experimentation.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHX7hffPkM6W",
        "outputId": "faa611b3-6d66-4a98-e41d-3491d900cb1d"
      },
      "outputs": [],
      "source": [
        "from arch.vgg import VGG\n",
        "from arch.resnet import WideResNet\n",
        "from arch.googlenet import GoogLeNet\n",
        "from arch.dataloader import load_data\n",
        "\n",
        "%load_ext autotime"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bffX8aCf1gsb"
      },
      "source": [
        "# VGG Main"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "QeHxpdCq73Qd",
        "outputId": "ee57c27d-9c93-4f5f-df04-b18f23ba47d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train: 60000\n",
            "Test: 10000\n",
            "Image structure: (100, 1, 32, 32)\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 1.15 s (started: 2021-04-25 17:58:11 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Load Data MNIST\n",
        "batch_size = 100\n",
        "train_loader, test_loader, valid_loader = load_data('MNIST', batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGX2NhUKlcfC",
        "outputId": "bad7d4fd-3ec8-44b7-c021-c7dbb3767866"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "VGG(\n",
            "  (convs): Sequential(\n",
            "    (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): ReLU(inplace=True)\n",
            "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (12): ReLU(inplace=True)\n",
            "    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (16): ReLU(inplace=True)\n",
            "    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (19): ReLU(inplace=True)\n",
            "    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (22): ReLU(inplace=True)\n",
            "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (26): ReLU(inplace=True)\n",
            "    (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (29): ReLU(inplace=True)\n",
            "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (32): ReLU(inplace=True)\n",
            "    (33): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (36): ReLU(inplace=True)\n",
            "    (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (39): ReLU(inplace=True)\n",
            "    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (42): ReLU(inplace=True)\n",
            "    (43): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (fully_connected): Sequential(\n",
            "    (0): Linear(in_features=512, out_features=128, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Dropout(p=0.2, inplace=False)\n",
            "    (3): Linear(in_features=128, out_features=128, bias=True)\n",
            "    (4): ReLU(inplace=True)\n",
            "    (5): Dropout(p=0.2, inplace=False)\n",
            "    (6): Linear(in_features=128, out_features=10, bias=True)\n",
            "  )\n",
            ")\n",
            "time: 135 ms (started: 2021-04-25 17:58:17 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Create Model\n",
        "model = VGG(arch='VGG16', \n",
        "            channels=1, \n",
        "            num_classes=10)\n",
        "\n",
        "print(model)\n",
        "print(\"No of parmeter:\",sum(p.numel() for p in model.parameters() if p.requires_grad))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PXjmjxtntm-K",
        "outputId": "c9460e25-4f9f-480c-835f-01001d4569a2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1 \tTraining Loss: 1.519695 \tValidation Loss: 1.032984\n",
            "Validation loss decreased (inf --> 1.032984).  Saving model ...\n",
            "Epoch: 2 \tTraining Loss: 0.718840 \tValidation Loss: 0.537401\n",
            "Validation loss decreased (1.032984 --> 0.537401).  Saving model ...\n",
            "Epoch: 3 \tTraining Loss: 0.517267 \tValidation Loss: 0.287810\n",
            "Validation loss decreased (0.537401 --> 0.287810).  Saving model ...\n",
            "Epoch: 4 \tTraining Loss: 0.341490 \tValidation Loss: 0.227533\n",
            "Validation loss decreased (0.287810 --> 0.227533).  Saving model ...\n",
            "Epoch: 5 \tTraining Loss: 0.262816 \tValidation Loss: 0.213715\n",
            "Validation loss decreased (0.227533 --> 0.213715).  Saving model ...\n",
            "Epoch: 6 \tTraining Loss: 0.226823 \tValidation Loss: 0.218859\n",
            "Epoch: 7 \tTraining Loss: 0.220878 \tValidation Loss: 0.144112\n",
            "Validation loss decreased (0.213715 --> 0.144112).  Saving model ...\n",
            "Epoch: 8 \tTraining Loss: 0.211850 \tValidation Loss: 0.147838\n",
            "Epoch: 9 \tTraining Loss: 0.177854 \tValidation Loss: 0.151883\n",
            "Epoch: 10 \tTraining Loss: 0.167184 \tValidation Loss: 0.110474\n",
            "Validation loss decreased (0.144112 --> 0.110474).  Saving model ...\n",
            "Epoch: 11 \tTraining Loss: 0.155551 \tValidation Loss: 0.104608\n",
            "Validation loss decreased (0.110474 --> 0.104608).  Saving model ...\n",
            "Epoch: 12 \tTraining Loss: 0.147948 \tValidation Loss: 0.117341\n",
            "Epoch: 13 \tTraining Loss: 0.138010 \tValidation Loss: 0.086396\n",
            "Validation loss decreased (0.104608 --> 0.086396).  Saving model ...\n",
            "Epoch: 14 \tTraining Loss: 0.136390 \tValidation Loss: 0.079047\n",
            "Validation loss decreased (0.086396 --> 0.079047).  Saving model ...\n",
            "Epoch: 15 \tTraining Loss: 0.142981 \tValidation Loss: 0.085701\n",
            "Epoch: 16 \tTraining Loss: 0.133558 \tValidation Loss: 0.088818\n",
            "Epoch: 17 \tTraining Loss: 0.132126 \tValidation Loss: 0.212888\n",
            "Epoch: 18 \tTraining Loss: 0.122602 \tValidation Loss: 0.115406\n",
            "Epoch: 19 \tTraining Loss: 0.111833 \tValidation Loss: 0.087519\n",
            "Epoch: 20 \tTraining Loss: 0.113250 \tValidation Loss: 0.106445\n",
            "Epoch: 21 \tTraining Loss: 0.109296 \tValidation Loss: 0.069703\n",
            "Validation loss decreased (0.079047 --> 0.069703).  Saving model ...\n",
            "Epoch: 22 \tTraining Loss: 0.116008 \tValidation Loss: 0.083171\n",
            "Epoch: 23 \tTraining Loss: 0.102147 \tValidation Loss: 0.070054\n",
            "Epoch: 24 \tTraining Loss: 0.085360 \tValidation Loss: 0.071496\n",
            "Epoch: 25 \tTraining Loss: 0.089697 \tValidation Loss: 0.063521\n",
            "Validation loss decreased (0.069703 --> 0.063521).  Saving model ...\n",
            "Epoch: 26 \tTraining Loss: 0.109116 \tValidation Loss: 0.060579\n",
            "Validation loss decreased (0.063521 --> 0.060579).  Saving model ...\n",
            "Epoch: 27 \tTraining Loss: 0.091276 \tValidation Loss: 0.072278\n",
            "Epoch: 28 \tTraining Loss: 0.085110 \tValidation Loss: 0.167783\n",
