pytorch_ipynb/cnn/cnn-resnet34-celeba-dataparallel.ipynb

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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UEBilEjLj5wY"
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   "source": [
    "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
    "- Author: Sebastian Raschka\n",
    "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 536,
     "status": "ok",
     "timestamp": 1524974472601,
     "user": {
      "displayName": "Sebastian Raschka",
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     "text": [
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.7.1\n",
      "IPython 7.2.0\n",
      "\n",
      "torch 1.0.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rH4XmErYj5wm"
   },
   "source": [
    "# Model Zoo -- CNN Gender Classifier (ResNet-34 Architecture, CelebA) with Data Parallelism"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The network in this notebook is an implementation of the ResNet-34 [1] architecture on the CelebA face dataset [2] to train a gender classifier.  \n",
    "\n",
    "\n",
    "References\n",
    "    \n",
    "- [1] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). ([CVPR Link](https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html))\n",
    "\n",
    "- [2] Zhang, K., Tan, L., Li, Z., & Qiao, Y. (2016). Gender and smile classification using deep convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 34-38).\n",
    "\n",
    "\n",
    "![](../images/resnets/resnet34/resnet34-arch.png)\n",
    "\n",
    "**Note that the CelebA images are 218 x 178, not 256 x 256. We resize to 128x128**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following figure illustrates residual blocks with skip connections such that the input passed via the shortcut matches the dimensions of the main path's output, which allows the network to learn identity functions.\n",
    "\n",
    "![](../images/resnets/resnet-ex-1-1.png)\n",
    "\n",
    "\n",
    "The ResNet-34 architecture actually uses residual blocks with skip connections such that the input passed via the shortcut matches is resized to dimensions of the main path's output. Such a residual block is illustrated below:\n",
    "\n",
    "![](../images/resnets/resnet-ex-1-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a more detailed explanation see the other notebook, [resnet-ex-1.ipynb](resnet-ex-1.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MkoGLH_Tj5wn"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "ORj09gnrj5wp"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 23936,
     "status": "ok",
     "timestamp": 1524974497505,
     "user": {
      "displayName": "Sebastian Raschka",
      "photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
      "userId": "118404394130788869227"
     },
     "user_tz": 240
    },
    "id": "NnT0sZIwj5wu",
    "outputId": "55aed925-d17e-4c6a-8c71-0d9b3bde5637"
   },
   "outputs": [],
   "source": [
    "##########################\n",
    "### SETTINGS\n",
    "##########################\n",
    "\n",
    "# Hyperparameters\n",
    "RANDOM_SEED = 1\n",
    "LEARNING_RATE = 0.001\n",
    "NUM_EPOCHS = 10\n",
    "\n",
    "# Architecture\n",
    "NUM_FEATURES = 128*128\n",
    "NUM_CLASSES = 2\n",
    "BATCH_SIZE = 256*torch.cuda.device_count()\n",
    "DEVICE = 'cuda:0' # default GPU device\n",
    "GRAYSCALE = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the ~200,000 CelebA face image dataset is relatively large (~1.3 Gb). The download link provided below was provided by the author on the official CelebA website at http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Download and unzip the file `img_align_celeba.zip`, which contains the images in jpeg format.\n",
    "\n",
    "2) Download the `list_attr_celeba.txt` file, which contains the class labels\n",
    "\n",
    "3) Download the `list_eval_partition.txt` file, which contains  training/validation/test partitioning info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "            Male\n",
       "000001.jpg     0\n",
       "000002.jpg     0\n",
       "000003.jpg     1\n",
       "000004.jpg     0\n",
       "000005.jpg     0"
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   "source": [
    "df1 = pd.read_csv('list_attr_celeba.txt', sep=\"\\s+\", skiprows=1, usecols=['Male'])\n",
    "\n",
    "# Make 0 (female) & 1 (male) labels instead of -1 & 1\n",
    "df1.loc[df1['Male'] == -1, 'Male'] = 0\n",
    "\n",
    "df1.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Partition</th>\n",
