RESNET_34_accuracy_91_96.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "RESNET_34_80_SGD_decay_normalized_dropout_0.1_91.96.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "854509dhu0c0",
        "colab_type": "text"
      },
      "source": [
        "# RESNET on CIFAR 10 dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_ECc7odu9yt",
        "colab_type": "text"
      },
      "source": [
        "## Library Installation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJZZ9hNj7Ys1",
        "colab_type": "code",
        "outputId": "11d75499-dd2c-451c-fe29-a3ca6c53e06d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!pip install ipython-autotime\n",
        "%load_ext autotime\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "\n",
        "# Device configuration\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: ipython-autotime in /usr/local/lib/python3.6/dist-packages (0.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIJHngBPvFqg",
        "colab_type": "text"
      },
      "source": [
        "## Hyper-parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixieggsE7eQR",
        "colab_type": "code",
        "outputId": "5f1d44cd-4393-485f-9a0f-28aac4589121",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Hyper-parameters\n",
        "num_epochs = 80\n",
        "learning_rate = 0.01"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.56 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6gNdKzZKvKZO",
        "colab_type": "text"
      },
      "source": [
        "## Image preprocessing modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RJoy91C7eTd",
        "colab_type": "code",
        "outputId": "a2f159cb-9bbd-4679-cb3f-ac45d3af950d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Image preprocessing modules\n",
        "transform = transforms.Compose([\n",
        "    transforms.Pad(4),\n",
        "    transforms.RandomRotation((0,5)),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.RandomCrop(32),\n",
        "    transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0, hue=0),\n",
        "    #transforms.RandomHorizontalFlip(p=0.5),\n",
        "    #transforms.RandomVerticalFlip(p=0.5),\n",
        "    #transforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0),\n",
        "    #transforms.RandomRotation(degrees=[-10,10], resample=False, expand=False, center=None, fill=0),\n",
        "    transforms.ToTensor(),\n",
        "    #transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
        "    #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "    ])"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 8.97 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AKqgCvcN3G86",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#transform_train = transforms.Compose([\n",
        "#    transforms.RandomCrop(32, padding=4),\n",
        "#    transforms.RandomHorizontalFlip(),\n",
        "#    transforms.ToTensor(),\n",
        "#    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
        "#])\n",
        "\n",
        "#transform_test = transforms.Compose([\n",
        "#    transforms.ToTensor(),\n",
        "#    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
        "#])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7fwfxxEOvTee",
        "colab_type": "text"
      },
      "source": [
        "## Dataset loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vFQ-c_D7eWp",
        "colab_type": "code",
        "outputId": "280dbb92-9a92-4e9f-d05a-20aa77442884",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# CIFAR-10 dataset\n",
        "train_dataset = torchvision.datasets.CIFAR10(root='../../data/',\n",
        "                                             train=True, \n",
        "                                             transform=transform,\n",
        "                                             download=True)\n",
        "\n",
        "test_dataset = torchvision.datasets.CIFAR10(root='../../data/',\n",
        "                                            train=False, \n",