            "Epoch: 29 \tTraining Loss: 0.100572 \tValidation Loss: 0.078695\n",
            "Epoch: 30 \tTraining Loss: 0.073850 \tValidation Loss: 0.064541\n",
            "Epoch: 31 \tTraining Loss: 0.092372 \tValidation Loss: 0.097021\n",
            "Epoch: 32 \tTraining Loss: 0.070519 \tValidation Loss: 0.057304\n",
            "Validation loss decreased (0.060579 --> 0.057304).  Saving model ...\n",
            "Epoch: 33 \tTraining Loss: 0.080167 \tValidation Loss: 0.069074\n",
            "Epoch: 34 \tTraining Loss: 0.072156 \tValidation Loss: 0.061591\n",
            "Epoch: 35 \tTraining Loss: 0.064643 \tValidation Loss: 0.070667\n",
            "Epoch: 36 \tTraining Loss: 0.065945 \tValidation Loss: 0.054960\n",
            "Validation loss decreased (0.057304 --> 0.054960).  Saving model ...\n",
            "Epoch: 37 \tTraining Loss: 0.075388 \tValidation Loss: 0.071600\n",
            "Epoch: 38 \tTraining Loss: 0.069211 \tValidation Loss: 0.070415\n",
            "Epoch: 39 \tTraining Loss: 0.085042 \tValidation Loss: 0.075076\n",
            "Epoch: 40 \tTraining Loss: 0.065854 \tValidation Loss: 0.058945\n",
            "Epoch: 41 \tTraining Loss: 0.063332 \tValidation Loss: 0.068956\n",
            "Epoch: 42 \tTraining Loss: 0.062844 \tValidation Loss: 0.050462\n",
            "Validation loss decreased (0.054960 --> 0.050462).  Saving model ...\n",
            "Epoch: 43 \tTraining Loss: 0.080582 \tValidation Loss: 0.078513\n",
            "Epoch: 44 \tTraining Loss: 0.057997 \tValidation Loss: 0.054614\n",
            "Epoch: 45 \tTraining Loss: 0.059168 \tValidation Loss: 0.073603\n",
            "Epoch: 46 \tTraining Loss: 0.069690 \tValidation Loss: 0.066863\n",
            "Epoch: 47 \tTraining Loss: 0.064551 \tValidation Loss: 0.064801\n",
            "Epoch: 48 \tTraining Loss: 0.055160 \tValidation Loss: 0.057841\n",
            "Epoch: 49 \tTraining Loss: 0.057188 \tValidation Loss: 0.057220\n",
            "Epoch: 50 \tTraining Loss: 0.067275 \tValidation Loss: 0.074275\n"
          ]
        },
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_2fd7af4d-31df-4c44-a189-79129674744e\", \"vgg16_mnist.pt\", 59288920)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 35min 42s (started: 2021-04-25 17:58:26 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Train Model With Early Stopping\n",
        "train(model,\n",
        "      train_loader, \n",
        "      valid_loader, \n",
        "      epochs=50,\n",
        "      name='vgg16_mnist.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECUUSZ32tPBW",
        "outputId": "e091da91-9b9c-4059-9c2d-5256d821d8ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Loss: 0.044682\n",
            "\n",
            "Test Accuracy of     0: 99% (978/980)\n",
            "Test Accuracy of     1: 99% (1132/1135)\n",
            "Test Accuracy of     2: 98% (1018/1032)\n",
            "Test Accuracy of     3: 99% (1004/1010)\n",
            "Test Accuracy of     4: 99% (975/982)\n",
            "Test Accuracy of     5: 97% (873/892)\n",
            "Test Accuracy of     6: 97% (932/958)\n",
            "Test Accuracy of     7: 99% (1019/1028)\n",
            "Test Accuracy of     8: 99% (968/974)\n",
            "Test Accuracy of     9: 99% (1005/1009)\n",
            "\n",
            "Test Accuracy (Overall): 99% (9904/10000)\n",
            "time: 6.07 s (started: 2021-04-25 18:41:15 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Test Model\n",
        "test(model, \n",
        "     test_loader, \n",
        "     name='vgg16_mnist.pt',\n",
        "     batch_size = batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
          "referenced_widgets": [
            "6fd4a3af3b2842ba8fcb3859fccefc68",
            "8fc80f25014f450aaa480bb46cb3e259",
            "fe516923a92b4b6abaa40aaa4ee6ddf2",
            "2e0ca1a329a746b198ad3a0017b118cf",
            "5958be4080cf4c7f811e663ef6627afe",
            "1f9918ae2147484bbe59ace9aa4fc0ca",
            "9c8e7f152acf4141b233d6dbd145f042",
            "22f43fa16ba049128c22cc47b28a2272"
          ]
        },
        "id": "ZKTzx1jEtqwv",
        "outputId": "286ab6fa-e271-4851-ccdb-4c9ec5bb2b3e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6fd4a3af3b2842ba8fcb3859fccefc68",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=170498071.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting data/cifar-10-python.tar.gz to data\n",
            "Files already downloaded and verified\n",
            "Train: 50000\n",
            "Test: 10000\n",
            "Image structure: (100, 3, 32, 32)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 8.21 s (started: 2021-04-26 18:21:14 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Load Data\n",
        "batch_size = 100\n",
        "train_loader, test_loader, valid_loader = load_data('CIFAR10', batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OEXtlPL29PZv",
        "outputId": "8a6d8b1b-9f22-4fa8-d0f7-cc907400ad71"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "VGG(\n",
            "  (convs): Sequential(\n",
            "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): ReLU(inplace=True)\n",
            "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (12): ReLU(inplace=True)\n",
            "    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (16): ReLU(inplace=True)\n",
            "    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (19): ReLU(inplace=True)\n",
            "    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (22): ReLU(inplace=True)\n",
            "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (26): ReLU(inplace=True)\n",
            "    (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (29): ReLU(inplace=True)\n",
            "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (32): ReLU(inplace=True)\n",
            "    (33): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (36): ReLU(inplace=True)\n",
            "    (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (39): ReLU(inplace=True)\n",
            "    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (42): ReLU(inplace=True)\n",
            "    (43): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (fully_connected): Sequential(\n",
            "    (0): Linear(in_features=512, out_features=128, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Dropout(p=0.2, inplace=False)\n",
            "    (3): Linear(in_features=128, out_features=128, bias=True)\n",
            "    (4): ReLU(inplace=True)\n",
            "    (5): Dropout(p=0.2, inplace=False)\n",
            "    (6): Linear(in_features=128, out_features=10, bias=True)\n",
            "  )\n",
            ")\n",
            "time: 114 ms (started: 2021-04-26 18:21:23 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Create Model\n",
