Model_1.ipynb

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        "<a href=\"https://colab.research.google.com/github/Ehsan-data/JAVA/blob/master/Model_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "\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 = 10\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": 5,
      "outputs": [
        {
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          "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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          "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": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "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 [1/10], Step [2000/5000], Loss: 1.7010\n",
            "Epoch [1/10], Step [4000/5000], Loss: 1.3629\n",
            "Epoch [2/10], Step [2000/5000], Loss: 1.6898\n",
            "Epoch [2/10], Step [4000/5000], Loss: 2.2158\n",
            "Epoch [3/10], Step [2000/5000], Loss: 1.3722\n",
            "Epoch [3/10], Step [4000/5000], Loss: 1.4009\n",
            "Epoch [4/10], Step [2000/5000], Loss: 1.7274\n",
            "Epoch [4/10], Step [4000/5000], Loss: 1.2296\n",
            "Epoch [5/10], Step [2000/5000], Loss: 0.7747\n",
            "Epoch [5/10], Step [4000/5000], Loss: 0.6946\n",
            "Epoch [6/10], Step [2000/5000], Loss: 0.8670\n",
            "Epoch [6/10], Step [4000/5000], Loss: 1.3589\n",
            "Epoch [7/10], Step [2000/5000], Loss: 0.8905\n",
            "Epoch [7/10], Step [4000/5000], Loss: 1.2436\n",
            "Epoch [8/10], Step [2000/5000], Loss: 0.6661\n",
            "Epoch [8/10], Step [4000/5000], Loss: 0.4985\n",
            "Epoch [9/10], Step [2000/5000], Loss: 0.7170\n",
            "Epoch [9/10], Step [4000/5000], Loss: 0.6119\n",
            "Epoch [10/10], Step [2000/5000], Loss: 0.4260\n",
            "Epoch [10/10], Step [4000/5000], Loss: 1.2498\n",
            "Finished Training\n",
            "Accuracy of the network: 61.1 %\n",
            "Accuracy of plane: 58.4 %\n",
            "Accuracy of car: 60.2 %\n",
            "Accuracy of bird: 35.0 %\n",
            "Accuracy of cat: 46.2 %\n",
            "Accuracy of deer: 49.7 %\n",
            "Accuracy of dog: 54.7 %\n",
            "Accuracy of frog: 75.0 %\n",
            "Accuracy of horse: 70.6 %\n",
            "Accuracy of ship: 76.5 %\n",
            "Accuracy of truck: 84.7 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3IHbJmkaUKyJ"
      },
      "source": [
        "most of the codes are refrened at:\n",
        "https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py"
      ]
    }
  ]
}