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numpy as np\n","import matplotlib.pyplot as plt\n","import torch\n","import torchvision.transforms as transforms\n","from torchvision import datasets\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.optim as optim"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xn9jsKvDCLGk","colab_type":"text"},"source":["## Hyperparameter"]},{"cell_type":"code","metadata":{"id":"OQmvQG6pBtVY","colab_type":"code","colab":{}},"source":["device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","batch_size = 64\n","lr = 0.0002\n","num_workers = 0\n","img_shape = (1, 28, 28)\n","g_input_dim = 100\n","num_epochs = 150"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"69Kkd2d8C-qo","colab_type":"text"},"source":["## MNIST Datasets"]},{"cell_type":"code","metadata":{"id":"EFiSvDR-VFfb","colab_type":"code","colab":{}},"source":["transform = transforms.Compose([transforms.ToTensor(),\n","                                transforms.Normalize([0.5], [0.5])])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cbluE45hC8KC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":369,"referenced_widgets":["9520da0d5e3f4a23a61d52bc8617a15f","3d622a6ee30945dc80aa1b89ea39700a","3d79a6828d6f43ccbfd1afb48659422a","934e6f4d257b48da9ec8db0976ed0966","eb1e7a55474746a3acb4f42deb89503d","e57ba13e0af7497785b8abb08278ff88","6ccdb18f402c421797980fd42e624f78","d39a3ac0d6634415868a17c5b8225675","a0db474e982045489251d72f2be4ae9f","87556df0360145b092188431b3c7a489","21785387aef54e0f8ac9af39755abad3","e45baec2ad40455783aeec4161daf480","5b1136fd1846410588b718d163f5d3a0","b41129d073da4581aebe1b4bb3d30a6a","e508b921446b408f92f5d4f338ee6f89","f8c744f069424ec4bf66b4883115d146","6534e5c5bdbe435aa33894b050453872","01ea6b12a41842868ea39ef76a823b82","4df44d3917ae4db19c04c74c84c058e6","002b2d94abca4179bf3ba91c614b2ded","fe1ec4fe3ad74d2c94d472ad4cd900e4","614e8f4062734903a1f84e61b30de0aa","2e3d49e3bccf4d2a912e5b962a983cb6","276af8d51413497daa1d659928b35875","01c0cc25d0ab43f8921087f3148c6fd0","4a7d256e397947ac9272af92ba4d9c7b","cc5bd34a6d3b4e93b2d029736dc4b43c","4824563236724ae080cc7b501ef5beee","079e61480dde4d32b8b16579579a7c3a","f78e71ba66c742d58b25fa6ed5ec19c1","715775b855e6455a82ae54750bbc6746","f138c64cfd8e43be86e8af434679f60b"]},"outputId":"f45c2874-4d69-41e7-a35e-673d13d65166","executionInfo":{"status":"ok","timestamp":1588862218943,"user_tz":-420,"elapsed":2070,"user":{"displayName":"Tuan Nguyen","photoUrl":"","userId":"02876849177809142877"}}},"source":["train_data = datasets.MNIST(root= \"data\", train = True, transform= transform, download= True)\n","\n","train_loader = torch.utils.data.DataLoader(train_data, batch_size= batch_size, num_workers= num_workers)"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9520da0d5e3f4a23a61d52bc8617a15f","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\n","Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a0db474e982045489251d72f2be4ae9f","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6534e5c5bdbe435aa33894b050453872","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"01c0cc25d0ab43f8921087f3148c6fd0","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n","Processing...\n","Done!