cifar_torch.ipynb

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      "source": [
        "import torch\n",
        "import torchvision\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision.transforms as transforms"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      },
      "source": [
        "transform=transforms.Compose([transforms.RandomHorizontalFlip(p=0.5),\n",
        "                              transforms.RandomVerticalFlip(p=0.5),\n",
        "                              transforms.ToTensor(),\n",
        "                               transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])\n",
        "trainset=torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                    download=True, transform=transform)\n",
        "trainloader=torch.utils.data.DataLoader(trainset, batch_size=5,\n",
        "                                          shuffle=True, num_workers=0)\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=1,\n",
        "                                         shuffle=False, num_workers=0)\n",
        "\n",
        "classes=('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0s88aW8dxYx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 20, (3,3))\n",
        "        # self.batch1=nn.BatchNorm2d(20)\n",
        "        self.conv2 = nn.Conv2d(20, 50, (3,3))\n",
        "        # self.batch2=nn.BatchNorm2d(50)\n",
        "        self.fc1 = nn.Linear(50 * (6*6), 125)\n",
        "        self.fc2 = nn.Linear(125, 75)\n",
        "        self.fc3 = nn.Linear(75, 10)\n",
        "        self.pool = nn.MaxPool2d((2,2), 2)\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 = x.view(-1, 50 * (6*6))\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()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5vcNj7BePcd",
        "colab_type": "code",
        "outputId": "4c25def9-9978-4bd8-d47d-f8e99d14130a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "# Classification Cross-Entropy loss and SGD with momentum\n",
        "criterion=nn.CrossEntropyLoss()\n",
        "optimizer=optim.Adam(net.parameters())\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)\n",
        "net.to(device)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Net(\n",
              "  (conv1): Conv2d(3, 20, kernel_size=(3, 3), stride=(1, 1))\n",
              "  (conv2): Conv2d(20, 50, kernel_size=(3, 3), stride=(1, 1))\n",
              "  (fc1): Linear(in_features=1800, out_features=125, bias=True)\n",
              "  (fc2): Linear(in_features=125, out_features=75, bias=True)\n",
              "  (fc3): Linear(in_features=75, out_features=10, bias=True)\n",
              "  (pool): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3tDAlvBkw9Mq",
        "colab_type": "text"
      },
      "source": [
        "Training my network here."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7balj5recg-",
        "colab_type": "code",
        "outputId": "6bed36b1-3537-4767-d47e-46a73bc41628",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "for epoch in range(10): # no of epochs\n",
        "    running_loss=0.0\n",
        "    for i,data in enumerate(trainloader,0):\n",
        "          inputs, labels = data[0].to(device), data[1].to(device)\n",
        "          optimizer.zero_grad()\n",
        "          outputs=net(inputs)\n",
        "          loss=criterion(outputs,labels)\n",
        "          loss.backward()\n",
        "          optimizer.step()\n",
        "          running_loss+=loss.item()\n",
        "          if i%2000==1999:\n",
        "                if i%12000==11999: running_loss=0\n",
        "                #   print('Epoch:',epoch+1,', loss:',running_loss/2000)\n",
        "                \n",
        "    correct=0\n",
        "    total=0\n",
        "    with torch.no_grad():\n",
        "        for data in testloader:\n",
        "            images, labels = data[0].to(device), data[1].to(device)\n",
        "            outputs=net(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "    print(\"Epoch=\",epoch+1,\", Accuracy=\",correct*100/total)\n",
        "print('Model trained :)')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch= 1 , Accuracy= 50.06\n",
            "Epoch= 2 , Accuracy= 57.52\n",
            "Epoch= 3 , Accuracy= 59.48\n",
            "Epoch= 4 , Accuracy= 62.01\n",
            "Epoch= 5 , Accuracy= 62.93\n",
            "Epoch= 6 , Accuracy= 63.91\n",
            "Epoch= 7 , Accuracy= 62.73\n",
            "Epoch= 8 , Accuracy= 65.75\n",
            "Epoch= 9 , Accuracy= 65.42\n",
            "Epoch= 10 , Accuracy= 64.28\n",
            "Model trained :)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdyl6Zpw65xr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# torch.save(net.state_dict(), './cifar2.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}