CIFAR10でGAPを使ったSample

CIFAR10GAP.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CIFAR10GAP.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "3En3Msax-XDq",
        "colab_type": "code",
        "outputId": "35164ced-a305-4d9f-895f-3f9c0ecefdcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "from torchsummary import summary\n",
        "import torch.nn.functional as F\n",
        "\n",
        "BATCH_SIZE = 100\n",
        "EPOCH = 20\n",
        "PATH = \"Dataset\"\n",
        "\n",
        "transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32,32)),torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.5,), (0.5,))])\n",
        "\n",
        "trainset = torchvision.datasets.CIFAR10(root = PATH, train = True, download = True, transform = transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size = BATCH_SIZE,\n",
        "                        shuffle = True, num_workers = 2)\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root = PATH, train = False, download = True, transform = transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size = BATCH_SIZE,\n",
        "                        shuffle = False, num_workers = 2)\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 16, 5)\n",
        "        self.conv2 = nn.Conv2d(16, 32, 5)\n",
        "        self.conv3 = nn.Conv2d(32, 64, 5)\n",
        "        self.conv4 = nn.Conv2d(64, 10, 5)\n",
        "        self.avgpool = torch.nn.AdaptiveAvgPool2d((1,1))\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.conv1(x))\n",
        "        x = F.relu(self.conv2(x))\n",
        "        x = F.relu(self.conv3(x))\n",
        "        x = F.relu(self.conv4(x))\n",
        "        x = self.avgpool(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        return x\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "net = Net()\n",
        "net = net.to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(net.parameters())\n",
        "\n",
        "train_loss_value=[]      #trainingのlossを保持するlist\n",
        "train_acc_value=[]       #trainingのaccuracyを保持するlist\n",
        "test_loss_value=[]       #testのlossを保持するlist\n",
        "test_acc_value=[]        #testのaccuracyを保持するlist \n",
        "\n",
        "summary(net, (3, 32, 32))\n",
        "\n",
        "for epoch in range(EPOCH):\n",
        "    print('epoch', epoch+1)    #epoch数の出力\n",
        "    for (inputs, labels) in trainloader:\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    sum_loss = 0.0   #lossの合計\n",
        "    sum_correct = 0 #正解率の合計\n",
        "    sum_total = 0     #dataの数の合計\n",
        "\n",
        "    #train dataを使ってテストをする(パラメータ更新がないようになっている)\n",
        "    for (inputs, labels) in trainloader:\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        #lossを足していく\n",
        "        sum_loss += loss.item()\n",
        "        #出力の最大値の添字(予想位置)を取得\n",
        "        _, predicted = outputs.max(1)\n",
        "        #labelの数を足していくことでデータの総和を取る                      \n",
        "        sum_total += labels.size(0)\n",
        "        #予想位置と実際の正解を比べ,正解している数だけ足す\n",
        "        sum_correct += (predicted == labels).sum().item()\n",
        "    \n",
        "    #lossとaccuracy出力\n",
        "    print(\"train mean loss={}, accuracy={}\"\n",
        "    .format(sum_loss*BATCH_SIZE/len(trainloader.dataset), float(sum_correct/sum_total)))\n",
        "    #traindataのlossをグラフ描画のためにlistに保持\n",
        "    train_loss_value.append(sum_loss*BATCH_SIZE/len(trainloader.dataset))\n",
        "    #traindataのaccuracyをグラフ描画のためにlistに保持\n",
        "    train_acc_value.append(float(sum_correct/sum_total))\n",
        "\n",
        "    sum_loss = 0.0\n",
        "    sum_correct = 0\n",
        "    sum_total = 0\n",
        "\n",
        "    #test dataを使ってテストをする\n",
        "    for (inputs, labels) in testloader:\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        sum_loss += loss.item()\n",
        "        _, predicted = outputs.max(1)\n",
        "        sum_total += labels.size(0)\n",
        "        sum_correct += (predicted == labels).sum().item()\n",
        "    print(\"test  mean loss={}, accuracy={}\"\n",
        "    .format(sum_loss*BATCH_SIZE/len(testloader.dataset), float(sum_correct/sum_total)))\n",
        "    test_loss_value.append(sum_loss*BATCH_SIZE/len(testloader.dataset))\n",
        "    test_acc_value.append(float(sum_correct/sum_total))\n",
        "\n",
        "#グラフ\n",
        "fig, (axL, axR) = plt.subplots(ncols=2, figsize=(12,6))\n",
        "\n",
        "#損失グラフ描画\n",
        "axL.plot(range(EPOCH), train_loss_value)\n",
        "axL.plot(range(EPOCH), test_loss_value, c='#00ff00')\n",