\n"],"name":"stdout"},{"output_type":"stream","text":["/pytorch/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"XmMSh6wuFbjW","colab_type":"code","outputId":"5f0d22bf-cc02-4e46-ef1e-21582d2024bb","executionInfo":{"status":"ok","timestamp":1588862219761,"user_tz":-420,"elapsed":1178,"user":{"displayName":"Tuan Nguyen","photoUrl":"","userId":"02876849177809142877"}},"colab":{"base_uri":"https://localhost:8080/","height":263}},"source":["def visualize(dataloader):\n","    dataiter = iter(dataloader)\n","    images, labels = dataiter.next()\n","    images = images / 2 + 0.5  \n","    images = images.numpy()\n","    \n","    #get one image in batch\n","    img = np.squeeze(images[5])\n","    fig = plt.figure(figsize = (3,3)) \n","    ax = fig.add_subplot(111)\n","    ax.imshow(img, cmap='gray')\n","\n","visualize(train_loader)"],"execution_count":5,"outputs":[{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 216x216 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"1LGXzs0sHfAy","colab_type":"text"},"source":["## Define the model"]},{"cell_type":"markdown","metadata":{"id":"vxXEUWOKHiHm","colab_type":"text"},"source":["### Generator"]},{"cell_type":"code","metadata":{"id":"oHZOu7h7G_F-","colab_type":"code","colab":{}},"source":["class Generator(nn.Module):\n","    def __init__(self, input_dim, output_dim = 784):\n","        super(Generator, self).__init__()\n","\n","        self.fc1 = nn.Linear(input_dim, 256)\n","        self.fc2 = nn.Linear(256, 512)\n","        self.fc3 = nn.Linear(512, 1024)\n","        self.fc4 = nn.Linear(1024, output_dim)\n","    \n","        self.drop_out = nn.Dropout(0.3)\n","    def forward(self, x):\n","        x = F.leaky_relu(self.fc1(x), 0.2)\n","        x = self.drop_out(x)\n","\n","        x = F.leaky_relu(self.fc2(x), 0.2)\n","        x = self.drop_out(x)\n","\n","        x = F.leaky_relu(self.fc3(x), 0.2)\n","        x = self.drop_out(x)\n","\n","        out = F.tanh(self.fc4(x))\n","        return out"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"RL61KJJRNRgF","colab_type":"text"},"source":["### Discriminator"]},{"cell_type":"code","metadata":{"id":"xBDU1sKbNHTd","colab_type":"code","colab":{}},"source":["class Discriminator(nn.Module):\n","    def __init__(self, input_dim):\n","        super(Discriminator, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, 1024)\n","        self.fc2 = nn.Linear(1024, 512)\n","        self.fc3 = nn.Linear(512, 256)\n","        self.fc4  = nn.Linear(256, 1)\n","\n","        self.dropout = nn.Dropout(0.3)\n","    def forward(self, img):\n","        x = img.view(img.size(0), -1)\n","        x = F.leaky_relu(self.fc1(x), 0.2)\n","        x = self.dropout(x)\n","\n","        x = F.leaky_relu(self.fc2(x), 0.2)\n","        x = self.dropout(x)\n","\n","        x = F.leaky_relu(self.fc3(x), 0.2)\n","        x = self.dropout(x)\n","        out = F.sigmoid(self.fc4(x))\n","        return out"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"r8UbY6YhP_L8","colab_type":"code","outputId":"cb7ad04c-4b1d-4157-8f58-b7e1bf45f042","executionInfo":{"status":"ok","timestamp":1588862513895,"user_tz":-420,"elapsed":10235,"user":{"displayName":"Tuan Nguyen","photoUrl":"","userId":"02876849177809142877"}},"colab":{"base_uri":"https://localhost:8080/","height":255}},"source":["G = Generator(g_input_dim, output_dim= 784).to(device)\n","D = Discriminator(input_dim= 784).to(device)\n","\n","print(G)\n","print(D)"],"execution_count":10,"outputs":[{"output_type":"stream","text":["Generator(\n","  (fc1): Linear(in_features=100, out_features=256, bias=True)\n","  (fc2): Linear(in_features=256, out_features=512, bias=True)\n","  (fc3): Linear(in_features=512, out_features=1024, bias=True)\n","  (fc4): Linear(in_features=1024, out_features=784, bias=True)\n","  (drop_out): Dropout(p=0.3, inplace=False)\n",")\n","Discriminator(\n","  (fc1): Linear(in_features=784, out_features=1024, bias=True)\n","  (fc2): Linear(in_features=1024, out_features=512, bias=True)\n","  (fc3): Linear(in_features=512, out_features=256, bias=True)\n","  (fc4): Linear(in_features=256, out_features=1, bias=True)\n","  (dropout): Dropout(p=0.3, inplace=False)\n",")\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IymvDKaHRg4Z","colab_type":"text"},"source":["## Define Losses and Optimizers"]},{"cell_type":"code","metadata":{"id":"TvXvBIPlRgK3","colab_type":"code","colab":{}},"source":["def real_loss(D_out):\n","    batch_size = D_out.size(0)\n","    labels = torch.ones(batch_size).to(device)\n","    criterion = nn.BCELoss()\n","    # calculate loss\n","    loss = criterion(D_out.squeeze(), labels)\n","    return loss\n","\n","def fake_loss(D_out):\n","    batch_size = D_out.size(0)\n","    labels = torch.zeros(batch_size).to(device) # fake labels = 0\n","    criterion = nn.BCELoss()\n","    # calculate loss\n","    loss = criterion(D_out.squeeze(), labels)\n","    return loss"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dphszSGxQg_-","colab_type":"code","colab":{}},"source":["d_optimizer = optim.Adam(D.parameters(), lr)\n","g_optimizer = optim.Adam(G.parameters(), lr)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WV__as2gUT_J","colab_type":"text"},"source":["## Training"]},{"cell_type":"code","metadata":{"id":"c8Pl7mo4l38Z","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"45f5ae19-68fd-4648-82db-41c527c51456","executionInfo":{"status":"ok","timestamp":1588866466022,"user_tz":-420,"elapsed":3229059,"user":{"displayName":"Tuan Nguyen","photoUrl":"","userId":"02876849177809142877"}}},"source":["D.train()\n","G.train()\n","for epoch in range(num_epochs):\n","    for i, (real_images, _) in enumerate(train_loader):\n","        batch_size = real_images.size(0)\n","        real_images = real_images.to(device)\n","\n","        #TRAIN DISCRIMINATOR\n","        d_optimizer.zero_grad()\n","\n","        #Train with real images\n","        D_real = D(real_images)\n","        d_real_loss = real_loss(D_real)\n","\n","        #Train with fake images\n","        z = np.random.uniform(-1, 1, size= (batch_size, g_input_dim))\n","        z = torch.from_numpy(z).float().to(device)\n","        fake_images = G(z)\n","        D_fake = D(fake_images)\n","        d_fake_loss = fake_loss(D_fake)\n","\n","        #Combine loss\n","        d_loss = d_real_loss + d_fake_loss\n","        d_loss.backward()\n","        d_optimizer.step()\n","\n","        #TRAIN GENERATOR\n","        g_optimizer.zero_grad()\n","\n","        z = np.random.uniform(-1, 1, size= (batch_size, g_input_dim))\n","        z = torch.from_numpy(z).float().to(device)\n","        fake_images = G(z)\n","        D_fake = D(fake_images)\n","        g_loss = real_loss(D_fake)\n","\n","        g_loss.backward()\n","        g_optimizer.step()\n","    \n","    print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n","        epoch+1, num_epochs, d_loss.item(), g_loss.item()))"],"execution_count":13,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1569: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n","  