DropoutModelのSample

DropoutModelSample.ipynb

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  "metadata": {
    "colab": {
      "name": "DropoutModelSample.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPcDpmvhWWQp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "5c0df660-64ae-42b8-a418-f3d6f2332510"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchsummary import summary\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv1 = nn.Conv2d(3, 16, 5)\n",
        "        self.conv2 = nn.Conv2d(16, 32, 5)\n",
        "        self.conv3 = nn.Conv2d(32, 32, 5)\n",
        "        self.fc1 = nn.Linear(32 * 6 * 6, 256)\n",
        "        self.fc2 = nn.Linear(256, 10)\n",
        "        self.dropout1 = torch.nn.Dropout2d(p=0.2)\n",
        "        self.dropout2 = torch.nn.Dropout2d(p=0.3)\n",
        "        self.dropout3 = torch.nn.Dropout(p=0.3)\n",
        "    def forward(self, x):\n",
        "        x = self.dropout1(x)\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.dropout2(x)\n",
        "        x = F.relu(self.conv2(x))\n",
        "        x = self.dropout2(x)\n",
        "        x = F.relu(self.conv3(x))\n",
        "        x = self.dropout2(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.dropout3(x)\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "summary(Net(), (3, 32, 32))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "         Dropout2d-1            [-1, 3, 32, 32]               0\n",
            "            Conv2d-2           [-1, 16, 28, 28]           1,216\n",
            "         MaxPool2d-3           [-1, 16, 14, 14]               0\n",
            "         Dropout2d-4           [-1, 16, 14, 14]               0\n",
            "            Conv2d-5           [-1, 32, 10, 10]          12,832\n",
            "         Dropout2d-6           [-1, 32, 10, 10]               0\n",
            "            Conv2d-7             [-1, 32, 6, 6]          25,632\n",
            "         Dropout2d-8             [-1, 32, 6, 6]               0\n",
            "            Linear-9                  [-1, 256]         295,168\n",
            "          Dropout-10                  [-1, 256]               0\n",
            "           Linear-11                   [-1, 10]           2,570\n",
            "================================================================\n",
            "Total params: 337,418\n",
            "Trainable params: 337,418\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 0.24\n",
            "Params size (MB): 1.29\n",
            "Estimated Total Size (MB): 1.54\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}