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       "      <th>Filename</th>\n",
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      "text/plain": [
       "            Partition\n",
       "Filename             \n",
       "000001.jpg          0\n",
       "000002.jpg          0\n",
       "000003.jpg          0\n",
       "000004.jpg          0\n",
       "000005.jpg          0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('list_eval_partition.txt', sep=\"\\s+\", skiprows=0, header=None)\n",
    "df2.columns = ['Filename', 'Partition']\n",
    "df2 = df2.set_index('Filename')\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Male</th>\n",
       "      <th>Partition</th>\n",
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      "text/plain": [
       "            Male  Partition\n",
       "000001.jpg     0          0\n",
       "000002.jpg     0          0\n",
       "000003.jpg     1          0\n",
       "000004.jpg     0          0\n",
       "000005.jpg     0          0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df1.merge(df2, left_index=True, right_index=True)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Male</th>\n",
       "      <th>Partition</th>\n",
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      "text/plain": [
       "            Male  Partition\n",
       "000001.jpg     0          0\n",
       "000002.jpg     0          0\n",
       "000003.jpg     1          0\n",
       "000004.jpg     0          0\n",
       "000005.jpg     0          0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.to_csv('celeba-gender-partitions.csv')\n",
    "df4 = pd.read_csv('celeba-gender-partitions.csv', index_col=0)\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4.loc[df4['Partition'] == 0].to_csv('celeba-gender-train.csv')\n",
    "df4.loc[df4['Partition'] == 1].to_csv('celeba-gender-valid.csv')\n",
    "df4.loc[df4['Partition'] == 2].to_csv('celeba-gender-test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(218, 178, 3)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = Image.open('img_align_celeba/000001.jpg')\n",
    "print(np.asarray(img, dtype=np.uint8).shape)\n",
    "plt.imshow(img);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing a Custom DataLoader Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CelebaDataset(Dataset):\n",
    "    \"\"\"Custom Dataset for loading CelebA face images\"\"\"\n",
    "\n",
    "    def __init__(self, csv_path, img_dir, transform=None):\n",
    "    \n",
    "        df = pd.read_csv(csv_path, index_col=0)\n",
    "        self.img_dir = img_dir\n",
    "        self.csv_path = csv_path\n",
    "        self.img_names = df.index.values\n",
    "        self.y = df['Male'].values\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = Image.open(os.path.join(self.img_dir,\n",
    "                                      self.img_names[index]))\n",
    "        \n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        \n",
    "        label = self.y[index]\n",
    "        return img, label\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.y.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that transforms.ToTensor()\n",
    "# already divides pixels by 255. internally\n",
    "\n",
    "custom_transform = transforms.Compose([transforms.CenterCrop((178, 178)),\n",
    "                                       transforms.Resize((128, 128)),\n",
    "                                       #transforms.Grayscale(),                                       \n",
    "                                       #transforms.Lambda(lambda x: x/255.),\n",
    "                                       transforms.ToTensor()])\n",
    "\n",
    "train_dataset = CelebaDataset(csv_path='celeba-gender-train.csv',\n",
    "                              img_dir='img_align_celeba/',\n",
    "                              transform=custom_transform)\n",
    "\n",
    "valid_dataset = CelebaDataset(csv_path='celeba-gender-valid.csv',\n",
    "                              img_dir='img_align_celeba/',\n",
    "                              transform=custom_transform)\n",
    "\n",
    "test_dataset = CelebaDataset(csv_path='celeba-gender-test.csv',\n",
    "                             img_dir='img_align_celeba/',\n",
    "                             transform=custom_transform)\n",
    "\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset,\n",
    "                          batch_size=BATCH_SIZE,\n",
    "                          shuffle=True,\n",