        "model = VGG(arch='VGG16', \n",
        "            channels=3, \n",
        "            num_classes=10)\n",
        "\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DJwLbp_F9PiE",
        "outputId": "da9224c2-86c9-4f14-c4f1-27be71af6067"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1 \tTraining Loss: 2.045406 \tValidation Loss: 1.952649\n",
            "Validation loss decreased (inf --> 1.952649).  Saving model ...\n",
            "Epoch: 2 \tTraining Loss: 1.855841 \tValidation Loss: 2.096480\n",
            "Epoch: 3 \tTraining Loss: 1.746558 \tValidation Loss: 1.812680\n",
            "Validation loss decreased (1.952649 --> 1.812680).  Saving model ...\n",
            "Epoch: 4 \tTraining Loss: 1.574471 \tValidation Loss: 1.633526\n",
            "Validation loss decreased (1.812680 --> 1.633526).  Saving model ...\n",
            "Epoch: 5 \tTraining Loss: 1.439925 \tValidation Loss: 1.448602\n",
            "Validation loss decreased (1.633526 --> 1.448602).  Saving model ...\n",
            "Epoch: 6 \tTraining Loss: 1.326193 \tValidation Loss: 1.322720\n",
            "Validation loss decreased (1.448602 --> 1.322720).  Saving model ...\n",
            "Epoch: 7 \tTraining Loss: 1.209647 \tValidation Loss: 1.124419\n",
            "Validation loss decreased (1.322720 --> 1.124419).  Saving model ...\n",
            "Epoch: 8 \tTraining Loss: 1.115489 \tValidation Loss: 1.116662\n",
            "Validation loss decreased (1.124419 --> 1.116662).  Saving model ...\n",
            "Epoch: 9 \tTraining Loss: 1.042397 \tValidation Loss: 0.995980\n",
            "Validation loss decreased (1.116662 --> 0.995980).  Saving model ...\n",
            "Epoch: 10 \tTraining Loss: 1.005277 \tValidation Loss: 0.932658\n",
            "Validation loss decreased (0.995980 --> 0.932658).  Saving model ...\n",
            "Epoch: 11 \tTraining Loss: 0.930890 \tValidation Loss: 1.033810\n",
            "Epoch: 12 \tTraining Loss: 0.891547 \tValidation Loss: 0.869445\n",
            "Validation loss decreased (0.932658 --> 0.869445).  Saving model ...\n",
            "Epoch: 13 \tTraining Loss: 0.846114 \tValidation Loss: 0.793243\n",
            "Validation loss decreased (0.869445 --> 0.793243).  Saving model ...\n",
            "Epoch: 14 \tTraining Loss: 0.815173 \tValidation Loss: 0.783786\n",
            "Validation loss decreased (0.793243 --> 0.783786).  Saving model ...\n",
            "Epoch: 15 \tTraining Loss: 0.764157 \tValidation Loss: 0.800732\n",
            "Epoch: 16 \tTraining Loss: 0.714207 \tValidation Loss: 0.794930\n",
            "Epoch: 17 \tTraining Loss: 0.694254 \tValidation Loss: 0.705357\n",
            "Validation loss decreased (0.783786 --> 0.705357).  Saving model ...\n",
            "Epoch: 18 \tTraining Loss: 0.652173 \tValidation Loss: 0.680050\n",
            "Validation loss decreased (0.705357 --> 0.680050).  Saving model ...\n",
            "Epoch: 19 \tTraining Loss: 0.626776 \tValidation Loss: 0.643753\n",
            "Validation loss decreased (0.680050 --> 0.643753).  Saving model ...\n",
            "Epoch: 20 \tTraining Loss: 0.617840 \tValidation Loss: 0.767494\n",
            "Epoch: 21 \tTraining Loss: 0.613676 \tValidation Loss: 0.624171\n",
            "Validation loss decreased (0.643753 --> 0.624171).  Saving model ...\n",
            "Epoch: 22 \tTraining Loss: 0.576876 \tValidation Loss: 0.641330\n",
            "Epoch: 23 \tTraining Loss: 0.561647 \tValidation Loss: 0.630551\n",
            "Epoch: 24 \tTraining Loss: 0.536865 \tValidation Loss: 0.541276\n",
            "Validation loss decreased (0.624171 --> 0.541276).  Saving model ...\n",
            "Epoch: 25 \tTraining Loss: 0.521758 \tValidation Loss: 0.609812\n",
            "Epoch: 26 \tTraining Loss: 0.529175 \tValidation Loss: 0.733127\n",
            "Epoch: 27 \tTraining Loss: 0.507649 \tValidation Loss: 0.577414\n",
            "Epoch: 28 \tTraining Loss: 0.489503 \tValidation Loss: 0.609205\n",
            "Epoch: 29 \tTraining Loss: 0.474463 \tValidation Loss: 0.614908\n",
            "Epoch: 30 \tTraining Loss: 0.470327 \tValidation Loss: 0.587756\n",
            "Epoch: 31 \tTraining Loss: 0.447576 \tValidation Loss: 0.572840\n",
            "Epoch: 32 \tTraining Loss: 0.448801 \tValidation Loss: 0.523149\n",
            "Validation loss decreased (0.541276 --> 0.523149).  Saving model ...\n",
            "Epoch: 33 \tTraining Loss: 0.448377 \tValidation Loss: 0.566334\n",
            "Epoch: 34 \tTraining Loss: 0.424217 \tValidation Loss: 0.526044\n",
            "Epoch: 35 \tTraining Loss: 0.403533 \tValidation Loss: 0.482849\n",
            "Validation loss decreased (0.523149 --> 0.482849).  Saving model ...\n",
            "Epoch: 36 \tTraining Loss: 0.386485 \tValidation Loss: 0.503966\n",
            "Epoch: 37 \tTraining Loss: 0.426847 \tValidation Loss: 0.537233\n",
            "Epoch: 38 \tTraining Loss: 0.408365 \tValidation Loss: 0.512281\n",
            "Epoch: 39 \tTraining Loss: 0.385568 \tValidation Loss: 0.493312\n",
            "Epoch: 40 \tTraining Loss: 0.363587 \tValidation Loss: 0.533718\n",
            "Epoch: 41 \tTraining Loss: 0.370177 \tValidation Loss: 0.485339\n",
            "Epoch: 42 \tTraining Loss: 0.342793 \tValidation Loss: 0.575740\n",
            "Epoch: 43 \tTraining Loss: 0.344575 \tValidation Loss: 0.511792\n",
            "Epoch: 44 \tTraining Loss: 0.359841 \tValidation Loss: 0.523088\n",
            "Epoch: 45 \tTraining Loss: 0.319089 \tValidation Loss: 0.470950\n",
            "Validation loss decreased (0.482849 --> 0.470950).  Saving model ...\n",
            "Epoch: 46 \tTraining Loss: 0.328822 \tValidation Loss: 0.594397\n",
            "Epoch: 47 \tTraining Loss: 0.365541 \tValidation Loss: 0.489794\n",
            "Epoch: 48 \tTraining Loss: 0.328607 \tValidation Loss: 0.501804\n",
            "Epoch: 49 \tTraining Loss: 0.335043 \tValidation Loss: 0.618323\n",
            "Epoch: 50 \tTraining Loss: 0.326612 \tValidation Loss: 0.520483\n",
            "Epoch: 51 \tTraining Loss: 0.280373 \tValidation Loss: 0.496639\n",
            "Epoch: 52 \tTraining Loss: 0.278476 \tValidation Loss: 0.463976\n",
            "Validation loss decreased (0.470950 --> 0.463976).  Saving model ...\n",
            "Epoch: 53 \tTraining Loss: 0.269779 \tValidation Loss: 0.496401\n",
            "Epoch: 54 \tTraining Loss: 0.284570 \tValidation Loss: 0.524392\n",
            "Epoch: 55 \tTraining Loss: 0.262705 \tValidation Loss: 0.487824\n",
            "Epoch: 56 \tTraining Loss: 0.259391 \tValidation Loss: 0.508770\n",
            "Epoch: 57 \tTraining Loss: 0.271737 \tValidation Loss: 0.493500\n",
            "Epoch: 58 \tTraining Loss: 0.250842 \tValidation Loss: 0.513126\n",
            "Epoch: 59 \tTraining Loss: 0.250718 \tValidation Loss: 0.490744\n",