        "                                            transform=transforms.ToTensor())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "time: 1.57 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78eYeI8H7eZi",
        "colab_type": "code",
        "outputId": "d907d3f3-31fb-4ad5-ace1-f4c3f6e4a120",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Data loader\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
        "                                           batch_size=64, \n",
        "                                           shuffle=True)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
        "                                          batch_size=64, \n",
        "                                          shuffle=False)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 4.98 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "26iEBDLEvZiY",
        "colab_type": "text"
      },
      "source": [
        "## functions to show an image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hW9WNjPii86",
        "colab_type": "code",
        "outputId": "6aa9420e-c18c-40bd-ee89-d5bb72341b0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "# functions to show an image\n",
        "\n",
        "\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# get some random training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print labels\n",
        "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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MekCIDisTRmrC+KuzWl9ZM9/6k/8BrCzSARk8IkZkDMgc6bYbkndIXVPPFwitykloKoSq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QEhJEqo/USKQsDI+lpYsSeTUMrk3vjplGFoubq+xFYaWxhsUfDi6UuO53Mm0wJpDEoqQgx75y2R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\nLgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "horse truck  frog  bird\n",
            "time: 386 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KsUaYIalvegs",
        "colab_type": "text"
      },
      "source": [
        "## RESNET Archtecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YJAT2jCimio",
        "colab_type": "code",
        "outputId": "a72af978-1b68-419e-8da0-e5e672a6923c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# 3x3 convolution\n",
        "def conv3x3(in_channels, out_channels, stride=1):\n",
        "    return nn.Conv2d(in_channels, out_channels, kernel_size=3, \n",
        "                     stride=stride, padding=1, bias=False)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.75 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xd6Th-OF7emh",
        "colab_type": "code",
        "outputId": "47153e10-794f-48da-d3f4-89de925d112a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "class BasicBlock(nn.Module):\n",
        "    expansion = 1\n",
        "\n",
        "    def __init__(self, in_planes, planes, stride=1):\n",
        "        super(BasicBlock, self).__init__()\n",
        "        dropout=0.25\n",
        "        dropout = 0 if dropout is None else dropout\n",
        "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "        self.shortcut = nn.Sequential()\n",
        "        if stride != 1 or in_planes != self.expansion*planes:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(self.expansion*planes)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = self.bn2(self.conv2(out))\n",
        "        out = self.dropout(out)\n",
        "        out += self.shortcut(x)\n",
        "        out = F.relu(out)\n",
        "        \n",
        "        return out"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 28.5 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0_toKu87e3j",
        "colab_type": "code",
        "outputId": "bb905b83-6dc5-4cb1-9588-71c3520f4b08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "class Bottleneck(nn.Module):\n",
        "    expansion = 4\n",
        "\n",
        "    def __init__(self, in_planes, planes, stride=1):\n",
        "        super(Bottleneck, self).__init__()\n",
        "        dropout=0.10\n",
        "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)\n",
        "        self.bn3 = nn.BatchNorm2d(self.expansion*planes)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "        self.shortcut = nn.Sequential()\n",