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n","/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1558: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n","  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch [    1/  150] | d_loss: 0.8764 | g_loss: 2.4439\n","Epoch [    2/  150] | d_loss: 1.8278 | g_loss: 0.7024\n","Epoch [    3/  150] | d_loss: 0.1879 | g_loss: 2.8824\n","Epoch [    4/  150] | d_loss: 0.4842 | g_loss: 2.0858\n","Epoch [    5/  150] | d_loss: 0.7363 | g_loss: 2.1272\n","Epoch [    6/  150] | d_loss: 0.6268 | g_loss: 1.3041\n","Epoch [    7/  150] | d_loss: 0.5456 | g_loss: 1.8777\n","Epoch [    8/  150] | d_loss: 1.0571 | g_loss: 2.2855\n","Epoch [    9/  150] | d_loss: 0.6964 | g_loss: 2.3851\n","Epoch [   10/  150] | d_loss: 0.4972 | g_loss: 1.9148\n","Epoch [   11/  150] | d_loss: 0.7918 | g_loss: 1.5752\n","Epoch [   12/  150] | d_loss: 0.8072 | g_loss: 1.7118\n","Epoch [   13/  150] | d_loss: 0.8152 | g_loss: 2.2381\n","Epoch [   14/  150] | d_loss: 0.6802 | g_loss: 1.5932\n","Epoch [   15/  150] | d_loss: 1.0210 | g_loss: 1.6635\n","Epoch [   16/  150] | d_loss: 1.0705 | g_loss: 1.4667\n","Epoch [   17/  150] | d_loss: 0.8479 | g_loss: 1.5240\n","Epoch [   18/  150] | d_loss: 0.9351 | g_loss: 1.3650\n","Epoch [   19/  150] | d_loss: 0.7555 | g_loss: 1.2856\n","Epoch [   20/  150] | d_loss: 0.8619 | g_loss: 1.8965\n","Epoch [   21/  150] | d_loss: 0.9728 | g_loss: 1.2759\n","Epoch [   22/  150] | d_loss: 0.9065 | g_loss: 1.2764\n","Epoch [   23/  150] | d_loss: 1.1061 | g_loss: 1.5054\n","Epoch [   24/  150] | d_loss: 0.8599 | g_loss: 1.1604\n","Epoch [   25/  150] | d_loss: 0.9709 | g_loss: 1.3692\n","Epoch [   26/  150] | d_loss: 0.7699 | g_loss: 1.6618\n","Epoch [   27/  150] | d_loss: 0.9167 | g_loss: 1.3868\n","Epoch [   28/  150] | d_loss: 0.7279 | g_loss: 1.3786\n","Epoch [   29/  150] | d_loss: 0.9208 | g_loss: 1.2243\n","Epoch [   30/  150] | d_loss: 1.2218 | g_loss: 0.9136\n","Epoch [   31/  150] | d_loss: 0.9612 | g_loss: 1.4310\n","Epoch [   32/  150] | d_loss: 0.9649 | g_loss: 1.0112\n","Epoch [   33/  150] | d_loss: 0.9960 | g_loss: 1.0064\n","Epoch [   34/  150] | d_loss: 0.7862 | g_loss: 1.0683\n","Epoch [   35/  150] | d_loss: 0.9115 | g_loss: 1.0085\n","Epoch [   36/  150] | d_loss: 1.1719 | g_loss: 0.8853\n","Epoch [   37/  150] | d_loss: 0.8700 | g_loss: 1.3290\n","Epoch [   38/  150] | d_loss: 0.9710 | g_loss: 1.1540\n","Epoch [   39/  150] | d_loss: 0.6761 | g_loss: 1.2689\n","Epoch [   40/  150] | d_loss: 0.9397 | g_loss: 1.1073\n","Epoch [   41/  150] | d_loss: 0.8156 | g_loss: 1.4159\n","Epoch [   42/  150] | d_loss: 1.0600 | g_loss: 1.1730\n","Epoch [   43/  150] | d_loss: 0.9722 | g_loss: 1.0481\n","Epoch [   44/  150] | d_loss: 0.8431 | g_loss: 1.0981\n","Epoch [   45/  150] | d_loss: 1.0231 | g_loss: 1.3930\n","Epoch [   46/  150] | d_loss: 0.9492 | g_loss: 1.1018\n","Epoch [   47/  150] | d_loss: 1.0940 | g_loss: 1.3013\n","Epoch [   48/  150] | d_loss: 0.7276 | g_loss: 1.2372\n","Epoch [   49/  150] | d_loss: 0.9311 | g_loss: 1.3807\n","Epoch [   50/  150] | d_loss: 0.8400 | g_loss: 1.6492\n","Epoch [   51/  150] | d_loss: 0.8194 | g_loss: 1.3630\n","Epoch [   52/  150] | d_loss: 1.0610 | g_loss: 1.6015\n","Epoch [   53/  150] | d_loss: 0.8794 | g_loss: 1.1706\n","Epoch [   54/  150] | d_loss: 0.8633 | g_loss: 1.0245\n","Epoch [   55/  150] | d_loss: 0.8070 | g_loss: 1.1324\n","Epoch [   56/  150] | d_loss: 0.7140 | g_loss: 1.2926\n","Epoch [   57/  150] | d_loss: 0.9280 | g_loss: 