            "Epoch: 60 \tTraining Loss: 0.254631 \tValidation Loss: 0.508731\n",
            "Epoch: 61 \tTraining Loss: 0.286023 \tValidation Loss: 0.542097\n",
            "Epoch: 62 \tTraining Loss: 0.259010 \tValidation Loss: 0.544940\n",
            "Epoch: 63 \tTraining Loss: 0.240057 \tValidation Loss: 0.506324\n",
            "Epoch: 64 \tTraining Loss: 0.242751 \tValidation Loss: 0.559845\n",
            "Epoch: 65 \tTraining Loss: 0.239186 \tValidation Loss: 0.544805\n",
            "Epoch: 66 \tTraining Loss: 0.236968 \tValidation Loss: 0.497917\n",
            "Epoch: 67 \tTraining Loss: 0.215780 \tValidation Loss: 0.506773\n",
            "Epoch: 68 \tTraining Loss: 0.219790 \tValidation Loss: 0.476265\n",
            "Epoch: 69 \tTraining Loss: 0.203724 \tValidation Loss: 0.479921\n",
            "Epoch: 70 \tTraining Loss: 0.204408 \tValidation Loss: 0.501232\n",
            "Epoch: 71 \tTraining Loss: 0.190960 \tValidation Loss: 0.456826\n",
            "Validation loss decreased (0.463976 --> 0.456826).  Saving model ...\n",
            "Epoch: 72 \tTraining Loss: 0.192520 \tValidation Loss: 0.538419\n",
            "Epoch: 73 \tTraining Loss: 0.219610 \tValidation Loss: 0.472698\n",
            "Epoch: 74 \tTraining Loss: 0.190978 \tValidation Loss: 0.513669\n",
            "Epoch: 75 \tTraining Loss: 0.192078 \tValidation Loss: 0.479375\n",
            "Epoch: 76 \tTraining Loss: 0.225121 \tValidation Loss: 0.510987\n",
            "Epoch: 77 \tTraining Loss: 0.187818 \tValidation Loss: 0.525983\n",
            "Epoch: 78 \tTraining Loss: 0.183797 \tValidation Loss: 0.486253\n",
            "Epoch: 79 \tTraining Loss: 0.185525 \tValidation Loss: 0.609132\n",
            "Epoch: 80 \tTraining Loss: 0.180324 \tValidation Loss: 0.503085\n",
            "Epoch: 81 \tTraining Loss: 0.179260 \tValidation Loss: 0.499215\n",
            "Epoch: 82 \tTraining Loss: 0.166071 \tValidation Loss: 0.495226\n",
            "Epoch: 83 \tTraining Loss: 0.173530 \tValidation Loss: 0.485839\n",
            "Epoch: 84 \tTraining Loss: 0.188516 \tValidation Loss: 0.593841\n",
            "Epoch: 85 \tTraining Loss: 0.172681 \tValidation Loss: 0.484217\n",
            "Epoch: 86 \tTraining Loss: 0.157395 \tValidation Loss: 0.468540\n",
            "Epoch: 87 \tTraining Loss: 0.169538 \tValidation Loss: 0.470030\n",
            "Epoch: 88 \tTraining Loss: 0.176899 \tValidation Loss: 0.481977\n",
            "Epoch: 89 \tTraining Loss: 0.168992 \tValidation Loss: 0.530586\n",
            "Epoch: 90 \tTraining Loss: 0.154212 \tValidation Loss: 0.527288\n",
            "Epoch: 91 \tTraining Loss: 0.152800 \tValidation Loss: 0.600163\n",
            "Epoch: 92 \tTraining Loss: 0.170300 \tValidation Loss: 0.484909\n",
            "Epoch: 93 \tTraining Loss: 0.151382 \tValidation Loss: 0.595987\n",
            "Epoch: 94 \tTraining Loss: 0.157912 \tValidation Loss: 0.527949\n",
            "Epoch: 95 \tTraining Loss: 0.155193 \tValidation Loss: 0.554732\n",
            "Epoch: 96 \tTraining Loss: 0.151374 \tValidation Loss: 0.520156\n",
            "Epoch: 97 \tTraining Loss: 0.144130 \tValidation Loss: 0.541988\n",
            "Epoch: 98 \tTraining Loss: 0.127698 \tValidation Loss: 0.555346\n",
            "Epoch: 99 \tTraining Loss: 0.144928 \tValidation Loss: 0.511247\n",
            "Epoch: 100 \tTraining Loss: 0.154534 \tValidation Loss: 0.558040\n"
          ]
        },
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_de1f1ac1-0839-44f5-b06a-1265d2390cf8\", \"vgg16_cifar10.pt\", 59293528)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 51min 47s (started: 2021-04-26 18:26:44 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Train Model With Early Stopping\n",
        "train(model, \n",
        "      train_loader, \n",
        "      valid_loader, \n",
        "      epochs=100,\n",
        "      name='vgg16_cifar10.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K0fUctxM9YZd",
        "outputId": "a3b5e148-83bc-4d21-cc7b-6ab362f397fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Loss: 0.481952\n",
            "\n",
            "Test Accuracy of     0: 91% (911/1000)\n",
            "Test Accuracy of     1: 92% (927/1000)\n",
            "Test Accuracy of     2: 80% (807/1000)\n",
            "Test Accuracy of     3: 79% (796/1000)\n",
            "Test Accuracy of     4: 91% (912/1000)\n",
            "Test Accuracy of     5: 78% (788/1000)\n",
            "Test Accuracy of     6: 87% (879/1000)\n",
            "Test Accuracy of     7: 87% (873/1000)\n",
            "Test Accuracy of     8: 90% (905/1000)\n",
            "Test Accuracy of     9: 92% (922/1000)\n",
            "\n",
            "Test Accuracy (Overall): 87% (8720/10000)\n",
            "time: 4.79 s (started: 2021-04-26 19:18:32 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Test Model\n",
        "test(model, \n",
        "     test_loader, \n",
        "     name='vgg16_cifar10.pt',\n",
        "     batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4SdD01Sw1YeD"
      },
      "source": [
        "# GoogLeNet Main"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "L4TN4gygwGjE",
        "outputId": "127ffd74-001e-4475-c0be-694cf5b20caf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train: 60000\n",
            "Test: 10000\n",
            "Image structure: (100, 1, 32, 32)\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 1.14 s (started: 2021-04-25 23:00:46 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Load Data\n",
        "batch_size = 100\n",
        "train_loader, test_loader, valid_loader = load_data('MNIST', batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uiubyx9uwqLJ",
        "outputId": "54c6e382-64ab-4075-e5ae-e7d8141a03ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GoogLeNet(\n",
            "  (pre_layers): Sequential(\n",
            "    (0): Conv2d(1, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): ReLU(inplace=True)\n",
            "  )\n",
            "  (a3): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (b3): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (max_pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
            "  (a4): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (b4): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (c4): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (d4): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (e4): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (a5): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (b5): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (avgpool): AvgPool2d(kernel_size=8, stride=1, padding=0)\n",
            "  (linear): Linear(in_features=1024, out_features=10, bias=True)\n",
            ")\n",
            "time: 79.9 ms (started: 2021-04-25 23:00:47 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Create Model\n",
