alexnet.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hao1zhao/Model/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",
      "execution_count": null,
      "id": "3be8d15c",
      "metadata": {
        "id": "3be8d15c"
      },
      "outputs": [],
      "source": [
        "!pip install git+https://github.com/d2l-ai/d2l-zh@release  # installing d2l\n",
        "!pip install matplotlib_inline\n",
        "!pip install matplotlib==3.0.0"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](http://d2l.ai/_images/alexnet.svg)\n",
        ":left lenet ,right alexnet"
      ],
      "metadata": {
        "id": "oCHujVTIDuEx"
      },
      "id": "oCHujVTIDuEx"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7ae34b7",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-07T17:34:12.197336Z",
          "iopub.status.busy": "2022-12-07T17:34:12.196338Z",
          "iopub.status.idle": "2022-12-07T17:34:14.891477Z",
          "shell.execute_reply": "2022-12-07T17:34:14.890527Z"
        },
        "origin_pos": 2,
        "tab": [
          "pytorch"
        ],
        "id": "c7ae34b7"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from d2l import torch as d2l\n",
        "\n",
        "net = nn.Sequential(\n",
        "    nn.Conv2d(1, 96, kernel_size=11, stride=4, padding=1), 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",
        "    # Reduce overfitting\n",
        "    nn.Linear(6400, 4096), nn.ReLU(),\n",
        "    nn.Dropout(p=0.5),\n",
        "    nn.Linear(4096, 4096), nn.ReLU(),\n",
        "    nn.Dropout(p=0.5),\n",
        "    # Fashion-MNIST 10 category\n",
        "    nn.Linear(4096, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "37a7ec36",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-07T17:34:14.895420Z",
          "iopub.status.busy": "2022-12-07T17:34:14.895045Z",
          "iopub.status.idle": "2022-12-07T17:34:14.970835Z",
          "shell.execute_reply": "2022-12-07T17:34:14.969863Z"
        },
        "origin_pos": 7,
        "tab": [
          "pytorch"
        ],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "37a7ec36",
        "outputId": "8132a21e-b13e-4ecb-9520-fa40ac4e6070"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conv2d output shape:\t torch.Size([1, 96, 54, 54])\n",
            "ReLU output shape:\t torch.Size([1, 96, 54, 54])\n",
            "MaxPool2d output shape:\t torch.Size([1, 96, 26, 26])\n",
            "Conv2d output shape:\t torch.Size([1, 256, 26, 26])\n",
            "ReLU output shape:\t torch.Size([1, 256, 26, 26])\n",
            "MaxPool2d output shape:\t torch.Size([1, 256, 12, 12])\n",
            "Conv2d output shape:\t torch.Size([1, 384, 12, 12])\n",
            "ReLU output shape:\t torch.Size([1, 384, 12, 12])\n",
            "Conv2d output shape:\t torch.Size([1, 384, 12, 12])\n",
            "ReLU output shape:\t torch.Size([1, 384, 12, 12])\n",
            "Conv2d output shape:\t torch.Size([1, 256, 12, 12])\n",
            "ReLU output shape:\t torch.Size([1, 256, 12, 12])\n",
            "MaxPool2d output shape:\t torch.Size([1, 256, 5, 5])\n",
            "Flatten output shape:\t torch.Size([1, 6400])\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"
          ]
        }
      ],
      "source": [
        "#test\n",
        "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": "4c1552a8",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-07T17:34:14.975037Z",
          "iopub.status.busy": "2022-12-07T17:34:14.974205Z",
          "iopub.status.idle": "2022-12-07T17:34:15.060385Z",
          "shell.execute_reply": "2022-12-07T17:34:15.059420Z"
        },
        "origin_pos": 11,
        "tab": [
          "pytorch"
        ],
        "id": "4c1552a8"
      },
      "outputs": [],
      "source": [
        "#load data_set\n",
        "batch_size = 128\n",
        "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224) # fashion_mnist is 28*28"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "56310da0",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-07T17:34:15.064388Z",
          "iopub.status.busy": "2022-12-07T17:34:15.063917Z",
          "iopub.status.idle": "2022-12-07T17:37:43.885593Z",
          "shell.execute_reply": "2022-12-07T17:37:43.884709Z"
        },
        "origin_pos": 13,
        "tab": [
          "pytorch"
        ],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "56310da0",
        "outputId": "c8dc9c82-a4df-421c-d932-d1b672816762"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss 0.327, train acc 0.881, test acc 0.885\n",
            "1350.0 examples/sec on cuda:0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
            ],
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          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#train\n",
        "lr, num_epochs = 0.01, 10\n",
        "d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}