        "        if stride != 1 or in_planes != self.expansion*planes:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(self.expansion*planes)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = F.relu(self.bn2(self.conv2(out)))\n",
        "        out = self.bn3(self.conv3(out))\n",
        "        out = self.dropout(out)\n",
        "        out += self.shortcut(x)\n",
        "        out = F.relu(out)\n",
        "        return out"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 29.2 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HpFNWO6-7ejB",
        "colab_type": "code",
        "outputId": "2dc61f0f-4a68-4ce1-a933-d7af646f3b6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "class ResNet(nn.Module):\n",
        "    def __init__(self, block, num_blocks, num_classes=10):\n",
        "        super(ResNet, self).__init__()\n",
        "        self.in_planes = 16\n",
        "\n",
        "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(16)\n",
        "        self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)\n",
        "        self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)\n",
        "        self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)\n",
        "        self.layer4 = self._make_layer(block, 128, num_blocks[3], stride=2)\n",
        "        self.linear = nn.Linear(128*block.expansion, num_classes)\n",
        "\n",
        "    def _make_layer(self, block, planes, num_blocks, stride):\n",
        "        strides = [stride] + [1]*(num_blocks-1)\n",
        "        layers = []\n",
        "        for stride in strides:\n",
        "            layers.append(block(self.in_planes, planes, stride))\n",
        "            self.in_planes = planes * block.expansion\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = self.layer1(out)\n",
        "        out = self.layer2(out)\n",
        "        out = self.layer3(out)\n",
        "        out = self.layer4(out)\n",
        "        out = F.avg_pool2d(out, 4)\n",
        "        out = out.view(out.size(0), -1)\n",
        "        out = self.linear(out)\n",
        "        return out"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 36.5 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PSaZ7W5evmoC",
        "colab_type": "text"
      },
      "source": [
        "## RESNET Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VivXxrw08DW7",
        "colab_type": "code",
        "outputId": "7c0956ad-6d0c-4f0a-aae0-71c6f6385b5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#RESNET-18\n",
        "#model = ResNet(BasicBlock, [2,2,2,2]).to(device)\n",
        "#RESNET-34\n",
        "model = ResNet(BasicBlock, [3,4,6,3]).to(device)\n",
        "#RESNET-50\n",
        "#model = ResNet(Bottleneck, [3,4,6,3]).to(device)\n",
        "#RESNET-101\n",
        "#model = ResNet(Bottleneck, [3,4,23,3]).to(device)\n",
        "#RESNET-152\n",
        "#model = ResNet(Bottleneck, [3,8,36,3]).to(device)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 7.95 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uSd2K--hvtme",
        "colab_type": "text"
      },
      "source": [
        "## Loss and optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiyEBpKI8Dge",
        "colab_type": "code",
        "outputId": "64ba149c-feb0-4901-d14b-b0e8e874bfd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "#optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "#optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,weight_decay=1e-5)\n",
        "#optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)\n",
        "#optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "\n",
        "# For updating learning rate\n",
        "def update_lr(optimizer, lr):    \n",
        "    for param_group in optimizer.param_groups:\n",
        "        param_group['lr'] = lr"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 3.98 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHx0WnuSvzCU",
        "colab_type": "text"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dRNxNEUy8DeB",