        "model = GoogLeNet(channels=1)\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gHXHrunQxIkU",
        "outputId": "df45c99e-714a-43f8-af38-927a06e46c43"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1 \tTraining Loss: 0.474756 \tValidation Loss: 0.235866\n",
            "Validation loss decreased (inf --> 0.235866).  Saving model ...\n",
            "Epoch: 2 \tTraining Loss: 0.110410 \tValidation Loss: 0.171602\n",
            "Validation loss decreased (0.235866 --> 0.171602).  Saving model ...\n",
            "Epoch: 3 \tTraining Loss: 0.083726 \tValidation Loss: 0.141283\n",
            "Validation loss decreased (0.171602 --> 0.141283).  Saving model ...\n",
            "Epoch: 4 \tTraining Loss: 0.074378 \tValidation Loss: 0.151407\n",
            "Epoch: 5 \tTraining Loss: 0.067750 \tValidation Loss: 0.075676\n",
            "Validation loss decreased (0.141283 --> 0.075676).  Saving model ...\n",
            "Epoch: 6 \tTraining Loss: 0.059705 \tValidation Loss: 0.065817\n",
            "Validation loss decreased (0.075676 --> 0.065817).  Saving model ...\n",
            "Epoch: 7 \tTraining Loss: 0.055335 \tValidation Loss: 0.068511\n",
            "Epoch: 8 \tTraining Loss: 0.053957 \tValidation Loss: 0.055858\n",
            "Validation loss decreased (0.065817 --> 0.055858).  Saving model ...\n",
            "Epoch: 9 \tTraining Loss: 0.052741 \tValidation Loss: 0.051502\n",
            "Validation loss decreased (0.055858 --> 0.051502).  Saving model ...\n",
            "Epoch: 10 \tTraining Loss: 0.044798 \tValidation Loss: 0.050477\n",
            "Validation loss decreased (0.051502 --> 0.050477).  Saving model ...\n",
            "Epoch: 11 \tTraining Loss: 0.046272 \tValidation Loss: 0.056020\n",
            "Epoch: 12 \tTraining Loss: 0.042487 \tValidation Loss: 0.041300\n",
            "Validation loss decreased (0.050477 --> 0.041300).  Saving model ...\n",
            "Epoch: 13 \tTraining Loss: 0.040772 \tValidation Loss: 0.049039\n",
            "Epoch: 14 \tTraining Loss: 0.038625 \tValidation Loss: 0.048672\n",
            "Epoch: 15 \tTraining Loss: 0.036506 \tValidation Loss: 0.049621\n",
            "Epoch: 16 \tTraining Loss: 0.035938 \tValidation Loss: 0.036513\n",
            "Validation loss decreased (0.041300 --> 0.036513).  Saving model ...\n",
            "Epoch: 17 \tTraining Loss: 0.033459 \tValidation Loss: 0.041026\n",
            "Epoch: 18 \tTraining Loss: 0.032525 \tValidation Loss: 0.051877\n",
            "Epoch: 19 \tTraining Loss: 0.031161 \tValidation Loss: 0.039843\n",
            "Epoch: 20 \tTraining Loss: 0.029264 \tValidation Loss: 0.038982\n",
            "Epoch: 21 \tTraining Loss: 0.028220 \tValidation Loss: 0.059196\n",
            "Epoch: 22 \tTraining Loss: 0.026193 \tValidation Loss: 0.041676\n",
            "Epoch: 23 \tTraining Loss: 0.024082 \tValidation Loss: 0.039464\n",
            "Epoch: 24 \tTraining Loss: 0.025082 \tValidation Loss: 0.041677\n",
            "Epoch: 25 \tTraining Loss: 0.025290 \tValidation Loss: 0.033921\n",
            "Validation loss decreased (0.036513 --> 0.033921).  Saving model ...\n",
            "Epoch: 26 \tTraining Loss: 0.020867 \tValidation Loss: 0.029802\n",
            "Validation loss decreased (0.033921 --> 0.029802).  Saving model ...\n",
            "Epoch: 27 \tTraining Loss: 0.023013 \tValidation Loss: 0.037210\n",
            "Epoch: 28 \tTraining Loss: 0.020063 \tValidation Loss: 0.059214\n",
            "Epoch: 29 \tTraining Loss: 0.020901 \tValidation Loss: 0.038277\n",
            "Epoch: 30 \tTraining Loss: 0.020016 \tValidation Loss: 0.033412\n",
            "Epoch: 31 \tTraining Loss: 0.018781 \tValidation Loss: 0.036076\n",
            "Epoch: 32 \tTraining Loss: 0.018330 \tValidation Loss: 0.026350\n",
            "Validation loss decreased (0.029802 --> 0.026350).  Saving model ...\n",
            "Epoch: 33 \tTraining Loss: 0.018557 \tValidation Loss: 0.037258\n",
            "Epoch: 34 \tTraining Loss: 0.015951 \tValidation Loss: 0.028082\n",
            "Epoch: 35 \tTraining Loss: 0.017116 \tValidation Loss: 0.032394\n",
            "Epoch: 36 \tTraining Loss: 0.017337 \tValidation Loss: 0.036005\n",
            "Epoch: 37 \tTraining Loss: 0.015499 \tValidation Loss: 0.042919\n",
            "Epoch: 38 \tTraining Loss: 0.014503 \tValidation Loss: 0.040386\n",
            "Epoch: 39 \tTraining Loss: 0.017188 \tValidation Loss: 0.034894\n",
            "Epoch: 40 \tTraining Loss: 0.015775 \tValidation Loss: 0.036185\n",
            "Epoch: 41 \tTraining Loss: 0.013397 \tValidation Loss: 0.035553\n",
            "Epoch: 42 \tTraining Loss: 0.014853 \tValidation Loss: 0.039073\n",
            "Epoch: 43 \tTraining Loss: 0.014758 \tValidation Loss: 0.033694\n",
            "Epoch: 44 \tTraining Loss: 0.013563 \tValidation Loss: 0.025478\n",
            "Validation loss decreased (0.026350 --> 0.025478).  Saving model ...\n",
            "Epoch: 45 \tTraining Loss: 0.012878 \tValidation Loss: 0.032645\n",
            "Epoch: 46 \tTraining Loss: 0.013041 \tValidation Loss: 0.043964\n",
            "Epoch: 47 \tTraining Loss: 0.014018 \tValidation Loss: 0.039572\n",
            "Epoch: 48 \tTraining Loss: 0.011362 \tValidation Loss: 0.037354\n",
            "Epoch: 49 \tTraining Loss: 0.010536 \tValidation Loss: 0.036245\n",
            "Epoch: 50 \tTraining Loss: 0.011867 \tValidation Loss: 0.040487\n"
          ]
        },
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_e1e4f598-be2b-4345-8b10-47f8dcfd5694\", \"googlenet_mnist.pt\", 24868097)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 2h 15min 56s (started: 2021-04-25 23:00:47 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Train Model With Early Stopping\n",
        "train(model, \n",
        "      train_loader, \n",
        "      valid_loader, \n",
        "      epochs=50, \n",
        "      name='googlenet_mnist.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9aZ-1-hxInW",
        "outputId": "2d13e575-6d91-44bc-d36d-8d7f9f76b679"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Loss: 0.032583\n",
            "\n",
            "Test Accuracy of     0: 99% (978/980)\n",
            "Test Accuracy of     1: 99% (1128/1135)\n",
            "Test Accuracy of     2: 97% (1008/1032)\n",
            "Test Accuracy of     3: 99% (1007/1010)\n",
            "Test Accuracy of     4: 99% (975/982)\n",
            "Test Accuracy of     5: 98% (880/892)\n",
            "Test Accuracy of     6: 97% (938/958)\n",
            "Test Accuracy of     7: 99% (1026/1028)\n",
            "Test Accuracy of     8: 99% (970/974)\n",
            "Test Accuracy of     9: 98% (994/1009)\n",
            "\n",
            "Test Accuracy (Overall): 99% (9904/10000)\n",
            "time: 13.4 s (started: 2021-04-26 01:16:44 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Test Model\n",
