cnn_연습.ipynb

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  "metadata": {
    "colab": {
      "name": "cnn 연습.ipynb",
      "version": "0.3.2",
      "views": {},
      "default_view": {},
      "provenance": []
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "metadata": {
        "id": "516oHk05Ppam",
        "colab_type": "code",
        "colab": {
          "autoexec": {
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            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "!pip3 install torch torchvision"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ECStf8neGy14",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn  \n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Z9lnkS98GzlF",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
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      "cell_type": "code",
      "source": [
        "class Net(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Net,self).__init__()\n",
        "    # CNN 은 직접  init 함수를 구현..??\n",
        "    self.conv1 = nn.Conv2d(1,6,5)\n",
        "    self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "    self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
        "    self.fc2 = nn.Linear(120, 84)\n",
        "    self.fc3 = nn.Linear(84, 10)\n",
        "  def forward(self, x):\n",
        "\n",
        "    x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
        "    x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n",
        "    x = x.view(-1, self.num_flat_features(x))\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.relu(self.fc2(x))\n",
        "    x = self.fc3(x)\n",
        "    return x\n",
        "  \n",
        "    def num_flat_features(self, x):\n",
        "        size = x.size()[1:] \n",
        "        num_features = 1\n",
        "        for s in size:\n",
        "            num_features *= s\n",
        "        return num_features\n",
        "\n",
        "net = Net()\n",
        "print(net)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qJg73V4Y7xpw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "참고자료\n",
        "https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html"
      ]
    },
    {
      "metadata": {
        "id": "4Qg_FaXH71Fw",
        "colab_type": "code",
        "colab": {
          "autoexec": {
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      "cell_type": "code",
      "source": [
        ""
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      "execution_count": 0,
      "outputs": []
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}