2-1-VGG16-torch.ipynb

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  "metadata": {
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      "authorship_tag": "ABX9TyO90ZFsDvbU39lvGCdeoW6k",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JSJeong-me/Prompt-Engineering/blob/main/2-1-VGG16-torch.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,
      "metadata": {
        "id": "LwEeKPty-y5x"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class VGG16(nn.Module):\n",
        "    def __init__(self, num_classes=1000):\n",
        "        super(VGG16, self).__init__()\n",
        "\n",
        "        self.features = nn.Sequential(\n",
        "            # Conv Block 1\n",
        "            nn.Conv2d(3, 64, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "\n",
        "            # Conv Block 2\n",
        "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "\n",
        "            # Conv Block 3\n",
        "            nn.Conv2d(128, 256, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "\n",
        "            # Conv Block 4\n",
        "            nn.Conv2d(256, 512, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "\n",
        "            # Conv Block 5\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "        )\n",
        "\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Linear(512 * 7 * 7, 4096),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Dropout(),\n",
        "            nn.Linear(4096, 4096),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Dropout(),\n",
        "            nn.Linear(4096, num_classes),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.features(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = self.classifier(x)\n",
        "        return x\n",
        "\n",
        "# 모델 생성 및 요약\n",
        "model = VGG16(num_classes=1000)\n",
        "print(model)\n",
        "\n",
        "# 모델 요약을 보기 위한 라이브러리 설치\n",
        "try:\n",
        "    from torchsummary import summary\n",
        "    summary(model, (3, 224, 224))\n",
        "except ImportError:\n",
        "    print(\"torchsummary 모듈을 설치해 주세요 (pip install torchsummary).\")\n"
      ]
    }
  ]
}