03_MNIST_GAN.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ref: https://github.com/prcastro/pytorch-gan/blob/master/MNIST%20GAN.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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     "start_time": "2020-01-09T13:38:38.632435Z"
    }
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import math\n",
    "import time\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:38:40.631060Z",
     "start_time": "2020-01-09T13:38:40.628383Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
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    }
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
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     "text": [
      " 99%|█████████▊| 9764864/9912422 [00:10<00:00, 2315227.72it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./mnist_data/MNIST/raw/train-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
      "0it [00:00, ?it/s]\u001b[A"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./mnist_data/MNIST/raw/train-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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      "\n",
      "0it [00:00, ?it/s]\u001b[A"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./mnist_data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "8192it [00:00, 20056.70it/s]            \u001b[A"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./mnist_data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw\n",
      "Processing...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "9920512it [00:30, 2315227.72it/s]                             "
     ]
    }
   ],
   "source": [
    "# download and transform train dataset\n",
    "train_loader = torch.utils.data.DataLoader(datasets.MNIST('./mnist_data', \n",
    "                                                          download=True, \n",
    "                                                          train=True,\n",
    "                                                          transform=transforms.Compose([\n",
    "                                                              transforms.ToTensor(), # first, convert image to PyTorch tensor\n",
    "                                                              transforms.Normalize((0.1307,), (0.3081,)) # normalize inputs\n",
    "                                                          ])), \n",
    "                                           batch_size=batch_size, \n",
    "                                           shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T14:14:15.628162Z",
     "start_time": "2020-01-09T14:14:15.619365Z"
    }
   },
   "outputs": [],
   "source": [
    "# download and transform test dataset\n",
    "test_loader = torch.utils.data.DataLoader(datasets.MNIST('./mnist_data', \n",
    "                                                          download=True, \n",
    "                                                          train=False,\n",
    "                                                          transform=transforms.Compose([\n",
    "                                                              transforms.ToTensor(), # first, convert image to PyTorch tensor\n",
    "                                                              transforms.Normalize((0.1307,), (0.3081,)) # normalize inputs\n",
    "                                                          ])), \n",
    "                                           batch_size=batch_size, \n",
    "                                           shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:38:54.750307Z",
     "start_time": "2020-01-09T13:38:54.726562Z"
    }
   },
   "outputs": [],
   "source": [
    "inputs, classes = next(iter(train_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:38:55.075686Z",
     "start_time": "2020-01-09T13:38:55.070587Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 1, 28, 28])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:38:55.600260Z",
     "start_time": "2020-01-09T13:38:55.596529Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyper-paramters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:38:57.543501Z",
     "start_time": "2020-01-09T13:38:57.540535Z"
    }
   },
   "outputs": [],
   "source": [
    "num_epochs = 100\n",
    "num_batches = len(train_loader)\n",
    "d_steps = 1\n",
    "g_steps = 1\n",
    "learning_rate = 2e-4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:38:57.959745Z",
     "start_time": "2020-01-09T13:38:57.954640Z"
    }
   },
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(Generator, self).__init__()\n",
    "        self.layer1 = nn.Linear(128, 256, bias=True)\n",
    "        self.layer2 = nn.Linear(256, 784, bias=True)\n",
    "        \n",
    "    def forward(self, z):\n",
    "        # z: noise\n",