        "axL.set_xlabel('EPOCH')\n",
        "axL.set_ylabel('LOSS')\n",
        "axL.legend(['train loss', 'test loss'])\n",
        "axL.set_title('loss')\n",
        "\n",
        "#正答率グラフ描画\n",
        "axR.plot(range(EPOCH), train_acc_value)\n",
        "axR.plot(range(EPOCH), test_acc_value, c='#00ff00')\n",
        "axR.set_xlabel('EPOCH')\n",
        "axR.set_ylabel('ACCURACY')\n",
        "axR.legend(['train acc', 'test acc'])\n",
        "axR.set_title('accuracy')\n",
        "\n",
        "fig.savefig(\"loss_accuracy_image.png\")\n",
        "fig.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 16, 28, 28]           1,216\n",
            "            Conv2d-2           [-1, 32, 24, 24]          12,832\n",
            "            Conv2d-3           [-1, 64, 20, 20]          51,264\n",
            "            Conv2d-4           [-1, 10, 16, 16]          16,010\n",
            " AdaptiveAvgPool2d-5             [-1, 10, 1, 1]               0\n",
            "================================================================\n",
            "Total params: 81,322\n",
            "Trainable params: 81,322\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 0.45\n",
            "Params size (MB): 0.31\n",
            "Estimated Total Size (MB): 0.77\n",
            "----------------------------------------------------------------\n",
            "epoch 1\n",
            "train mean loss=1.8753954305648803, accuracy=0.34898\n",
            "test  mean loss=1.876963770389557, accuracy=0.351\n",
            "epoch 2\n",
            "train mean loss=1.7881581859588622, accuracy=0.39088\n",
            "test  mean loss=1.8026284503936767, accuracy=0.3917\n",
            "epoch 3\n",
            "train mean loss=1.6759743094444275, accuracy=0.4397\n",
            "test  mean loss=1.6939589202404022, accuracy=0.4349\n",
            "epoch 4\n",
            "train mean loss=1.6510359325408936, accuracy=0.44444\n",
            "test  mean loss=1.6731702125072478, accuracy=0.4396\n",
            "epoch 5\n",
            "train mean loss=1.6047766225337983, accuracy=0.46894\n",
            "test  mean loss=1.6281603837013245, accuracy=0.4618\n",
            "epoch 6\n",
            "train mean loss=1.5690029315948486, accuracy=0.48236\n",
            "test  mean loss=1.5994542968273162, accuracy=0.4708\n",
            "epoch 7\n",
            "train mean loss=1.5360382354259492, accuracy=0.49398\n",
            "test  mean loss=1.574644582271576, accuracy=0.4839\n",
            "epoch 8\n",
            "train mean loss=1.5160074324607848, accuracy=0.49464\n",
            "test  mean loss=1.5557704830169679, accuracy=0.4859\n",
            "epoch 9\n",
            "train mean loss=1.4975026261806488, accuracy=0.50384\n",
            "test  mean loss=1.5442944002151489, accuracy=0.4925\n",
            "epoch 10\n",
            "train mean loss=1.5049279425144195, accuracy=0.4997\n",
            "test  mean loss=1.555120862722397, accuracy=0.4868\n",
            "epoch 11\n",
            "train mean loss=1.4826647455692292, accuracy=0.50918\n",
            "test  mean loss=1.5392323923110962, accuracy=0.4938\n",
            "epoch 12\n",
            "train mean loss=1.460705751657486, accuracy=0.51118\n",
            "test  mean loss=1.5212853717803956, accuracy=0.4949\n",
            "epoch 13\n",
            "train mean loss=1.4437483689785005, accuracy=0.51802\n",
            "test  mean loss=1.512642605304718, accuracy=0.5038\n",
            "epoch 14\n",
            "train mean loss=1.4193825724124909, accuracy=0.52722\n",
            "test  mean loss=1.482859982252121, accuracy=0.5088\n",
            "epoch 15\n",
            "train mean loss=1.4094636778831482, accuracy=0.5278\n",
            "test  mean loss=1.4785270190238953, accuracy=0.5053\n",
            "epoch 16\n",
            "train mean loss=1.3860473586320876, accuracy=0.5379\n",
            "test  mean loss=1.4659891474246978, accuracy=0.5155\n",
            "epoch 17\n",
            "train mean loss=1.3771601893901826, accuracy=0.54046\n",
            "test  mean loss=1.462211995124817, accuracy=0.5146\n",
            "epoch 18\n",
            "train mean loss=1.3792241594791412, accuracy=0.53896\n",
            "test  mean loss=1.4691423952579499, accuracy=0.5149\n",
            "epoch 19\n",
            "train mean loss=1.3409191426038742, accuracy=0.5542\n",
            "test  mean loss=1.436096396446228, accuracy=0.5248\n",
            "epoch 20\n",
            "train mean loss=1.3407690443992615, accuracy=0.55284\n",
            "test  mean loss=1.4437957155704497, accuracy=0.5233\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}