        "test(model, \n",
        "     test_loader, \n",
        "     name='googlenet_mnist.pt',\n",
        "     batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "27M4Glsk-SkI",
        "outputId": "b5b50763-6a6c-4265-be70-c5fd82bda7ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Train: 50000\n",
            "Test: 10000\n",
            "Image structure: (100, 3, 32, 32)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 2.8 s (started: 2021-04-26 01:16:58 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Load Data\n",
        "train_loader, test_loader, valid_loader = load_data('CIFAR10', batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_EhsOhm-SnP",
        "outputId": "41c3572b-f4f4-4501-f52d-de8ee76f9e24"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GoogLeNet(\n",
            "  (pre_layers): Sequential(\n",
            "    (0): Conv2d(3, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): ReLU(inplace=True)\n",
            "  )\n",
            "  (a3): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (b3): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (max_pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
            "  (a4): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (b4): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (c4): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (d4): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (e4): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (a5): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (b5): Inception(\n",
            "    (b1): Sequential(\n",
            "      (0): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (b2): Sequential(\n",
            "      (0): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "    )\n",
            "    (b3): Sequential(\n",
            "      (0): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "      (3): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (5): ReLU(inplace=True)\n",
            "      (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (8): ReLU(inplace=True)\n",
            "    )\n",
            "    (b4): Sequential(\n",
            "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
            "      (1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (3): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (avgpool): AvgPool2d(kernel_size=8, stride=1, padding=0)\n",
            "  (linear): Linear(in_features=1024, out_features=10, bias=True)\n",
            ")\n",
            "time: 86.5 ms (started: 2021-04-26 01:17:01 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Create Model\n",
        "model = GoogLeNet(channels=3)\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xwOJ0yGv-St3",
        "outputId": "8639fecb-4c2a-44c9-9bbc-6238e85955db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1 \tTraining Loss: 1.797998 \tValidation Loss: 1.530040\n",
            "Validation loss decreased (inf --> 1.530040).  Saving model ...\n",
            "Epoch: 2 \tTraining Loss: 1.219956 \tValidation Loss: 1.386715\n",
            "Validation loss decreased (1.530040 --> 1.386715).  Saving model ...\n",
            "Epoch: 3 \tTraining Loss: 0.941958 \tValidation Loss: 1.015910\n",
            "Validation loss decreased (1.386715 --> 1.015910).  Saving model ...\n",
            "Epoch: 4 \tTraining Loss: 0.780740 \tValidation Loss: 0.729454\n",
            "Validation loss decreased (1.015910 --> 0.729454).  Saving model ...\n",
            "Epoch: 5 \tTraining Loss: 0.667933 \tValidation Loss: 0.750155\n",
            "Epoch: 6 \tTraining Loss: 0.590681 \tValidation Loss: 0.772718\n",
            "Epoch: 7 \tTraining Loss: 0.529690 \tValidation Loss: 0.575274\n",
            "Validation loss decreased (0.729454 --> 0.575274).  Saving model ...\n",
            "Epoch: 8 \tTraining Loss: 0.486116 \tValidation Loss: 0.580873\n",
            "Epoch: 9 \tTraining Loss: 0.446255 \tValidation Loss: 0.598825\n",
            "Epoch: 10 \tTraining Loss: 0.408491 \tValidation Loss: 0.480747\n",
            "Validation loss decreased (0.575274 --> 0.480747).  Saving model ...\n",
            "Epoch: 11 \tTraining Loss: 0.387293 \tValidation Loss: 0.552324\n",
            "Epoch: 12 \tTraining Loss: 0.357205 \tValidation Loss: 0.494847\n",
            "Epoch: 13 \tTraining Loss: 0.330507 \tValidation Loss: 0.542156\n",
            "Epoch: 14 \tTraining Loss: 0.307144 \tValidation Loss: 0.493800\n",
            "Epoch: 15 \tTraining Loss: 0.289784 \tValidation Loss: 0.399401\n",
            "Validation loss decreased (0.480747 --> 0.399401).  Saving model ...\n",
            "Epoch: 16 \tTraining Loss: 0.277457 \tValidation Loss: 0.444107\n",
            "Epoch: 17 \tTraining Loss: 0.252942 \tValidation Loss: 0.506934\n",
            "Epoch: 18 \tTraining Loss: 0.236079 \tValidation Loss: 0.408891\n",
            "Epoch: 19 \tTraining Loss: 0.225129 \tValidation Loss: 0.361067\n",
            "Validation loss decreased (0.399401 --> 0.361067).  Saving model ...\n",
            "Epoch: 20 \tTraining Loss: 0.212564 \tValidation Loss: 0.386971\n",
            "Epoch: 21 \tTraining Loss: 0.197249 \tValidation Loss: 0.480658\n",
            "Epoch: 22 \tTraining Loss: 0.186552 \tValidation Loss: 0.414430\n",
            "Epoch: 23 \tTraining Loss: 0.178720 \tValidation Loss: 0.397165\n",
            "Epoch: 24 \tTraining Loss: 0.163816 \tValidation Loss: 0.405531\n",
            "Epoch: 25 \tTraining Loss: 0.155524 \tValidation Loss: 0.415195\n",
            "Epoch: 26 \tTraining Loss: 0.153603 \tValidation Loss: 0.419671\n",
            "Epoch: 27 \tTraining Loss: 0.135785 \tValidation Loss: 0.402292\n",
            "Epoch: 28 \tTraining Loss: 0.131444 \tValidation Loss: 0.362661\n",
            "Epoch: 29 \tTraining Loss: 0.122401 \tValidation Loss: 0.376008\n",
            "Epoch: 30 \tTraining Loss: 0.122983 \tValidation Loss: 0.381322\n",
            "Epoch: 31 \tTraining Loss: 0.112444 \tValidation Loss: 0.373249\n",
            "Epoch: 32 \tTraining Loss: 0.109754 \tValidation Loss: 0.379775\n",
            "Epoch: 33 \tTraining Loss: 0.099869 \tValidation Loss: 0.446743\n",
            "Epoch: 34 \tTraining Loss: 0.096698 \tValidation Loss: 0.367404\n",
            "Epoch: 35 \tTraining Loss: 0.093049 \tValidation Loss: 0.393672\n",
            "Epoch: 36 \tTraining Loss: 0.092749 \tValidation Loss: 0.347234\n",
            "Validation loss decreased (0.361067 --> 0.347234).  Saving model ...\n",
            "Epoch: 37 \tTraining Loss: 0.089188 \tValidation Loss: 0.403472\n",
            "Epoch: 38 \tTraining Loss: 0.085849 \tValidation Loss: 0.385047\n",
            "Epoch: 39 \tTraining Loss: 0.080048 \tValidation Loss: 0.441989\n",
            "Epoch: 40 \tTraining Loss: 0.084644 \tValidation Loss: 0.439966\n",
