PyTorch_Convolution_Neural_Network.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PyTorch_Convolution Neural Network.ipynb",
      "provenance": [],
      "mount_file_id": "1FZUl33EJaQthf21gv205YGxdJHeRaxQu",
      "authorship_tag": "ABX9TyO+44s44TDTc5wR9NrSOeQK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shrey-Viradiya/PyTorch_for_DL/blob/master/PyTorch_Convolution_Neural_Network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uEwf7lLutlru"
      },
      "source": [
        "# PyTorch_Convolution Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vp41hvLdXL3s",
        "outputId": "c4056e7a-2adb-4b6f-d267-1181f387f319",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fri Oct  2 05:45:56 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.23.05    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07iQUZeBx0xE",
        "outputId": "5c6eabe5-b380-4c59-ec9b-f6d4663bee54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!rm -r data train test val"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'data': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s9DOdsf0XNsa",
        "outputId": "6ac4390d-2c23-4375-8df8-cb98781d4054",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "! mkdir ~/.kaggle\n",
        "! cp '/content/drive/My Drive/kaggle.json' ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle datasets download -d lucassj/dogs-vs-cats-train-validadion-and-evaluation\n",
        "! unzip -n '/content/dogs-vs-cats-train-validadion-and-evaluation.zip'"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6RaBy5fWx7YO"
      },
      "source": [
        "# !unzip -n '/content/drive/My Drive/images.zip'"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RoMFOE67tn9P"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XgqYxgPcovgv"
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "from PIL import Image\n",
        "import time"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Wcf8B4PNZuX"
      },
      "source": [
        "# CNNNet (or AlexNet by another name…)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2bBf3CYtv5Y"
      },
      "source": [
        "class CNNNet(torch.nn.Module):\n",
        "\n",
        "    def __init__(self, num_classes = 2):\n",
        "        super(CNNNet, self).__init__()\n",
        "        self.features = torch.nn.Sequential(\n",
        "            torch.nn.Conv2d(3,64, kernel_size=11, stride=4, padding=2),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.MaxPool2d(kernel_size=3, stride = 2),\n",
        "            torch.nn.Conv2d(64, 192, kernel_size = 5, padding = 2),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.MaxPool2d(kernel_size=3, stride=2),\n",
        "            torch.nn.Conv2d(192, 384, kernel_size=3, padding=1),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.Conv2d(384, 256, kernel_size=3, padding=1),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.MaxPool2d(kernel_size=3, stride=2),\n",
        "        )\n",
        "        self.avgpool = torch.nn.AdaptiveAvgPool2d((6,6))\n",
        "        self.classifier = torch.nn.Sequential(\n",
        "            torch.nn.Dropout(),\n",
        "            torch.nn.Linear(256 * 6 * 6, 4096),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.Dropout(),\n",
        "            torch.nn.Linear(4096, 4096),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.Linear(4096, num_classes)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.features(x)\n",
        "        x = self.avgpool(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = self.classifier(x)\n",
        "        return x"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtbasjC4Wc7V"
      },
      "source": [
        "cnnnet = CNNNet()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZlRjABmhWkmv"
      },
      "source": [
        "def train(model, optimizer, loss_fn, train_loader, val_loader, epochs=20, device=\"cpu\"):\n",
        "    \n",
        "    for epoch in range(epochs):\n",
        "        start = time.time()\n",
        "        training_loss = 0.0\n",
        "        valid_loss = 0.0\n",
        "        model.train()\n",
        "        for batch in train_loader:\n",
        "            optimizer.zero_grad()\n",
        "            inputs, targets = batch\n",
        "            inputs = inputs.to(device)\n",