        "colab_type": "code",
        "outputId": "e4ec40ff-8b80-405a-ad0a-969834d16806",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "curr_lr = learning_rate\n",
        "for epoch in range(num_epochs):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for i,(images, labels) in enumerate(train_loader):\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "        #if (i+1) % 100 == 0:\n",
        "          \n",
        "    print (\"Epoch [{}/{}], Train Loss: {:.4f} Train Accuracy: {} %\"\n",
        "                   .format(epoch+1, num_epochs,  loss.item(),round((100 * correct / total),2)))\n",
        "\n",
        "    # Decay learning rate\n",
        "    if (epoch+1) % 20 == 0:\n",
        "        curr_lr /= 3\n",
        "        update_lr(optimizer, curr_lr)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [1/80], Train Loss: 1.5326 Train Accuracy: 34.1 %\n",
            "Epoch [2/80], Train Loss: 1.7529 Train Accuracy: 49.16 %\n",
            "Epoch [3/80], Train Loss: 1.4392 Train Accuracy: 58.66 %\n",
            "Epoch [4/80], Train Loss: 0.6748 Train Accuracy: 64.96 %\n",
            "Epoch [5/80], Train Loss: 0.6398 Train Accuracy: 69.67 %\n",
            "Epoch [6/80], Train Loss: 0.9281 Train Accuracy: 72.11 %\n",
            "Epoch [7/80], Train Loss: 1.1081 Train Accuracy: 74.53 %\n",
            "Epoch [8/80], Train Loss: 0.8787 Train Accuracy: 76.27 %\n",
            "Epoch [9/80], Train Loss: 0.5811 Train Accuracy: 77.61 %\n",
            "Epoch [10/80], Train Loss: 0.4375 Train Accuracy: 78.47 %\n",
            "Epoch [11/80], Train Loss: 0.6199 Train Accuracy: 79.57 %\n",
            "Epoch [12/80], Train Loss: 0.5540 Train Accuracy: 80.2 %\n",
            "Epoch [13/80], Train Loss: 0.3218 Train Accuracy: 81.56 %\n",
            "Epoch [14/80], Train Loss: 0.2415 Train Accuracy: 81.43 %\n",
            "Epoch [15/80], Train Loss: 0.2833 Train Accuracy: 82.15 %\n",
            "Epoch [16/80], Train Loss: 0.1955 Train Accuracy: 82.94 %\n",
            "Epoch [17/80], Train Loss: 0.5185 Train Accuracy: 83.39 %\n",
            "Epoch [18/80], Train Loss: 0.9880 Train Accuracy: 83.38 %\n",
            "Epoch [19/80], Train Loss: 0.4012 Train Accuracy: 84.28 %\n",
            "Epoch [20/80], Train Loss: 0.7025 Train Accuracy: 84.43 %\n",
            "Epoch [21/80], Train Loss: 0.6353 Train Accuracy: 87.35 %\n",
            "Epoch [22/80], Train Loss: 0.7841 Train Accuracy: 88.02 %\n",
            "Epoch [23/80], Train Loss: 0.3932 Train Accuracy: 88.17 %\n",
            "Epoch [24/80], Train Loss: 0.3806 Train Accuracy: 88.68 %\n",
            "Epoch [25/80], Train Loss: 0.2129 Train Accuracy: 88.84 %\n",
            "Epoch [26/80], Train Loss: 0.5366 Train Accuracy: 89.16 %\n",
            "Epoch [27/80], Train Loss: 0.2149 Train Accuracy: 89.15 %\n",
            "Epoch [28/80], Train Loss: 0.3813 Train Accuracy: 89.27 %\n",
            "Epoch [29/80], Train Loss: 0.5294 Train Accuracy: 89.58 %\n",
            "Epoch [30/80], Train Loss: 0.2148 Train Accuracy: 89.58 %\n",
            "Epoch [31/80], Train Loss: 0.1812 Train Accuracy: 89.66 %\n",
            "Epoch [32/80], Train Loss: 0.1659 Train Accuracy: 89.9 %\n",
            "Epoch [33/80], Train Loss: 0.1597 Train Accuracy: 90.02 %\n",
            "Epoch [34/80], Train Loss: 0.3901 Train Accuracy: 90.18 %\n",
            "Epoch [35/80], Train Loss: 0.3083 Train Accuracy: 90.39 %\n",
            "Epoch [36/80], Train Loss: 0.1552 Train Accuracy: 90.26 %\n",
            "Epoch [37/80], Train Loss: 0.1179 Train Accuracy: 90.39 %\n",
            "Epoch [38/80], Train Loss: 0.2937 Train Accuracy: 90.46 %\n",
            "Epoch [39/80], Train Loss: 0.4640 Train Accuracy: 90.39 %\n",
            "Epoch [40/80], Train Loss: 0.4767 Train Accuracy: 90.62 %\n",
            "Epoch [41/80], Train Loss: 0.1967 Train Accuracy: 92.21 %\n",
            "Epoch [42/80], Train Loss: 0.1087 Train Accuracy: 92.7 %\n",
            "Epoch [43/80], Train Loss: 0.3545 Train Accuracy: 92.95 %\n",
            "Epoch [44/80], Train Loss: 0.2720 Train Accuracy: 92.93 %\n",
            "Epoch [45/80], Train Loss: 0.2869 Train Accuracy: 93.1 %\n",
