AlexNet/AlexNet.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/garrisonz/reproduce/blob/main/AlexNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install d2l\n",
        "!pip install ipython-autotime\n",
        "%load_ext autotime"
      ],
      "metadata": {
        "id": "oeCpyOX-0Af4"
      },
      "id": "oeCpyOX-0Af4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "1d212cde",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-22T23:38:55.546552Z",
          "start_time": "2022-06-22T23:38:54.361426Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1d212cde",
        "outputId": "35aead45-e615-4c1e-beca-f826bc5fd16e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 1.19 s (started: 2022-06-22 23:48:02 +00:00)\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from d2l import torch as d2l"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "ed2e0930",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-22T23:45:51.412204Z",
          "start_time": "2022-06-22T23:45:51.147535Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ed2e0930",
        "outputId": "85973be2-2274-4083-cda2-06a99e563adb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 501 ms (started: 2022-06-22 23:48:03 +00:00)\n"
          ]
        }
      ],
      "source": [
        "net = nn.Sequential(nn.Conv2d(1, 96, kernel_size=11, stride=4, padding=2), nn.ReLU(),\n",
        "                    nn.MaxPool2d(kernel_size=3, stride=2),\n",
        "                    nn.Conv2d(96, 256, kernel_size=5, padding=2), nn.ReLU(),\n",
        "                    nn.MaxPool2d(kernel_size=3, stride=2),\n",
        "                    nn.Conv2d(256, 384, kernel_size=3, padding=1), nn.ReLU(),\n",
        "                    nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.ReLU(),\n",
        "                    nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(),\n",
        "                    nn.MaxPool2d(kernel_size=3, stride=2),\n",
        "                    nn.Flatten(),\n",
        "                    nn.Linear(256 * 6 * 6, 4096), nn.ReLU(), nn.Dropout(p=0.5),\n",
        "                    nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(p=0.5),\n",
        "                    nn.Linear(4096, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "8a408005",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-22T23:45:52.439525Z",
          "start_time": "2022-06-22T23:45:52.406855Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8a408005",
        "outputId": "38fb0cfc-7b42-48d5-eecc-e73a2fd05456"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conv2d output shape:\t torch.Size([1, 96, 55, 55])\n",
            "ReLU output shape:\t torch.Size([1, 96, 55, 55])\n",
            "MaxPool2d output shape:\t torch.Size([1, 96, 27, 27])\n",
            "Conv2d output shape:\t torch.Size([1, 256, 27, 27])\n",
            "ReLU output shape:\t torch.Size([1, 256, 27, 27])\n",
            "MaxPool2d output shape:\t torch.Size([1, 256, 13, 13])\n",
            "Conv2d output shape:\t torch.Size([1, 384, 13, 13])\n",
            "ReLU output shape:\t torch.Size([1, 384, 13, 13])\n",
            "Conv2d output shape:\t torch.Size([1, 384, 13, 13])\n",
            "ReLU output shape:\t torch.Size([1, 384, 13, 13])\n",
            "Conv2d output shape:\t torch.Size([1, 256, 13, 13])\n",
            "ReLU output shape:\t torch.Size([1, 256, 13, 13])\n",
            "MaxPool2d output shape:\t torch.Size([1, 256, 6, 6])\n",
            "Flatten output shape:\t torch.Size([1, 9216])\n",
            "Linear output shape:\t torch.Size([1, 4096])\n",
            "ReLU output shape:\t torch.Size([1, 4096])\n",
            "Dropout output shape:\t torch.Size([1, 4096])\n",
            "Linear output shape:\t torch.Size([1, 4096])\n",
            "ReLU output shape:\t torch.Size([1, 4096])\n",
            "Dropout output shape:\t torch.Size([1, 4096])\n",
            "Linear output shape:\t torch.Size([1, 10])\n",
            "time: 283 ms (started: 2022-06-22 23:48:04 +00:00)\n"
          ]
        }
      ],
      "source": [
        "X = torch.randn(1, 1, 224, 224)\n",
        "for layer in net:\n",
        "    X=layer(X)\n",
        "    print(layer.__class__.__name__,'output shape:\\t',X.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "71eb84ff",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-22T23:45:57.616075Z",
          "start_time": "2022-06-22T23:45:57.575537Z"
        },
        "id": "71eb84ff"
      },
      "outputs": [],
      "source": [
        "batch_size = 128\n",
        "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "d6d4a644",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-22T23:46:06.806276Z",
          "start_time": "2022-06-22T23:46:00.911943Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "d6d4a644",
        "outputId": "201314a9-fe9e-4798-a4a2-d2f6fe649a47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss 0.314, train acc 0.886, test acc 0.887\n",
            "1654.7 examples/sec on cuda:0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 7min 36s (started: 2022-06-22 23:48:11 +00:00)\n"
          ]
        }
      ],
      "source": [
        "lr, num_epochs = 0.01, 10\n",
        "d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())"
      ]
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