        "            targets = targets.to(device)\n",
        "            output = model(inputs)\n",
        "            loss = loss_fn(output, targets)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            training_loss += loss.item()\n",
        "        training_loss /= len(train_loader.dataset)\n",
        "        \n",
        "        model.eval()\n",
        "        correct = 0 \n",
        "        total = 0\n",
        "        with torch.no_grad():\n",
        "            for batch in val_loader:\n",
        "                inputs, targets = batch\n",
        "                inputs = inputs.to(device)\n",
        "                output = model(inputs)\n",
        "                targets = targets.to(device)\n",
        "                loss = loss_fn(output,targets) \n",
        "                valid_loss += loss.item()\n",
        "                _, predicted = torch.max(output.data, 1)\n",
        "                total += targets.size(0)\n",
        "                correct += (predicted == targets).sum().item()\n",
        "\n",
        "        valid_loss /= len(val_loader.dataset)\n",
        "\n",
        "        print(f'Epoch: {epoch}, Training Loss: {training_loss:.2f}, Validation Loss: {valid_loss:.2f}, accuracy = {(correct / total):.2f}, time: {time.time() - start}')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qr39WCfWnFz"
      },
      "source": [
        "def check_image(path):\n",
        "    try:\n",
        "        im = Image.open(path)\n",
        "        return True\n",
        "    except:\n",
        "        return False\n",
        "\n",
        "img_transforms = torchvision.transforms.Compose([\n",
        "    torchvision.transforms.Resize((64,64)),\n",
        "    torchvision.transforms.ColorJitter(),\n",
        "    torchvision.transforms.RandomHorizontalFlip(),\n",
        "    torchvision.transforms.ToTensor(),\n",
        "    ])\n",
        "\n",
        "train_data_path = \"./train/\"\n",
        "train_data = torchvision.datasets.ImageFolder(root=train_data_path,transform=img_transforms, is_valid_file=check_image)\n",
        "val_data_path = \"./validation/\"\n",
        "val_data = torchvision.datasets.ImageFolder(root=val_data_path,transform=img_transforms, is_valid_file=check_image)\n",
        "batch_size=10\n",
        "train_data_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,shuffle=True)\n",
        "val_data_loader  = torch.utils.data.DataLoader(val_data, batch_size=batch_size, shuffle=True)\n",
        "if torch.cuda.is_available():\n",
        "    device = torch.device(\"cuda\") \n",
        "else:\n",
        "    device = torch.device(\"cpu\")"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PfmJfSimXbwS"
      },
      "source": [
        "cnnnet.to(device)\n",
        "optimizer = torch.optim.Adam(cnnnet.parameters(), lr=0.00005)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5gW7NrhXc6V",
        "outputId": "98932988-ca0a-41e8-9eeb-003ddb60f52c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        }
      },
      "source": [
        "train(cnnnet, optimizer,torch.nn.CrossEntropyLoss(), train_data_loader,val_data_loader, epochs=25, device=device)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0, Training Loss: 0.06, Validation Loss: 0.06, accuracy = 0.70, time: 125.05465865135193\n",
            "Epoch: 1, Training Loss: 0.05, Validation Loss: 0.05, accuracy = 0.77, time: 122.81562161445618\n",
            "Epoch: 2, Training Loss: 0.05, Validation Loss: 0.05, accuracy = 0.79, time: 121.94584250450134\n",
            "Epoch: 3, Training Loss: 0.04, Validation Loss: 0.04, accuracy = 0.79, time: 121.91100025177002\n",
            "Epoch: 4, Training Loss: 0.04, Validation Loss: 0.05, accuracy = 0.78, time: 121.85887789726257\n",
            "Epoch: 5, Training Loss: 0.04, Validation Loss: 0.04, accuracy = 0.80, time: 121.60627222061157\n",
            "Epoch: 6, Training Loss: 0.03, Validation Loss: 0.04, accuracy = 0.80, time: 122.5108962059021\n",
            "Epoch: 7, Training Loss: 0.03, Validation Loss: 0.05, accuracy = 0.79, time: 120.41695475578308\n",
            "Epoch: 8, Training Loss: 0.03, Validation Loss: 0.05, accuracy = 0.80, time: 122.41351103782654\n",
            "Epoch: 9, Training Loss: 0.03, Validation Loss: 0.05, accuracy = 0.79, time: 122.75190949440002\n",
