7886\n","Epoch:52 train_acc:0.9073% train_loss:0.2712 val_acc:0.7814% val_loss:0.7773\n","Epoch:53 train_acc:0.9062% train_loss:0.2657 val_acc:0.7752% val_loss:0.8211\n","Epoch:54 train_acc:0.9061% train_loss:0.2679 val_acc:0.7759% val_loss:0.8608\n","Epoch:55 train_acc:0.9111% train_loss:0.2575 val_acc:0.7748% val_loss:0.7849\n","Epoch:56 train_acc:0.9095% train_loss:0.2615 val_acc:0.7791% val_loss:0.8101\n","Epoch:57 train_acc:0.9119% train_loss:0.2509 val_acc:0.7759% val_loss:0.8177\n","Epoch:58 train_acc:0.9148% train_loss:0.2457 val_acc:0.7791% val_loss:0.8421\n","Epoch:59 train_acc:0.9131% train_loss:0.2469 val_acc:0.7734% val_loss:0.8664\n","Epoch:60 train_acc:0.9144% train_loss:0.2437 val_acc:0.7774% val_loss:0.8488\n","Epoch:61 train_acc:0.9185% train_loss:0.2327 val_acc:0.7750% val_loss:0.8252\n","Epoch:62 train_acc:0.9183% train_loss:0.2347 val_acc:0.7793% val_loss:0.8704\n","Epoch:63 train_acc:0.9186% train_loss:0.2384 val_acc:0.7789% val_loss:0.8069\n","Epoch:64 train_acc:0.9219% train_loss:0.2278 val_acc:0.7641% val_loss:0.9297\n","Epoch:65 train_acc:0.9201% train_loss:0.2326 val_acc:0.7754% val_loss:0.9135\n","Epoch:66 train_acc:0.9193% train_loss:0.2332 val_acc:0.7780% val_loss:0.8556\n","Epoch:67 train_acc:0.9214% train_loss:0.2263 val_acc:0.7834% val_loss:0.8621\n","Epoch:68 train_acc:0.9258% train_loss:0.2146 val_acc:0.7842% val_loss:0.8720\n","Epoch:69 train_acc:0.9229% train_loss:0.2227 val_acc:0.7766% val_loss:0.8709\n","Epoch:70 train_acc:0.9249% train_loss:0.2158 val_acc:0.7834% val_loss:0.8350\n","Epoch:71 train_acc:0.9234% train_loss:0.2215 val_acc:0.7817% val_loss:0.8355\n","Epoch:72 train_acc:0.9260% train_loss:0.2166 val_acc:0.7775% val_loss:0.9039\n","Epoch:73 train_acc:0.9276% train_loss:0.2098 val_acc:0.7792% val_loss:0.9041\n","Epoch:74 train_acc:0.9260% train_loss:0.2149 val_acc:0.7753% val_loss:0.9392\n","Epoch:75 train_acc:0.9286% train_loss:0.2068 val_acc:0.7827% val_loss:0.8731\n","Epoch:76 train_acc:0.9260% train_loss:0.2140 val_acc:0.7810% val_loss:0.8880\n","Epoch:77 train_acc:0.9304% train_loss:0.2029 val_acc:0.7823% val_loss:0.8801\n","Epoch:78 train_acc:0.9290% train_loss:0.2076 val_acc:0.7876% val_loss:0.9225\n","Epoch:79 train_acc:0.9301% train_loss:0.1981 val_acc:0.7805% val_loss:0.8848\n","Epoch:80 train_acc:0.9308% train_loss:0.2035 val_acc:0.7851% val_loss:0.9487\n","Epoch:81 train_acc:0.9283% train_loss:0.2104 val_acc:0.7744% val_loss:0.8744\n","Epoch:82 train_acc:0.9300% train_loss:0.2023 val_acc:0.7899% val_loss:0.8685\n","Epoch:83 train_acc:0.9336% train_loss:0.1953 val_acc:0.7847% val_loss:0.9062\n","Epoch:84 train_acc:0.9311% train_loss:0.1989 val_acc:0.7883% val_loss:0.8800\n","Epoch:85 train_acc:0.9320% train_loss:0.1961 val_acc:0.7815% val_loss:0.9092\n","Epoch:86 train_acc:0.9333% train_loss:0.1966 val_acc:0.7735% val_loss:0.9059\n","Epoch:87 train_acc:0.9335% train_loss:0.1991 val_acc:0.7745% val_loss:0.9126\n","Epoch:88 train_acc:0.9330% train_loss:0.1951 val_acc:0.7801% val_loss:0.9592\n","Epoch:89 train_acc:0.9359% train_loss:0.1830 val_acc:0.7789% val_loss:0.9900\n","Epoch:90 train_acc:0.9328% train_loss:0.1936 val_acc:0.7817% val_loss:0.9010\n","Epoch:91 train_acc:0.9354% train_loss:0.1853 val_acc:0.7768% val_loss:0.9501\n","Epoch:92 train_acc:0.9363% train_loss:0.1896 val_acc:0.7839% val_loss:0.9471\n","Epoch:93 train_acc:0.9363% train_loss:0.1886 val_acc:0.7864% val_loss:0.8806\n","Epoch:94 train_acc:0.9371% train_loss:0.1874 val_acc:0.7859% val_loss:0.9225\n","Epoch:95 train_acc:0.9378% train_loss:0.1813 val_acc:0.7796% val_loss:0.9073\n","Epoch:96 train_acc:0.9370% train_loss:0.1831 val_acc:0.7856% val_loss:0.9255\n","Epoch:97 train_acc:0.9361% train_loss:0.1871 val_acc:0.7921% val_loss:0.9422\n","Epoch:98 train_acc:0.9390% train_loss:0.1776 val_acc:0.7814% val_loss:1.0071\n","Epoch:99 