            "Epoch [46/80], Train Loss: 0.1297 Train Accuracy: 93.23 %\n",
            "Epoch [47/80], Train Loss: 0.3884 Train Accuracy: 93.32 %\n",
            "Epoch [48/80], Train Loss: 0.0116 Train Accuracy: 93.49 %\n",
            "Epoch [49/80], Train Loss: 0.1838 Train Accuracy: 93.28 %\n",
            "Epoch [50/80], Train Loss: 0.1861 Train Accuracy: 93.47 %\n",
            "Epoch [51/80], Train Loss: 0.6032 Train Accuracy: 93.67 %\n",
            "Epoch [52/80], Train Loss: 0.3020 Train Accuracy: 93.73 %\n",
            "Epoch [53/80], Train Loss: 0.0807 Train Accuracy: 93.8 %\n",
            "Epoch [54/80], Train Loss: 0.4219 Train Accuracy: 93.81 %\n",
            "Epoch [55/80], Train Loss: 0.0874 Train Accuracy: 93.88 %\n",
            "Epoch [56/80], Train Loss: 0.0853 Train Accuracy: 93.92 %\n",
            "Epoch [57/80], Train Loss: 0.1266 Train Accuracy: 93.84 %\n",
            "Epoch [58/80], Train Loss: 0.0951 Train Accuracy: 94.0 %\n",
            "Epoch [59/80], Train Loss: 0.0617 Train Accuracy: 94.14 %\n",
            "Epoch [60/80], Train Loss: 0.1412 Train Accuracy: 94.22 %\n",
            "Epoch [61/80], Train Loss: 0.3105 Train Accuracy: 94.87 %\n",
            "Epoch [62/80], Train Loss: 0.1661 Train Accuracy: 95.04 %\n",
            "Epoch [63/80], Train Loss: 0.3264 Train Accuracy: 95.15 %\n",
            "Epoch [64/80], Train Loss: 0.0817 Train Accuracy: 95.2 %\n",
            "Epoch [65/80], Train Loss: 0.2662 Train Accuracy: 95.36 %\n",
            "Epoch [66/80], Train Loss: 0.2949 Train Accuracy: 95.36 %\n",
            "Epoch [67/80], Train Loss: 0.0176 Train Accuracy: 95.29 %\n",
            "Epoch [68/80], Train Loss: 0.0752 Train Accuracy: 95.5 %\n",
            "Epoch [69/80], Train Loss: 0.4103 Train Accuracy: 95.27 %\n",
            "Epoch [70/80], Train Loss: 0.1637 Train Accuracy: 95.45 %\n",
            "Epoch [71/80], Train Loss: 0.1602 Train Accuracy: 95.45 %\n",
            "Epoch [72/80], Train Loss: 0.2021 Train Accuracy: 95.63 %\n",
            "Epoch [73/80], Train Loss: 0.2315 Train Accuracy: 95.59 %\n",
            "Epoch [74/80], Train Loss: 0.0698 Train Accuracy: 95.68 %\n",
            "Epoch [75/80], Train Loss: 0.3025 Train Accuracy: 95.52 %\n",
            "Epoch [76/80], Train Loss: 0.0915 Train Accuracy: 95.73 %\n",
            "Epoch [77/80], Train Loss: 0.0748 Train Accuracy: 95.78 %\n",
            "Epoch [78/80], Train Loss: 0.0285 Train Accuracy: 95.77 %\n",
            "Epoch [79/80], Train Loss: 0.2554 Train Accuracy: 95.59 %\n",
            "Epoch [80/80], Train Loss: 0.3753 Train Accuracy: 95.82 %\n",
            "time: 1h 36min 14s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2-vn6CLxSBq",
        "colab_type": "code",
        "outputId": "c1e32dda-10c9-477f-b4c4-f906ecdd356e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 76
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "def convert_to_imshow_format(image):\n",
        "    # first convert back to [0,1] range from [-1,1] range\n",
        "    image = image / 2 + 0.5\n",
        "    image = image.numpy()\n",
        "    # convert from CHW to HWC\n",
        "    # from 3x32x32 to 32x32x3\n",
        "    return image.transpose(1,2,0)\n",
        "\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "fig, axes = plt.subplots(1, len(images), figsize=(12,2.5))\n",
        "for idx, image in enumerate(images):\n",
        "    axes[idx].imshow(convert_to_imshow_format(image))\n",
        "    axes[idx].set_title(classes[labels[idx]])\n",
        "    axes[idx].set_xticks([])\n",
        "    axes[idx].set_yticks([])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LqPchnDm4vXzo9rA7sy3CQpbs7/S2/MsWv+4ouNW3QAzhhPBgZFtGBllafZLW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\n",
            "text/plain": [
              "<Figure size 864x180 with 64 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "time: 2.5 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytAhp0JWxCuV",
        "colab_type": "code",
        "outputId": "fc16cd6f-031d-43fb-9d35-3fa9aff7e09c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 76