            "Epoch: 10, Training Loss: 0.02, Validation Loss: 0.05, accuracy = 0.80, time: 121.70402598381042\n",
            "Epoch: 11, Training Loss: 0.02, Validation Loss: 0.05, accuracy = 0.79, time: 122.94780278205872\n",
            "Epoch: 12, Training Loss: 0.02, Validation Loss: 0.06, accuracy = 0.78, time: 121.22021174430847\n",
            "Epoch: 13, Training Loss: 0.02, Validation Loss: 0.06, accuracy = 0.80, time: 121.30251693725586\n",
            "Epoch: 14, Training Loss: 0.02, Validation Loss: 0.06, accuracy = 0.80, time: 123.78286790847778\n",
            "Epoch: 15, Training Loss: 0.01, Validation Loss: 0.07, accuracy = 0.78, time: 123.08036923408508\n",
            "Epoch: 16, Training Loss: 0.01, Validation Loss: 0.07, accuracy = 0.80, time: 122.69875836372375\n",
            "Epoch: 17, Training Loss: 0.01, Validation Loss: 0.06, accuracy = 0.79, time: 122.46563649177551\n",
            "Epoch: 18, Training Loss: 0.01, Validation Loss: 0.09, accuracy = 0.78, time: 122.63345241546631\n",
            "Epoch: 19, Training Loss: 0.01, Validation Loss: 0.07, accuracy = 0.80, time: 123.79559206962585\n",
            "Epoch: 20, Training Loss: 0.01, Validation Loss: 0.09, accuracy = 0.80, time: 123.12882828712463\n",
            "Epoch: 21, Training Loss: 0.01, Validation Loss: 0.09, accuracy = 0.79, time: 122.45837426185608\n",
            "Epoch: 22, Training Loss: 0.01, Validation Loss: 0.12, accuracy = 0.79, time: 122.16413164138794\n",
            "Epoch: 23, Training Loss: 0.01, Validation Loss: 0.07, accuracy = 0.80, time: 122.67180442810059\n",
            "Epoch: 24, Training Loss: 0.01, Validation Loss: 0.07, accuracy = 0.80, time: 123.36309957504272\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sW7SPT64caC7"
      },
      "source": [
        "\n",
        "## Downloading a pretrained network\n",
        "There are two ways of downloading pre-trained image models with PyTorch. Firstly, you can use the torchvision.models library, or you can use PyTorch Hub. The latter is preferred as of 2019, as this is a one-stop shop for all models and the new standard for distributing models with PyTorch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwiswoKJcZAC"
      },
      "source": [
        "import torchvision.models as models\n",
        "alexnet = models.alexnet(num_classes=1000, pretrained=True)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7Gxktzeccex",
        "outputId": "23ddef5f-b68a-4d3a-dcf3-b08ff9f18612",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "resnet50 = torch.hub.load('pytorch/vision', 'resnet50')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/pytorch_vision_master\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kCFoK1r-cerL",
        "outputId": "3b620e7e-938f-412c-f52b-840dd9283172",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "print(alexnet)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AlexNet(\n",
            "  (features): Sequential(\n",
            "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
            "    (4): ReLU(inplace=True)\n",
            "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
            "  (classifier): Sequential(\n",
            "    (0): Dropout(p=0.5, inplace=False)\n",
            "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
            "    (2): ReLU(inplace=True)\n",
            "    (3): Dropout(p=0.5, inplace=False)\n",
            "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
            "  )\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPPgAcexcgTm",
        "outputId": "652a19f0-a6dd-49e2-efcf-62e24cad0f42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(resnet50)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ResNet(\n",
            "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
            "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (relu): ReLU(inplace=True)\n",
            "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
            "  (layer1): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (2): Bottleneck(\n",
            "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (layer2): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (2): Bottleneck(\n",
            "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (3): Bottleneck(\n",