    "                          num_workers=4)\n",
    "\n",
    "valid_loader = DataLoader(dataset=valid_dataset,\n",
    "                          batch_size=BATCH_SIZE,\n",
    "                          shuffle=False,\n",
    "                          num_workers=4)\n",
    "\n",
    "test_loader = DataLoader(dataset=test_dataset,\n",
    "                         batch_size=BATCH_SIZE,\n",
    "                         shuffle=False,\n",
    "                         num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | Batch index: 0 | Batch size: 1024\n",
      "Epoch: 2 | Batch index: 0 | Batch size: 1024\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(0)\n",
    "\n",
    "for epoch in range(2):\n",
    "\n",
    "    for batch_idx, (x, y) in enumerate(train_loader):\n",
    "        \n",
    "        print('Epoch:', epoch+1, end='')\n",
    "        print(' | Batch index:', batch_idx, end='')\n",
    "        print(' | Batch size:', y.size()[0])\n",
    "        \n",
    "        x = x.to(DEVICE)\n",
    "        y = y.to(DEVICE)\n",
    "        time.sleep(1)\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I6hghKPxj5w0"
   },
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code cell that implements the ResNet-34 architecture is a derivative of the code provided at https://pytorch.org/docs/0.4.0/_modules/torchvision/models/resnet.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################\n",
    "### MODEL\n",
    "##########################\n",
    "\n",
    "\n",
    "def conv3x3(in_planes, out_planes, stride=1):\n",
    "    \"\"\"3x3 convolution with padding\"\"\"\n",
    "    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n",
    "                     padding=1, bias=False)\n",
    "\n",
    "\n",
    "class BasicBlock(nn.Module):\n",
    "    expansion = 1\n",
    "\n",
    "    def __init__(self, inplanes, planes, stride=1, downsample=None):\n",
    "        super(BasicBlock, self).__init__()\n",
    "        self.conv1 = conv3x3(inplanes, planes, stride)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.conv2 = conv3x3(planes, planes)\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.downsample = downsample\n",
    "        self.stride = stride\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        out += residual\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "class ResNet(nn.Module):\n",
    "\n",
    "    def __init__(self, block, layers, num_classes, grayscale):\n",
    "        self.inplanes = 64\n",
    "        if grayscale:\n",
    "            in_dim = 1\n",
    "        else:\n",
    "            in_dim = 3\n",
    "        super(ResNet, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_dim, 64, kernel_size=7, stride=2, padding=3,\n",
    "                               bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "        self.layer1 = self._make_layer(block, 64, layers[0])\n",
    "        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n",
    "        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n",
    "        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n",
    "        self.avgpool = nn.AvgPool2d(7, stride=1, padding=2)\n",
    "        self.fc = nn.Linear(2048 * block.expansion, num_classes)\n",
    "\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n",
    "                m.weight.data.normal_(0, (2. / n)**.5)\n",
    "            elif isinstance(m, nn.BatchNorm2d):\n",
    "                m.weight.data.fill_(1)\n",
    "                m.bias.data.zero_()\n",
    "\n",
    "    def _make_layer(self, block, planes, blocks, stride=1):\n",
    "        downsample = None\n",
    "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
    "            downsample = nn.Sequential(\n",
    "                nn.Conv2d(self.inplanes, planes * block.expansion,\n",
    "                          kernel_size=1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(planes * block.expansion),\n",
    "            )\n",
    "\n",
    "        layers = []\n",
    "        layers.append(block(self.inplanes, planes, stride, downsample))\n",
    "        self.inplanes = planes * block.expansion\n",
    "        for i in range(1, blocks):\n",
    "            layers.append(block(self.inplanes, planes))\n",
    "\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.maxpool(x)\n",
    "\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        x = self.layer4(x)\n",
    "\n",
    "        x = self.avgpool(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        logits = self.fc(x)\n",