train_acc:0.9368% train_loss:0.1825 val_acc:0.7905% val_loss:0.9118\n","Epoch:100 train_acc:0.9398% train_loss:0.1750 val_acc:0.7785% val_loss:0.9479\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"LJ2ErwlIWDXY","colab_type":"code","colab":{}},"source":["save_model = False\n","if (save_model):\n","    torch.save(model.state_dict(),\"cifar10.pt\")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"yXuyk4FOMP9L","colab_type":"code","outputId":"bf1004bf-d5a4-4436-e995-78b70a2da280","executionInfo":{"status":"ok","timestamp":1562490269275,"user_tz":-540,"elapsed":1302220,"user":{"displayName":"中嶋拓郎","photoUrl":"","userId":"11259832953657969381"}},"colab":{"base_uri":"https://localhost:8080/","height":549}},"source":["def plot_history(history):\n","    \n","    epochs = len(history['train_acc'],)\n","    \n","    xaxis = range(epochs)\n","    plt.figure()\n","    plt.plot(xaxis, history['train_acc'], 'r-', label='train_acc')\n","    plt.plot(xaxis, history['val_acc'], 'b-', label='val_acc')\n","    plt.legend()\n","    plt.xlabel('epoch')\n","    plt.ylabel('acc')\n","\n","\n","    plt.figure()\n","    plt.plot(xaxis, history['train_loss'], 'r-', label='train_loss')\n","    plt.plot(xaxis, history['val_loss'], 'b-', label='val_loss')\n","    plt.legend()\n","    plt.xlabel('epoch')\n","    plt.ylabel('loss')\n","\n","    \n","plot_history(history)\n"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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3w178u3mv85z/27fmqq+wmKaJ66aX27fjTT+1u9Oyzxz7HxIl23Jln2s/777cqourV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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"VMfBgjPZZsT6","colab_type":"code","outputId":"0acb32ad-ff93-424c-d969-5443259171db","executionInfo":{"status":"ok","timestamp":1562490271967,"user_tz":-540,"elapsed":1300392,"user":{"displayName":"中嶋拓郎","photoUrl":"","userId":"11259832953657969381"}},"colab":{"base_uri":"https://localhost:8080/","height":238}},"source":["def test(model, classes, device, criterion, test_loader):\n","    outsize = len(classes)\n","    class_correct = list(0. for i in range(outsize))\n","    class_total = list(0. for i in range(outsize))\n","    runnning_loss = 0\n","    with torch.no_grad():\n","        for data in test_loader:\n","            imgs, labels = data\n","            n_batch = imgs.size(0)\n","            imgs, labels = imgs.to(device), labels.to(device)\n","            outputs = model(imgs)\n","            runnning_loss += criterion(outputs, labels).item()\n","            pred = torch.argmax(outputs, 1)\n","            c = (pred == labels).squeeze()\n","            for i in range(n_batch):\n","                label = labels[i]\n","                class_correct[label] += c[i].item()\n","                class_total[label] += 1\n","\n","    for i in range(outsize):\n","        print('Accuracy of %5s : %2d %%' % (\n","            classes[i], 100 * class_correct[i] / class_total[i]))\n","        \n","    test_acc = int(sum(class_correct)/sum(class_total)*100)\n","    test_loss = runnning_loss / len(test_loader)\n","    print('acc  : {:2d}%'.format(test_acc))\n","    print('loss : {:.4f}'.format(test_loss) )\n","\n","\n","    \n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(\"device:\",device)\n","model = model.to(device)\n","test(model, classes, device, criterion, test_loader)"],"execution_count":17,"outputs":[{"output_type":"stream","text":["device: cuda\n","Accuracy of plane : 83 %\n","Accuracy of   car : 91 %\n","Accuracy of  bird : 69 %\n","Accuracy of   cat : 66 %\n","Accuracy of  deer : 75 %\n","Accuracy of   dog : 74 %\n","Accuracy of  frog : 85 %\n","Accuracy of horse : 84 %\n","Accuracy of  ship : 88 %\n","Accuracy of truck : 82 %\n","acc  : 80%\n","loss : 0.9337\n"],"name":"stdout"}]}]}