            "Epoch: 41 \tTraining Loss: 0.075891 \tValidation Loss: 0.430556\n",
            "Epoch: 42 \tTraining Loss: 0.068318 \tValidation Loss: 0.389418\n",
            "Epoch: 43 \tTraining Loss: 0.074359 \tValidation Loss: 0.411130\n",
            "Epoch: 44 \tTraining Loss: 0.062957 \tValidation Loss: 0.441172\n",
            "Epoch: 45 \tTraining Loss: 0.070659 \tValidation Loss: 0.409590\n",
            "Epoch: 46 \tTraining Loss: 0.065801 \tValidation Loss: 0.434055\n",
            "Epoch: 47 \tTraining Loss: 0.061816 \tValidation Loss: 0.462024\n",
            "Epoch: 48 \tTraining Loss: 0.066682 \tValidation Loss: 0.386572\n",
            "Epoch: 49 \tTraining Loss: 0.056327 \tValidation Loss: 0.424624\n",
            "Epoch: 50 \tTraining Loss: 0.064289 \tValidation Loss: 0.371449\n",
            "Epoch: 51 \tTraining Loss: 0.053547 \tValidation Loss: 0.407941\n",
            "Epoch: 52 \tTraining Loss: 0.059068 \tValidation Loss: 0.384312\n",
            "Epoch: 53 \tTraining Loss: 0.054412 \tValidation Loss: 0.430108\n",
            "Epoch: 54 \tTraining Loss: 0.051517 \tValidation Loss: 0.448018\n",
            "Epoch: 55 \tTraining Loss: 0.055735 \tValidation Loss: 0.471453\n",
            "Epoch: 56 \tTraining Loss: 0.056398 \tValidation Loss: 0.376463\n",
            "Epoch: 57 \tTraining Loss: 0.046435 \tValidation Loss: 0.396329\n",
            "Epoch: 58 \tTraining Loss: 0.047159 \tValidation Loss: 0.381056\n",
            "Epoch: 59 \tTraining Loss: 0.049811 \tValidation Loss: 0.408829\n",
            "Epoch: 60 \tTraining Loss: 0.050615 \tValidation Loss: 0.407189\n",
            "Epoch: 61 \tTraining Loss: 0.051016 \tValidation Loss: 0.430718\n",
            "Epoch: 62 \tTraining Loss: 0.043463 \tValidation Loss: 0.380386\n",
            "Epoch: 63 \tTraining Loss: 0.041039 \tValidation Loss: 0.440199\n",
            "Epoch: 64 \tTraining Loss: 0.047778 \tValidation Loss: 0.421680\n",
            "Epoch: 65 \tTraining Loss: 0.042706 \tValidation Loss: 0.394917\n",
            "Epoch: 66 \tTraining Loss: 0.042455 \tValidation Loss: 0.486296\n",
            "Epoch: 67 \tTraining Loss: 0.039539 \tValidation Loss: 0.388117\n",
            "Epoch: 68 \tTraining Loss: 0.046278 \tValidation Loss: 0.482029\n",
            "Epoch: 69 \tTraining Loss: 0.036332 \tValidation Loss: 0.418125\n",
            "Epoch: 70 \tTraining Loss: 0.043747 \tValidation Loss: 0.409816\n",
            "Epoch: 71 \tTraining Loss: 0.040852 \tValidation Loss: 0.398013\n",
            "Epoch: 72 \tTraining Loss: 0.033455 \tValidation Loss: 0.440312\n",
            "Epoch: 73 \tTraining Loss: 0.038760 \tValidation Loss: 0.440786\n",
            "Epoch: 74 \tTraining Loss: 0.039660 \tValidation Loss: 0.396539\n",
            "Epoch: 75 \tTraining Loss: 0.041908 \tValidation Loss: 0.409402\n",
            "Epoch: 76 \tTraining Loss: 0.035424 \tValidation Loss: 0.381706\n",
            "Epoch: 77 \tTraining Loss: 0.035177 \tValidation Loss: 0.470014\n",
            "Epoch: 78 \tTraining Loss: 0.037947 \tValidation Loss: 0.408819\n",
            "Epoch: 79 \tTraining Loss: 0.037790 \tValidation Loss: 0.418939\n",
            "Epoch: 80 \tTraining Loss: 0.034476 \tValidation Loss: 0.418683\n",
            "Epoch: 81 \tTraining Loss: 0.035448 \tValidation Loss: 0.419153\n",
            "Epoch: 82 \tTraining Loss: 0.031951 \tValidation Loss: 0.440020\n",
            "Epoch: 83 \tTraining Loss: 0.035490 \tValidation Loss: 0.421815\n",
            "Epoch: 84 \tTraining Loss: 0.030474 \tValidation Loss: 0.441479\n",
            "Epoch: 85 \tTraining Loss: 0.029561 \tValidation Loss: 0.445557\n",
            "Epoch: 86 \tTraining Loss: 0.035262 \tValidation Loss: 0.490879\n",
            "Epoch: 87 \tTraining Loss: 0.030602 \tValidation Loss: 0.423354\n",
            "Epoch: 88 \tTraining Loss: 0.034774 \tValidation Loss: 0.414823\n",
            "Epoch: 89 \tTraining Loss: 0.027725 \tValidation Loss: 0.437951\n",
            "Epoch: 90 \tTraining Loss: 0.033836 \tValidation Loss: 0.416274\n",
            "Epoch: 91 \tTraining Loss: 0.028917 \tValidation Loss: 0.424664\n",
            "Epoch: 92 \tTraining Loss: 0.028883 \tValidation Loss: 0.440343\n",
            "Epoch: 93 \tTraining Loss: 0.034046 \tValidation Loss: 0.419962\n",
            "Epoch: 94 \tTraining Loss: 0.031766 \tValidation Loss: 0.451177\n",
            "Epoch: 95 \tTraining Loss: 0.027866 \tValidation Loss: 0.403798\n",
            "Epoch: 96 \tTraining Loss: 0.027604 \tValidation Loss: 0.435547\n",
            "Epoch: 97 \tTraining Loss: 0.033463 \tValidation Loss: 0.436774\n",
            "Epoch: 98 \tTraining Loss: 0.028088 \tValidation Loss: 0.462165\n",
            "Epoch: 99 \tTraining Loss: 0.028907 \tValidation Loss: 0.418111\n",
            "Epoch: 100 \tTraining Loss: 0.027111 \tValidation Loss: 0.569697\n"
          ]
        },
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_3bacf89c-b944-4f69-9968-07e05a26d8d7\", \"googlenet_cifar10.pt\", 24881921)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 3h 48min 46s (started: 2021-04-26 01:17:01 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Train Model\n",
        "train(model, \n",
        "      train_loader, \n",
        "      valid_loader, \n",
        "      epochs=100, \n",
        "      name='googlenet_cifar10.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkWWdZOu-Sz3",
        "outputId": "f918765d-24c3-4149-cfa8-b0c604c7dfcf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Loss: 0.375508\n",
            "\n",
            "Test Accuracy of     0: 92% (921/1000)\n",
            "Test Accuracy of     1: 96% (960/1000)\n",
            "Test Accuracy of     2: 88% (887/1000)\n",
            "Test Accuracy of     3: 80% (805/1000)\n",
            "Test Accuracy of     4: 90% (900/1000)\n",
            "Test Accuracy of     5: 80% (802/1000)\n",
            "Test Accuracy of     6: 89% (897/1000)\n",
            "Test Accuracy of     7: 95% (954/1000)\n",
            "Test Accuracy of     8: 95% (950/1000)\n",
            "Test Accuracy of     9: 89% (894/1000)\n",
            "\n",
            "Test Accuracy (Overall): 89% (8970/10000)\n",
            "time: 13.8 s (started: 2021-04-26 05:05:47 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Test Model\n",
        "test(model, \n",
        "     test_loader, \n",
        "     name='googlenet_cifar10.pt',\n",
        "     batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "raqXBN8kTGYO"
      },
      "source": [
        "# ResNet Main"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "3da8778700434b9f9a575c776710f65e",
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            "8481d1d24bd74be3a1fe149fdb61b4cd",
            "feaaf272d63c4cbd83878811aec9f144",
            "b179c2c630084f5f99d33aa2592d2a88",