1.2202\n","Epoch [   58/  150] | d_loss: 1.0082 | g_loss: 1.2478\n","Epoch [   59/  150] | d_loss: 0.8593 | g_loss: 1.2014\n","Epoch [   60/  150] | d_loss: 0.8565 | g_loss: 1.2940\n","Epoch [   61/  150] | d_loss: 0.8782 | g_loss: 1.4012\n","Epoch [   62/  150] | d_loss: 0.8668 | g_loss: 1.4516\n","Epoch [   63/  150] | d_loss: 0.6511 | g_loss: 1.3823\n","Epoch [   64/  150] | d_loss: 0.9733 | g_loss: 1.3761\n","Epoch [   65/  150] | d_loss: 0.9483 | g_loss: 1.2410\n","Epoch [   66/  150] | d_loss: 0.9285 | g_loss: 0.9628\n","Epoch [   67/  150] | d_loss: 0.9908 | g_loss: 0.9746\n","Epoch [   68/  150] | d_loss: 0.8167 | g_loss: 1.2159\n","Epoch [   69/  150] | d_loss: 0.8571 | g_loss: 1.3029\n","Epoch [   70/  150] | d_loss: 1.0172 | g_loss: 0.9844\n","Epoch [   71/  150] | d_loss: 0.7529 | g_loss: 1.2719\n","Epoch [   72/  150] | d_loss: 0.8140 | g_loss: 1.1633\n","Epoch [   73/  150] | d_loss: 0.9277 | g_loss: 1.3153\n","Epoch [   74/  150] | d_loss: 0.8057 | g_loss: 1.1768\n","Epoch [   75/  150] | d_loss: 0.9072 | g_loss: 1.7114\n","Epoch [   76/  150] | d_loss: 0.7280 | g_loss: 1.2146\n","Epoch [   77/  150] | d_loss: 0.7507 | g_loss: 1.4005\n","Epoch [   78/  150] | d_loss: 0.8226 | g_loss: 1.2327\n","Epoch [   79/  150] | d_loss: 0.9470 | g_loss: 1.2056\n","Epoch [   80/  150] | d_loss: 0.8570 | g_loss: 1.0973\n","Epoch [   81/  150] | d_loss: 0.7713 | g_loss: 1.2248\n","Epoch [   82/  150] | d_loss: 0.8217 | g_loss: 1.4946\n","Epoch [   83/  150] | d_loss: 0.8597 | g_loss: 1.4867\n","Epoch [   84/  150] | d_loss: 0.8248 | g_loss: 1.3803\n","Epoch [   85/  150] | d_loss: 0.7931 | g_loss: 1.0231\n","Epoch [   86/  150] | d_loss: 0.8653 | g_loss: 0.8601\n","Epoch [   87/  150] | d_loss: 1.1850 | g_loss: 1.4289\n","Epoch [   88/  150] | d_loss: 0.7755 | g_loss: 1.2154\n","Epoch [   89/  150] | d_loss: 0.8251 | g_loss: 1.0600\n","Epoch [   90/  150] | d_loss: 0.6481 | g_loss: 1.2738\n","Epoch [   91/  150] | d_loss: 1.0983 | g_loss: 1.1913\n","Epoch [   92/  150] | d_loss: 0.6879 | g_loss: 1.1787\n","Epoch [   93/  150] | d_loss: 0.9900 | g_loss: 1.2652\n","Epoch [   94/  150] | d_loss: 0.7457 | g_loss: 1.2627\n","Epoch [   95/  150] | d_loss: 0.9590 | g_loss: 1.5165\n","Epoch [   96/  150] | d_loss: 0.7866 | g_loss: 1.5829\n","Epoch [   97/  150] | d_loss: 0.7647 | g_loss: 1.3108\n","Epoch [   98/  150] | d_loss: 0.8989 | g_loss: 1.2145\n","Epoch [   99/  150] | d_loss: 0.8650 | g_loss: 1.9977\n","Epoch [  100/  150] | d_loss: 0.8090 | g_loss: 1.2783\n","Epoch [  101/  150] | d_loss: 0.8127 | g_loss: 1.0363\n","Epoch [  102/  150] | d_loss: 0.6571 | g_loss: 1.2602\n","Epoch [  103/  150] | d_loss: 0.7922 | g_loss: 1.2223\n","Epoch [  104/  150] | d_loss: 0.9175 | g_loss: 1.4137\n","Epoch [  105/  150] | d_loss: 1.0309 | g_loss: 1.2151\n","Epoch [  106/  150] | d_loss: 0.7321 | g_loss: 1.3659\n","Epoch [  107/  150] | d_loss: 0.9802 | g_loss: 1.4516\n","Epoch [  108/  150] | d_loss: 0.7575 | g_loss: 1.2947\n","Epoch [  109/  150] | d_loss: 0.9046 | g_loss: 1.2412\n","Epoch [  110/  150] | d_loss: 0.8870 | g_loss: 1.5795\n","Epoch [  111/  150] | d_loss: 0.7963 | g_loss: 1.3614\n","Epoch [  112/  150] | d_loss: 0.9973 | g_loss: 1.2598\n","Epoch [  113/  150] | d_loss: 0.8343 | g_loss: 1.0489\n","Epoch [  114/  150] | d_loss: 0.8364 | g_loss: 1.6433\n","Epoch [  115/  150] | d_loss: 0.9212 | g_loss: 1.3757\n","Epoch [  116/  150] | d_loss: 0.7105 | g_loss: 1.3313\n","Epoch [  117/  