        }
      },
      "source": [
        "dataiter = iter(test_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "fig, axes = plt.subplots(1, len(images), figsize=(12,2.5))\n",
        "for idx, image in enumerate(images):\n",
        "    axes[idx].imshow(convert_to_imshow_format(image))\n",
        "    axes[idx].set_title(classes[labels[idx]])\n",
        "    axes[idx].set_xticks([])\n",
        "    axes[idx].set_yticks([])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AbFE+HDmv9Pd3tN2F6PtHiZRST1RKVc32ze8jWxPfuovF+j66K+U02z2ueT20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\n0dHR0dHR8VbQKbYdHR0dHR0dHR1vBf8FtUGwAcv5sHwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x180 with 64 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "time: 2.44 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Bo-eyWBxn9T",
        "colab_type": "code",
        "outputId": "ec587cea-3514-4644-8398-c05f9a941037",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "images = images.to(device)\n",
        "labels = labels.to(device)\n",
        "outputs = model(images)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 45.4 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wv_wo9I6yIMW",
        "colab_type": "code",
        "outputId": "a2cca81c-b2e3-451f-bb06-b9e4823e8ad7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sm = nn.Softmax(dim=1)\n",
        "sm_outputs = sm(outputs)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 2.58 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfhHhpi2yO9q",
        "colab_type": "code",
        "outputId": "5d8fe0b8-a06d-421e-976f-9017cf6fb093",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "probs, index = torch.max(sm_outputs, dim=1)\n",
        "\n",
        "for p, i in zip(probs, index):\n",
        "    print('{0} - {1:.4f}'.format(classes[i], p))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cat - 0.9998\n",
            "ship - 0.9996\n",
            "ship - 0.9750\n",
            "plane - 0.8719\n",
            "frog - 0.9998\n",
            "frog - 0.9904\n",
            "car - 0.9808\n",
            "frog - 0.9481\n",
            "cat - 0.9937\n",
            "car - 0.9552\n",
            "plane - 0.9985\n",
            "truck - 0.9999\n",
            "dog - 0.9682\n",
            "horse - 0.9999\n",
            "truck - 0.9945\n",
            "ship - 0.9964\n",
            "dog - 0.9966\n",
            "horse - 0.9999\n",
            "ship - 0.9966\n",
            "frog - 0.9998\n",
            "horse - 0.9940\n",
            "plane - 0.9704\n",
            "deer - 0.8096\n",
            "truck - 1.0000\n",
            "dog - 0.5143\n",
            "bird - 0.9439\n",
            "deer - 0.9027\n",
            "plane - 0.9982\n",
            "truck - 0.9997\n",
            "frog - 0.8602\n",
            "frog - 0.8374\n",
            "dog - 0.9970\n",
            "ship - 0.5147\n",
            "dog - 0.8045\n",
            "truck - 0.9996\n",
            "ship - 0.8805\n",
            "deer - 0.9452\n",
            "car - 0.6674\n",
            "truck - 0.9999\n",
            "dog - 0.9998\n",
            "deer - 0.9822\n",
            "frog - 0.9912\n",
            "dog - 0.6485\n",
            "frog - 0.9718\n",
            "plane - 0.9997\n",
            "truck - 0.9998\n",
            "cat - 0.9918\n",
            "ship - 0.9310\n",
            "horse - 0.9997\n",
            "frog - 0.9480\n",
            "truck - 0.9994\n",
            "ship - 0.9999\n",
            "bird - 0.9315\n",
            "cat - 0.9627\n",
            "ship - 0.9996\n",
            "ship - 1.0000\n",
            "horse - 0.9993\n",
            "cat - 0.9878\n",
            "dog - 0.5994\n",
            "cat - 0.7901\n",
            "horse - 0.9999\n",
            "dog - 0.5197\n",
            "frog - 0.9993\n",
            "cat - 0.8062\n",
            "time: 35.8 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hmQF10zMv_PS",
        "colab_type": "text"
      },
      "source": [
        "## Accuracy on test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiZftaxHyShZ",