    "        probas = F.softmax(logits, dim=1)\n",
    "        return logits, probas\n",
    "\n",
    "\n",
    "\n",
    "def resnet34(num_classes, grayscale):\n",
    "    \"\"\"Constructs a ResNet-34 model.\"\"\"\n",
    "    model = ResNet(block=BasicBlock, \n",
    "                   layers=[3, 4, 6, 3],\n",
    "                   num_classes=NUM_CLASSES,\n",
    "                   grayscale=grayscale)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "_lza9t_uj5w1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 4 GPUs\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(RANDOM_SEED)\n",
    "\n",
    "##########################\n",
    "### COST AND OPTIMIZER\n",
    "##########################\n",
    "\n",
    "\n",
    "\n",
    "model = resnet34(NUM_CLASSES, GRAYSCALE)\n",
    "\n",
    "\n",
    "#### DATA PARALLEL START ####\n",
    "if torch.cuda.device_count() > 1:\n",
    "    print(\"Using\", torch.cuda.device_count(), \"GPUs\")\n",
    "    model = nn.DataParallel(model)\n",
    "#### DATA PARALLEL END ####\n",
    "\n",
    "\n",
    "model.to(DEVICE)\n",
    "\n",
    "\n",
    "\n",
    "cost_fn = torch.nn.CrossEntropyLoss()  \n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RAodboScj5w6"
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 1547
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2384585,
     "status": "ok",
     "timestamp": 1524976888520,
     "user": {
      "displayName": "Sebastian Raschka",
      "photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
      "userId": "118404394130788869227"
     },
     "user_tz": 240
    },
    "id": "Dzh3ROmRj5w7",
    "outputId": "5f8fd8c9-b076-403a-b0b7-fd2d498b48d7",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/010 | Batch 0000/0159 | Cost: 0.6839\n",
      "Epoch: 001/010 | Batch 0050/0159 | Cost: 0.1407\n",
      "Epoch: 001/010 | Batch 0100/0159 | Cost: 0.0978\n",
      "Epoch: 001/010 | Batch 0150/0159 | Cost: 0.1101\n",
      "Epoch: 001/010 | Train: 96.633% | Valid: 96.960%\n",
      "Time elapsed: 2.80 min\n",
      "Epoch: 002/010 | Batch 0000/0159 | Cost: 0.0683\n",
      "Epoch: 002/010 | Batch 0050/0159 | Cost: 0.0705\n",
      "Epoch: 002/010 | Batch 0100/0159 | Cost: 0.0884\n",
      "Epoch: 002/010 | Batch 0150/0159 | Cost: 0.0798\n",
      "Epoch: 002/010 | Train: 97.172% | Valid: 97.242%\n",
      "Time elapsed: 5.45 min\n",
      "Epoch: 003/010 | Batch 0000/0159 | Cost: 0.0563\n",
      "Epoch: 003/010 | Batch 0050/0159 | Cost: 0.0755\n",
      "Epoch: 003/010 | Batch 0100/0159 | Cost: 0.0648\n",
      "Epoch: 003/010 | Batch 0150/0159 | Cost: 0.0443\n",
      "Epoch: 003/010 | Train: 97.288% | Valid: 97.227%\n",
      "Time elapsed: 8.12 min\n",
      "Epoch: 004/010 | Batch 0000/0159 | Cost: 0.0342\n",
      "Epoch: 004/010 | Batch 0050/0159 | Cost: 0.0599\n",
      "Epoch: 004/010 | Batch 0100/0159 | Cost: 0.0639\n",
      "Epoch: 004/010 | Batch 0150/0159 | Cost: 0.0566\n",
      "Epoch: 004/010 | Train: 98.357% | Valid: 97.569%\n",
      "Time elapsed: 10.75 min\n",
      "Epoch: 005/010 | Batch 0000/0159 | Cost: 0.0293\n",
      "Epoch: 005/010 | Batch 0050/0159 | Cost: 0.0469\n",
      "Epoch: 005/010 | Batch 0100/0159 | Cost: 0.0486\n",
      "Epoch: 005/010 | Batch 0150/0159 | Cost: 0.0365\n",
      "Epoch: 005/010 | Train: 99.018% | Valid: 98.022%\n",
      "Time elapsed: 13.40 min\n",
      "Epoch: 006/010 | Batch 0000/0159 | Cost: 0.0246\n",
      "Epoch: 006/010 | Batch 0050/0159 | Cost: 0.0362\n",
      "Epoch: 006/010 | Batch 0100/0159 | Cost: 0.0323\n",
      "Epoch: 006/010 | Batch 0150/0159 | Cost: 0.0385\n",
      "Epoch: 006/010 | Train: 98.730% | Valid: 97.770%\n",
      "Time elapsed: 16.07 min\n",
      "Epoch: 007/010 | Batch 0000/0159 | Cost: 0.0220\n",
      "Epoch: 007/010 | Batch 0050/0159 | Cost: 0.0279\n",
      "Epoch: 007/010 | Batch 0100/0159 | Cost: 0.0452\n",
      "Epoch: 007/010 | Batch 0150/0159 | Cost: 0.0219\n",
      "Epoch: 007/010 | Train: 99.188% | Valid: 97.926%\n",
      "Time elapsed: 18.76 min\n",
      "Epoch: 008/010 | Batch 0000/0159 | Cost: 0.0168\n",
      "Epoch: 008/010 | Batch 0050/0159 | Cost: 0.0163\n",
      "Epoch: 008/010 | Batch 0100/0159 | Cost: 0.0276\n",
      "Epoch: 008/010 | Batch 0150/0159 | Cost: 0.0455\n",
      "Epoch: 008/010 | Train: 99.456% | Valid: 98.072%\n",
      "Time elapsed: 21.43 min\n",
      "Epoch: 009/010 | Batch 0000/0159 | Cost: 0.0104\n",
      "Epoch: 009/010 | Batch 0050/0159 | Cost: 0.0184\n",
