81. CIFAR 10 Image Classification (Tensor).ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# CIFAR 10 Image Classification"
      ],
      "metadata": {
        "id": "irpGIXdgvz75"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load and normalize CIFAR 10\n"
      ],
      "metadata": {
        "id": "MQAoWU37GCdJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ],
      "metadata": {
        "id": "kmCG9ejqGFZC"
      },
      "execution_count": 1,
      "outputs": []
    },
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      "source": [
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "     ]\n",
        ")\n",
        "\n",
        "batch_size = 4\n",
        "\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
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        "outputId": "b6da769f-d05f-467f-b7ea-230c9a534434"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
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          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def imshow(img):\n",
        "  img = img/2+0.5\n",
        "  npimg = img.numpy()\n",
        "  plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "dataiter = iter(trainloader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "\n",
        "print(' '.join((f'{classes[labels[j]]:5s}' for j in range(batch_size))))"
      ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bird  car   ship  dog  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Defining a Convolutional Neural Network"
      ],
      "metadata": {
        "id": "_PgejUxzKk8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class Net(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "    self.pool = nn.MaxPool2d(2, 2)\n",
        "    self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "    self.fc1 = nn.Linear(16*5*5, 120)\n",
        "    self.fc2 = nn.Linear(120, 84)\n",
        "    self.fc3 = nn.Linear(84, 10)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x = self.pool(F.relu(self.conv1(x)))\n",
        "    x = self.pool(F.relu(self.conv2(x)))\n",
        "    x = torch.flatten(x, 1)\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.relu(self.fc2(x))\n",
        "    x = self.fc3(x)\n",
        "    return x\n",
        "  \n",
        "net = Net()"
      ],
      "metadata": {
        "id": "GK6rf5hZK6Qu"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define a Loss function and optimizer"
      ],
      "metadata": {
        "id": "ZAigqqvcL0eO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
      ],
      "metadata": {
        "id": "YpkaffToL8eo"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the network"
      ],
      "metadata": {
        "id": "N4nX0m1iMLof"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(2):\n",
        "  running_loss = 0.0\n",
        "  for i, data in enumerate(trainloader, 0):\n",
        "    inputs, labels = data\n",
        "    optimizer.zero_grad()\n",
        "    outputs = net(inputs)\n",
        "    loss = criterion(outputs, labels)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    running_loss += loss.item()\n",
        "\n",
        "    if i % 2000 == 1999:\n",
        "      print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')\n",
        "      running_loss = 0.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3veilr5MQiK",
        "outputId": "3d63b5e0-7222-47c8-c0d5-c6ca872e6ab0"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1,  2000] loss: 2.223\n",
            "[1,  4000] loss: 1.832\n",
            "[1,  6000] loss: 1.638\n",
            "[1,  8000] loss: 1.551\n",
            "[1, 10000] loss: 1.475\n",
            "[1, 12000] loss: 1.439\n",
            "[2,  2000] loss: 1.375\n",
            "[2,  4000] loss: 1.348\n",
            "[2,  6000] loss: 1.322\n",
            "[2,  8000] loss: 1.299\n",
            "[2, 10000] loss: 1.278\n",
            "[2, 12000] loss: 1.275\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(net.state_dict(), 'cifar.pth')"
      ],
      "metadata": {
        "id": "3JnpDreQMwka"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test the network on test data"
      ],
      "metadata": {
        "id": "VDe3YsKoNshG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataiter = iter(testloader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print(\"Ground Truth\", ' '.join(f'{classes[labels[j]]:5s}' for j in range(4)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "id": "2fb3GxMANxRh",
        "outputId": "a72429b5-fbba-4990-db47-51478241d474"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ground Truth plane horse frog  truck\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "net = Net()\n",
        "net.load_state_dict(torch.load('cifar.pth'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6SP3RR_mOEdl",
        "outputId": "e6b601d2-1317-47d7-b5e9-a438022bdfa5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outputs  = net(images)"
      ],
      "metadata": {
        "id": "pbayCzumOMf0"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "_, predicted = torch.max(outputs, 1)\n",
        "\n",
        "print('Predicted: ', ' '.join(f'{classes[predicted[j]]:5s}'\n",
        "                              for j in range(4)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHJi463sOTdL",
        "outputId": "2aeb45fd-3f14-41c0-85e2-815e826f326a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted:  horse cat   frog  truck\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correct = 0\n",
        "total = 0\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kbMBc1diOZgw",
        "outputId": "534c2540-1db0-4aa8-8a8f-c4a833fc09f4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the network on the 10000 test images: 56 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correct_pred = {classname: 0 for classname in classes}\n",
        "total_pred = {classname: 0 for classname in classes}\n",
        "\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        outputs = net(images)\n",
        "        _, predictions = torch.max(outputs, 1)\n",
        "        for label, prediction in zip(labels, predictions):\n",
        "            if label == prediction:\n",
        "                correct_pred[classes[label]] += 1\n",
        "            total_pred[classes[label]] += 1\n",
        "\n",
        "\n",
        "for classname, correct_count in correct_pred.items():\n",
        "    accuracy = 100 * float(correct_count) / total_pred[classname]\n",
        "    print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FCvI8JNhOgcg",
        "outputId": "26e9eada-3f88-4074-f67d-78682cd984b4"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy for class: plane is 46.7 %\n",
            "Accuracy for class: car   is 67.5 %\n",
            "Accuracy for class: bird  is 40.0 %\n",
            "Accuracy for class: cat   is 51.0 %\n",
            "Accuracy for class: deer  is 53.1 %\n",
            "Accuracy for class: dog   is 24.9 %\n",
            "Accuracy for class: frog  is 60.2 %\n",
            "Accuracy for class: horse is 70.6 %\n",
            "Accuracy for class: ship  is 72.8 %\n",
            "Accuracy for class: truck is 75.3 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z0KcFz9POqZy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}