Model.2 .ipynb

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        "<a href=\"https://colab.research.google.com/github/Ehsan-data/JAVA/blob/master/Model.2%20.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "\n",
        "# Importing the pytorch moduels\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Device \n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Hyper-parameters \n",
        "num_epochs = 20\n",
        "batch_size = 10\n",
        "learning_rate = 0.01\n",
        "\n",
        "# We transform images \n",
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "#loading dataset\n",
        "train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "\n",
        "test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,\n",
        "                                          shuffle=True)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,\n",
        "                                         shuffle=False)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "\n",
        "def imshow(image):\n",
        "    image = image / 2 + 0.5  # unnormalize\n",
        "    arr_image = image.numpy()\n",
        "    plt.imshow(np.transpose(arr_image, (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",
        "\n",
        "class ConvNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(ConvNet, self).__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",
        "        # -> n, 3, 32, 32\n",
        "        x = self.pool(F.relu(self.conv1(x)))  \n",
        "        x = self.pool(F.relu(self.conv2(x)))  \n",
        "        x = x.view(-1, 16 * 5 * 5)            \n",
        "        x = F.relu(self.fc1(x))               \n",
        "        x = F.relu(self.fc2(x))               \n",
        "        x = self.fc3(x)                       \n",
        "        return x\n",
        "\n",
        "\n",
        "model_1 = ConvNet().to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model_1.parameters(), lr=learning_rate)\n",
        "\n",
        "n_total_steps = len(train_loader)\n",
        "for epoch in range(num_epochs):\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_1(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimization\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if (i+1) % 2000 == 0:\n",
        "            print (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')\n",
        "\n",
        "print('Finished Training')\n",
        "PATH = './cnn.pth'\n",
        "torch.save(model_1.state_dict(), PATH)\n",
        "\n",
        "with torch.no_grad():\n",
        "    n_correct = 0\n",
        "    n_samples = 0\n",
        "    n_class_correct = [0 for i in range(10)]\n",
        "    n_class_samples = [0 for i in range(10)]\n",
        "    for images, labels in test_loader:\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "        outputs = model_1(images)\n",
        "\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        n_samples += labels.size(0)\n",
        "        n_correct += (predicted == labels).sum().item()\n",
        "        \n",
        "        for i in range(batch_size):\n",
        "            label = labels[i]\n",
        "            pred = predicted[i]\n",
        "            if (label == pred):\n",
        "                n_class_correct[label] += 1\n",
        "            n_class_samples[label] += 1\n",
        "\n",
        "    acc = 100.0 * n_correct / n_samples\n",
        "    print(f'Accuracy of the network: {acc} %')\n",
        "\n",
        "    for i in range(10):\n",
        "        acc = 100.0 * n_class_correct[i] / n_class_samples[i]\n",
        "        print(f'Accuracy of {classes[i]}: {acc} %')"
      ],
      "execution_count": 1,
      "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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              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        },
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
            "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/20], Step [2000/5000], Loss: 2.2637\n",
            "Epoch [1/20], Step [4000/5000], Loss: 1.9672\n",
            "Epoch [2/20], Step [2000/5000], Loss: 1.5578\n",
            "Epoch [2/20], Step [4000/5000], Loss: 1.1976\n",
            "Epoch [3/20], Step [2000/5000], Loss: 1.1270\n",
            "Epoch [3/20], Step [4000/5000], Loss: 0.9546\n",
            "Epoch [4/20], Step [2000/5000], Loss: 0.9351\n",
            "Epoch [4/20], Step [4000/5000], Loss: 0.5662\n",
            "Epoch [5/20], Step [2000/5000], Loss: 1.5362\n",
            "Epoch [5/20], Step [4000/5000], Loss: 0.8854\n",
            "Epoch [6/20], Step [2000/5000], Loss: 0.8042\n",
            "Epoch [6/20], Step [4000/5000], Loss: 1.1067\n",
            "Epoch [7/20], Step [2000/5000], Loss: 0.5189\n",
            "Epoch [7/20], Step [4000/5000], Loss: 0.6090\n",
            "Epoch [8/20], Step [2000/5000], Loss: 1.2574\n",
            "Epoch [8/20], Step [4000/5000], Loss: 0.8753\n",
            "Epoch [9/20], Step [2000/5000], Loss: 0.7134\n",
            "Epoch [9/20], Step [4000/5000], Loss: 0.8139\n",
            "Epoch [10/20], Step [2000/5000], Loss: 1.2401\n",
            "Epoch [10/20], Step [4000/5000], Loss: 0.4798\n",
            "Epoch [11/20], Step [2000/5000], Loss: 1.1969\n",
            "Epoch [11/20], Step [4000/5000], Loss: 0.3687\n",
            "Epoch [12/20], Step [2000/5000], Loss: 0.4753\n",
            "Epoch [12/20], Step [4000/5000], Loss: 1.3367\n",
            "Epoch [13/20], Step [2000/5000], Loss: 0.8422\n",
            "Epoch [13/20], Step [4000/5000], Loss: 1.2679\n",
            "Epoch [14/20], Step [2000/5000], Loss: 0.6754\n",
            "Epoch [14/20], Step [4000/5000], Loss: 1.3211\n",
            "Epoch [15/20], Step [2000/5000], Loss: 0.7344\n",
            "Epoch [15/20], Step [4000/5000], Loss: 0.8757\n",
            "Epoch [16/20], Step [2000/5000], Loss: 0.8744\n",
            "Epoch [16/20], Step [4000/5000], Loss: 0.6230\n",
            "Epoch [17/20], Step [2000/5000], Loss: 0.6307\n",
            "Epoch [17/20], Step [4000/5000], Loss: 0.4816\n",
            "Epoch [18/20], Step [2000/5000], Loss: 0.5389\n",
            "Epoch [18/20], Step [4000/5000], Loss: 0.6449\n",
            "Epoch [19/20], Step [2000/5000], Loss: 0.3599\n",
            "Epoch [19/20], Step [4000/5000], Loss: 0.6509\n",
            "Epoch [20/20], Step [2000/5000], Loss: 0.9489\n",
            "Epoch [20/20], Step [4000/5000], Loss: 0.4427\n",
            "Finished Training\n",
            "Accuracy of the network: 64.01 %\n",
            "Accuracy of plane: 75.0 %\n",
            "Accuracy of car: 77.8 %\n",
            "Accuracy of bird: 54.6 %\n",
            "Accuracy of cat: 46.4 %\n",
            "Accuracy of deer: 54.1 %\n",
            "Accuracy of dog: 54.9 %\n",
            "Accuracy of frog: 67.8 %\n",
            "Accuracy of horse: 60.8 %\n",
            "Accuracy of ship: 75.2 %\n",
            "Accuracy of truck: 73.5 %\n"
          ]
        }
      ]
    }
  ]
}