150] | d_loss: 0.9631 | g_loss: 1.1304\n","Epoch [  118/  150] | d_loss: 0.8337 | g_loss: 1.3024\n","Epoch [  119/  150] | d_loss: 0.7182 | g_loss: 1.5609\n","Epoch [  120/  150] | d_loss: 1.0100 | g_loss: 1.2873\n","Epoch [  121/  150] | d_loss: 0.9674 | g_loss: 1.5260\n","Epoch [  122/  150] | d_loss: 0.6355 | g_loss: 1.1481\n","Epoch [  123/  150] | d_loss: 0.8299 | g_loss: 1.0643\n","Epoch [  124/  150] | d_loss: 0.7190 | g_loss: 1.2853\n","Epoch [  125/  150] | d_loss: 0.7381 | g_loss: 1.4197\n","Epoch [  126/  150] | d_loss: 0.8296 | g_loss: 1.2904\n","Epoch [  127/  150] | d_loss: 0.9417 | g_loss: 1.3133\n","Epoch [  128/  150] | d_loss: 0.7970 | g_loss: 0.9305\n","Epoch [  129/  150] | d_loss: 0.8448 | g_loss: 1.1681\n","Epoch [  130/  150] | d_loss: 0.8897 | g_loss: 1.7744\n","Epoch [  131/  150] | d_loss: 1.2053 | g_loss: 1.3046\n","Epoch [  132/  150] | d_loss: 0.8754 | g_loss: 1.3368\n","Epoch [  133/  150] | d_loss: 0.8102 | g_loss: 1.1408\n","Epoch [  134/  150] | d_loss: 0.9557 | g_loss: 1.1217\n","Epoch [  135/  150] | d_loss: 0.8983 | g_loss: 0.8896\n","Epoch [  136/  150] | d_loss: 0.8297 | g_loss: 1.5052\n","Epoch [  137/  150] | d_loss: 0.7835 | g_loss: 1.4743\n","Epoch [  138/  150] | d_loss: 0.8917 | g_loss: 1.5579\n","Epoch [  139/  150] | d_loss: 0.7743 | g_loss: 1.5177\n","Epoch [  140/  150] | d_loss: 0.7342 | g_loss: 1.5716\n","Epoch [  141/  150] | d_loss: 0.8415 | g_loss: 1.0566\n","Epoch [  142/  150] | d_loss: 0.9475 | g_loss: 1.2881\n","Epoch [  143/  150] | d_loss: 0.8211 | g_loss: 1.2566\n","Epoch [  144/  150] | d_loss: 0.8637 | g_loss: 1.1657\n","Epoch [  145/  150] | d_loss: 0.8003 | g_loss: 1.0777\n","Epoch [  146/  150] | d_loss: 0.8143 | g_loss: 1.8410\n","Epoch [  147/  150] | d_loss: 1.0268 | g_loss: 1.5893\n","Epoch [  148/  150] | d_loss: 0.9351 | g_loss: 1.0696\n","Epoch [  149/  150] | d_loss: 0.7703 | g_loss: 1.5321\n","Epoch [  150/  150] | d_loss: 0.9693 | g_loss: 1.6625\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"dG__F20N1F1K","colab_type":"text"},"source":["## Evaluation"]},{"cell_type":"code","metadata":{"id":"-Sxg-sPbiKVX","colab_type":"code","colab":{}},"source":["def view_samples(epoch, samples):\n","    fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True)\n","    for ax, img in zip(axes.flatten(), samples[epoch]):\n","        img = img.detach()\n","        ax.xaxis.set_visible(False)\n","        ax.yaxis.set_visible(False)\n","        im = ax.imshow(img.reshape((28,28)).to(\"cpu\"), cmap='Greys_r')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tH9RZrP21FHB","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":449},"outputId":"73d70879-2fd2-4a7c-e76e-98e475289b1c","executionInfo":{"status":"ok","timestamp":1588866870956,"user_tz":-420,"elapsed":1876,"user":{"displayName":"Tuan Nguyen","photoUrl":"","userId":"02876849177809142877"}}},"source":["# randomly generated, new latent vectors\n","sample_size=20\n","rand_z = np.random.uniform(-1, 1, size=(sample_size, g_input_dim))\n","rand_z = torch.from_numpy(rand_z).float().to(device)\n","\n","G.eval() # eval mode\n","# generated samples\n","rand_images = G(rand_z)\n","view_samples(0, [rand_images])"],"execution_count":22,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1558: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n","  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 504x504 with 16 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}