        "colab_type": "code",
        "outputId": "aa3d9133-0c2d-498e-83a4-2ecbe4bcfc2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "total_correct = 0\n",
        "total_images = 0\n",
        "confusion_matrix = np.zeros([10,10], int)\n",
        "with torch.no_grad():\n",
        "    for data in test_loader:\n",
        "        images, labels = data\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total_images += labels.size(0)\n",
        "        total_correct += (predicted == labels).sum().item()\n",
        "        for i, l in enumerate(labels):\n",
        "            confusion_matrix[l.item(), predicted[i].item()] += 1 \n",
        "\n",
        "model_accuracy = total_correct / total_images * 100\n",
        "print('Model accuracy on {0} test images: {1:.2f}%'.format(total_images, model_accuracy))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model accuracy on 10000 test images: 91.96%\n",
            "time: 6.53 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uRQDR2vwwEPw",
        "colab_type": "text"
      },
      "source": [
        "## Accuracy per class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6v5gH5D6y0sZ",
        "colab_type": "code",
        "outputId": "ba49433e-db2a-4756-8f46-ef6a532a22c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "print('{0:10s} - {1}'.format('Category','Accuracy'))\n",
        "for i, r in enumerate(confusion_matrix):\n",
        "    print('{0:10s} - {1:.1f}'.format(classes[i], r[i]/np.sum(r)*100))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Category   - Accuracy\n",
            "plane      - 87.5\n",
            "car        - 94.3\n",
            "bird       - 83.4\n",
            "cat        - 76.6\n",
            "deer       - 85.5\n",
            "dog        - 78.0\n",
            "frog       - 89.4\n",
            "horse      - 92.5\n",
            "ship       - 94.1\n",
            "truck      - 93.9\n",
            "time: 9.18 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7jR1Qrw2wJl6",
        "colab_type": "text"
      },
      "source": [
        "## Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHszLmdNy5v_",
        "colab_type": "code",
        "outputId": "214f542b-b216-41af-fe1f-a8cc16fbfc11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "fig, ax = plt.subplots(1,1,figsize=(10,8))\n",
        "ax.matshow(confusion_matrix, aspect='auto', vmin=0, vmax=1000, cmap=plt.get_cmap('Blues'),)\n",
        "for (i, j), z in np.ndenumerate(confusion_matrix):\n",
        "    ax.text(j, i, '{:0.1f}'.format(z), ha='center', va='center',\n",
        "            bbox=dict(boxstyle='round', facecolor='white', edgecolor='0.3'))\n",
        "plt.ylabel('Actual Category')\n",
        "plt.yticks(range(10), classes)\n",
        "plt.xlabel('Predicted Category')\n",
        "plt.xticks(range(10), classes)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TEhLCM888w5CbbzErCbSET9Z/yPjx45k8ebJFyuvSpQs+Pj68+85qXn9jtUXKbIrS0lI+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\npWzg/wDBZ6h30LZ9IwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "time: 650 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZ5U1tkZ7ecu",
        "colab_type": "code",
        "outputId": "655e4a6d-94b7-4885-d258-69ee20fef530",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Save the model checkpoint\n",
        "torch.save(model.state_dict(), 'resnet.ckpt')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 44 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o46VPJ_WwODk",
        "colab_type": "text"
      },
      "source": [
        "## Parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2x-t3dD2-H5A",
        "colab_type": "code",
        "outputId": "8f416412-c4a2-43da-e619-aa0506cb330d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "pytorch_total_params = sum(p.numel() for p in model.parameters())\n",
        "pytorch_total_params"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1334618"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "stream",
          "text": [
            "time: 6.29 ms\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}