    "        h1 = F.relu(self.layer1(z))\n",
    "        out = F.tanh(self.layer2(h1))\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:38:58.492871Z",
     "start_time": "2020-01-09T13:38:58.487426Z"
    }
   },
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.layer1 = nn.Linear(784, 256, bias=True)\n",
    "        self.layer2 = nn.Linear(256, 1, bias=True)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # x: generated or real image (10, 1, 28, 28)\n",
    "        x = x.view(x.size(0), -1) # (10, 784)\n",
    "        h1 = F.relu(self.layer1(x))\n",
    "        y = F.sigmoid(self.layer2(h1)) # (10, 1) -> probability\n",
    "        return y.view(y.size(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:38:59.400977Z",
     "start_time": "2020-01-09T13:38:59.394520Z"
    }
   },
   "outputs": [],
   "source": [
    "G = Generator()\n",
    "D = Discriminator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:38:59.879244Z",
     "start_time": "2020-01-09T13:38:59.876663Z"
    }
   },
   "outputs": [],
   "source": [
    "# Binary Cross Entropy Loss(h(x), y), Sigmoid Cross Entropy Loss 함수라고도 불림. -ylogh(x)-(1-y)log(1-h(x))\n",
    "criterion = nn.BCELoss() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:39:00.151796Z",
     "start_time": "2020-01-09T13:39:00.148581Z"
    }
   },
   "outputs": [],
   "source": [
    "d_optimizer = optim.Adam(D.parameters(), lr=learning_rate)\n",
    "g_optimizer = optim.Adam(G.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:39:00.847777Z",
     "start_time": "2020-01-09T13:39:00.408307Z"
    }
   },
   "outputs": [],
   "source": [
    "use_cuda = torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T13:39:08.523534Z",
     "start_time": "2020-01-09T13:39:00.849677Z"
    }
   },
   "outputs": [],
   "source": [
    "if use_cuda:\n",
    "    G = G.cuda()\n",
    "    D = D.cuda()\n",
    "    criterion = criterion.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-09T14:12:18.156131Z",
     "start_time": "2020-01-09T13:40:23.129628Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 100/100, Step: 1500/1875, d_loss: 0.0002, g_loss: 10.9128\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_noise = torch.randn(batch_size, 128).cuda()\n",
    "\n",
    "# create figure for plotting\n",
    "row_num = 2\n",
    "col_num = 5\n",
    "fig, ax = plt.subplots(row_num, col_num, figsize=(6,6))\n",
    "for i, j in itertools.product(range(row_num), range(col_num)):\n",
    "    ax[i,j].get_xaxis().set_visible(False)\n",
    "    ax[i,j].get_yaxis().set_visible(False)\n",
    "\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for t, data in enumerate(train_loader, 0):\n",
    "        x, _ = data # 10, 1, 28, 28 // 10\n",
    "        x = x.cuda()\n",
    "        x_labels = torch.ones(batch_size).cuda()\n",
    "        z, z_labels = torch.randn(batch_size, 128).cuda(), torch.zeros(batch_size).cuda()\n",
    "        \n",
    "        # train Dis\n",
    "        for d_idx in range(d_steps):\n",
    "            d_optimizer.zero_grad()\n",
    "\n",
    "            # train on real\n",
    "            d_real_loss = criterion(D(x), x_labels)\n",
    "\n",
    "            # train on fake\n",
    "            d_fake_loss = criterion(D(G(z)), z_labels)\n",
    "            \n",
    "            d_loss = d_real_loss + d_fake_loss # criterion(D(x), 1) + criterion(D(G(z)), 0)\n",
    "            d_loss.backward()\n",
    "            d_optimizer.step()\n",
    "\n",
    "        # train Gen\n",
    "        for g_idx in range(g_steps):\n",
    "            g_optimizer.zero_grad()\n",
    "            z, z_labels = torch.randn(batch_size, 128).cuda(), torch.ones(batch_size).cuda()\n",
    "            g_loss = criterion(D(G(z)), z_labels) # criterion(D(G(z)), 1)\n",
    "            g_loss.backward()\n",
    "            g_optimizer.step()\n",
    "        \n",
    "        # print status\n",
    "        if (t+1) % 500 == 0:\n",
    "            test_images = G(test_noise)\n",
    "            \n",
    "            for k in range(row_num*col_num):\n",
    "                i = k//5\n",
    "                j = k%5\n",
    "                ax[i,j].cla() # clear the current axis\n",
    "                ax[i,j].imshow(test_images[k,:].data.cpu().numpy().reshape(28,28), cmap='Greys')\n",
    "            \n",
    "            display.clear_output(wait=True)\n",
    "            display.display(plt.gcf()) # get a reference to a current figure\n",
    "            \n",
    "            print(\"Epoch: {}/{}, Step: {}/{}, d_loss: {:.4f}, g_loss: {:.4f}\"\n",
    "                  .format(epoch + 1, num_epochs, t+1, num_batches, d_loss.data.item(), g_loss.data.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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