      "Epoch: 009/010 | Batch 0100/0159 | Cost: 0.0105\n",
      "Epoch: 009/010 | Batch 0150/0159 | Cost: 0.0305\n",
      "Epoch: 009/010 | Train: 98.984% | Valid: 97.866%\n",
      "Time elapsed: 24.11 min\n",
      "Epoch: 010/010 | Batch 0000/0159 | Cost: 0.0106\n",
      "Epoch: 010/010 | Batch 0050/0159 | Cost: 0.0228\n",
      "Epoch: 010/010 | Batch 0100/0159 | Cost: 0.0123\n",
      "Epoch: 010/010 | Batch 0150/0159 | Cost: 0.0137\n",
      "Epoch: 010/010 | Train: 99.512% | Valid: 97.836%\n",
      "Time elapsed: 26.75 min\n",
      "Total Training Time: 26.75 min\n"
     ]
    }
   ],
   "source": [
    "def compute_accuracy(model, data_loader, device):\n",
    "    correct_pred, num_examples = 0, 0\n",
    "    for i, (features, targets) in enumerate(data_loader):\n",
    "            \n",
    "        features = features.to(device)\n",
    "        targets = targets.to(device)\n",
    "\n",
    "        logits, probas = model(features)\n",
    "        _, predicted_labels = torch.max(probas, 1)\n",
    "        num_examples += targets.size(0)\n",
    "        correct_pred += (predicted_labels == targets).sum()\n",
    "    return correct_pred.float()/num_examples * 100\n",
    "    \n",
    "\n",
    "start_time = time.time()\n",
    "for epoch in range(NUM_EPOCHS):\n",
    "    \n",
    "    model.train()\n",
    "    for batch_idx, (features, targets) in enumerate(train_loader):\n",
    "        \n",
    "        features = features.to(DEVICE)\n",
    "        targets = targets.to(DEVICE)\n",
    "            \n",
    "        ### FORWARD AND BACK PROP\n",
    "        logits, probas = model(features)\n",
    "        cost = cost_fn(logits, targets)\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        cost.backward()\n",
    "        \n",
    "        ### UPDATE MODEL PARAMETERS\n",
    "        optimizer.step()\n",
    "        \n",
    "        ### LOGGING\n",
    "        if not batch_idx % 50:\n",
    "            print ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f' \n",
    "                   %(epoch+1, NUM_EPOCHS, batch_idx, \n",
    "                     len(train_loader), cost))\n",
    "\n",
    "        \n",
    "\n",
    "    model.eval()\n",
    "    with torch.set_grad_enabled(False): # save memory during inference\n",
    "        print('Epoch: %03d/%03d | Train: %.3f%% | Valid: %.3f%%' % (\n",
    "              epoch+1, NUM_EPOCHS, \n",
    "              compute_accuracy(model, train_loader, device=DEVICE),\n",
    "              compute_accuracy(model, valid_loader, device=DEVICE)))\n",
    "        \n",
    "    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
    "    \n",
    "print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "paaeEQHQj5xC"
   },
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6514,
     "status": "ok",
     "timestamp": 1524976895054,
     "user": {
      "displayName": "Sebastian Raschka",
      "photoUrl": "//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg",
      "userId": "118404394130788869227"
     },
     "user_tz": 240
    },
    "id": "gzQMWKq5j5xE",
    "outputId": "de7dc005-5eeb-4177-9f9f-d9b5d1358db9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 97.56%\n"
     ]
    }
   ],
   "source": [
    "with torch.set_grad_enabled(False): # save memory during inference\n",
    "    print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader, device=DEVICE)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9e6145d278>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for batch_idx, (features, targets) in enumerate(test_loader):\n",
    "\n",
    "    features = features\n",
    "    targets = targets\n",
    "    break\n",
    "    \n",
    "plt.imshow(np.transpose(features[0], (1, 2, 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability Female 100.00%\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "logits, probas = model(features.to(DEVICE)[0, None])\n",
    "print('Probability Female %.2f%%' % (probas[0][0]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy       1.15.4\n",
      "pandas      0.23.4\n",
      "torch       1.0.0\n",
      "PIL.Image   5.3.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -iv"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "default_view": {},
   "name": "convnet-vgg16.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "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": "3.7.1"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "371px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}