            "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (layer3): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (2): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (3): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (4): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (5): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (layer4): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (2): Bottleneck(\n",
            "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
            "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISoTjHdWciLk"
      },
      "source": [
        "alexnet.classifier = torch.nn.Sequential(\n",
        "            torch.nn.Dropout(),\n",
        "            torch.nn.Linear(256 * 6 * 6, 4096),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.Dropout(),\n",
        "            torch.nn.Linear(4096, 4096),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.Linear(4096, 2)\n",
        "        )"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCWBUorZp-GI"
      },
      "source": [
        "alexnet.to(device)\n",
        "optimizer = torch.optim.Adam(alexnet.parameters(), lr=0.00005)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7srswNMqE7P",
        "outputId": "0c068354-3903-4f15-bdb3-ce13cb8689dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "train(alexnet, optimizer,torch.nn.CrossEntropyLoss(), train_data_loader,val_data_loader, epochs=25, device=device)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0, Training Loss: 0.04, Validation Loss: 0.04, accuracy = 0.84, time: 114.83827948570251\n",
            "Epoch: 1, Training Loss: 0.04, Validation Loss: 0.04, accuracy = 0.84, time: 114.16915845870972\n",
            "Epoch: 2, Training Loss: 0.03, Validation Loss: 0.03, accuracy = 0.85, time: 115.19968914985657\n",
            "Epoch: 3, Training Loss: 0.03, Validation Loss: 0.03, accuracy = 0.85, time: 114.92985272407532\n",
            "Epoch: 4, Training Loss: 0.02, Validation Loss: 0.04, accuracy = 0.85, time: 114.98709869384766\n",
            "Epoch: 5, Training Loss: 0.02, Validation Loss: 0.04, accuracy = 0.85, time: 114.97286581993103\n",
            "Epoch: 6, Training Loss: 0.02, Validation Loss: 0.04, accuracy = 0.86, time: 115.00862884521484\n",
            "Epoch: 7, Training Loss: 0.01, Validation Loss: 0.04, accuracy = 0.85, time: 114.64329528808594\n",
            "Epoch: 8, Training Loss: 0.01, Validation Loss: 0.05, accuracy = 0.86, time: 114.20113921165466\n",
            "Epoch: 9, Training Loss: 0.01, Validation Loss: 0.04, accuracy = 0.85, time: 114.54360580444336\n",
            "Epoch: 10, Training Loss: 0.01, Validation Loss: 0.06, accuracy = 0.84, time: 115.26448273658752\n",
            "Epoch: 11, Training Loss: 0.01, Validation Loss: 0.06, accuracy = 0.86, time: 115.07743310928345\n",
            "Epoch: 12, Training Loss: 0.01, Validation Loss: 0.06, accuracy = 0.86, time: 115.40643787384033\n",
            "Epoch: 13, Training Loss: 0.01, Validation Loss: 0.06, accuracy = 0.87, time: 115.91563296318054\n",
            "Epoch: 14, Training Loss: 0.01, Validation Loss: 0.06, accuracy = 0.86, time: 115.84507322311401\n",
            "Epoch: 15, Training Loss: 0.01, Validation Loss: 0.07, accuracy = 0.86, time: 116.7790584564209\n",
            "Epoch: 16, Training Loss: 0.00, Validation Loss: 0.09, accuracy = 0.84, time: 117.00603652000427\n",
            "Epoch: 17, Training Loss: 0.00, Validation Loss: 0.07, accuracy = 0.86, time: 116.93038964271545\n",
            "Epoch: 18, Training Loss: 0.00, Validation Loss: 0.06, accuracy = 0.86, time: 117.17641496658325\n",
            "Epoch: 19, Training Loss: 0.00, Validation Loss: 0.07, accuracy = 0.86, time: 116.14470291137695\n",
            "Epoch: 20, Training Loss: 0.00, Validation Loss: 0.07, accuracy = 0.85, time: 115.53653407096863\n",
            "Epoch: 21, Training Loss: 0.00, Validation Loss: 0.09, accuracy = 0.86, time: 115.95072722434998\n",
            "Epoch: 23, Training Loss: 0.00, Validation Loss: 0.06, accuracy = 0.86, time: 116.73349452018738\n",
            "Epoch: 24, Training Loss: 0.00, Validation Loss: 0.07, accuracy = 0.86, time: 116.63698363304138\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQscqFRrqGmz"
      },
      "source": [
        ""
      ],
      "execution_count": 18,
      "outputs": []
    }
  ]
}