            "28ec567bce6f4265864ca6feb12d370a",
            "1c6f5b5c2e2c494680dab883a4f8b22d",
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            "24833692e1c54e1aa071d29210294775",
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            "112422b2c29e47229f643a979c528131",
            "cc904d2d7c1748269780509cbadfd7c3",
            "c7009061f0dc4224a2b2b1c285a49149",
            "a73ff05f797546fa874573ed0eb0c761",
            "1a9279ab55d741668f5bff79d19d6e14",
            "e7feecbbc0b8402c812904a64e78974a",
            "10da14584e304de0ac3f8c6db667f485",
            "add21ded8d964c44af4d4249f1e3a7a8",
            "454d6b5b8eaf483c9a3848357d53feef",
            "f528a9e79c15448bbfdbc6da13dac8e1"
          ]
        },
        "id": "BW6ZbPolw765",
        "outputId": "653e4491-a1d2-4a53-cc92-d2cb93e1fe6d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "745b0c1e6bcb47b1a9cb9438ffeb7a7c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=9912422.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1e088a436bd24e5f83bea30aaa731c8b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=28881.0), HTML(value='')))"
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          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "24833692e1c54e1aa071d29210294775",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1648877.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c7009061f0dc4224a2b2b1c285a49149",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4542.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Processing...\n",
            "Done!\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train: 60000\n",
            "Test: 10000\n",
            "Image structure: (100, 1, 32, 32)\n"
          ]
        },
        {
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",
            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 4.17 s (started: 2021-04-27 06:07:06 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Load Data\n",
        "batch_size = 100\n",
        "train_loader, test_loader, valid_loader = load_data('MNIST', batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hnqMSTwPpBGp",
        "outputId": "12b33421-0bf7-45c9-f540-c28cfac33f60"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WideResNet(\n",
            "  (conv1): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "  (block1): NetworkBlock(\n",
            "    (layer): Sequential(\n",
            "      (0): BasicBlock(\n",
            "        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (1): BasicBlock(\n",
            "        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (2): BasicBlock(\n",
            "        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (3): BasicBlock(\n",
            "        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (block2): NetworkBlock(\n",
            "    (layer): Sequential(\n",
            "      (0): BasicBlock(\n",
            "        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (convShortcut): Conv2d(16, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "      )\n",
            "      (1): BasicBlock(\n",
            "        (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (2): BasicBlock(\n",
            "        (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (3): BasicBlock(\n",
            "        (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (block3): NetworkBlock(\n",
            "    (layer): Sequential(\n",
            "      (0): BasicBlock(\n",
            "        (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (convShortcut): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "      )\n",
            "      (1): BasicBlock(\n",
            "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (2): BasicBlock(\n",
            "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (3): BasicBlock(\n",
            "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (relu): ReLU(inplace=True)\n",
            "  (fc): Linear(in_features=64, out_features=10, bias=True)\n",
            ")\n",
            "time: 32 ms (started: 2021-04-27 06:07:11 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Create Model\n",
        "model = WideResNet(channels=1, depth=28, num_classes=10)\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3KdJEEZ0xJZE",
        "outputId": "41a0a711-a314-4735-e3f3-8d20a78c3825"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1 \tTraining Loss: 0.328981 \tValidation Loss: 0.894210\n",
            "Validation loss decreased (inf --> 0.894210).  Saving model ...\n",
            "Epoch: 2 \tTraining Loss: 0.119461 \tValidation Loss: 0.113145\n",
            "Validation loss decreased (0.894210 --> 0.113145).  Saving model ...\n",
            "Epoch: 3 \tTraining Loss: 0.086903 \tValidation Loss: 0.124485\n",
            "Epoch: 4 \tTraining Loss: 0.076666 \tValidation Loss: 0.072597\n",
            "Validation loss decreased (0.113145 --> 0.072597).  Saving model ...\n",
            "Epoch: 5 \tTraining Loss: 0.066494 \tValidation Loss: 0.080168\n",
            "Epoch: 6 \tTraining Loss: 0.057095 \tValidation Loss: 0.089729\n",
            "Epoch: 7 \tTraining Loss: 0.056299 \tValidation Loss: 0.075080\n",
            "Epoch: 8 \tTraining Loss: 0.050447 \tValidation Loss: 0.055518\n",
            "Validation loss decreased (0.072597 --> 0.055518).  Saving model ...\n",
            "Epoch: 9 \tTraining Loss: 0.048387 \tValidation Loss: 0.066888\n",
            "Epoch: 10 \tTraining Loss: 0.046151 \tValidation Loss: 0.057326\n",
            "Epoch: 11 \tTraining Loss: 0.042850 \tValidation Loss: 0.056419\n",
            "Epoch: 12 \tTraining Loss: 0.042015 \tValidation Loss: 0.057181\n",
            "Epoch: 13 \tTraining Loss: 0.038837 \tValidation Loss: 0.047660\n",
            "Validation loss decreased (0.055518 --> 0.047660).  Saving model ...\n",
            "Epoch: 14 \tTraining Loss: 0.037760 \tValidation Loss: 0.051890\n",
            "Epoch: 15 \tTraining Loss: 0.038295 \tValidation Loss: 0.057227\n",
            "Epoch: 16 \tTraining Loss: 0.033816 \tValidation Loss: 0.053399\n",
            "Epoch: 17 \tTraining Loss: 0.033415 \tValidation Loss: 0.071635\n",
            "Epoch: 18 \tTraining Loss: 0.032947 \tValidation Loss: 0.049143\n",
            "Epoch: 19 \tTraining Loss: 0.029664 \tValidation Loss: 0.045303\n",
            "Validation loss decreased (0.047660 --> 0.045303).  Saving model ...\n",
            "Epoch: 20 \tTraining Loss: 0.029761 \tValidation Loss: 0.045078\n",
            "Validation loss decreased (0.045303 --> 0.045078).  Saving model ...\n",
            "Epoch: 21 \tTraining Loss: 0.029929 \tValidation Loss: 0.045916\n",
            "Epoch: 22 \tTraining Loss: 0.027146 \tValidation Loss: 0.043263\n",
            "Validation loss decreased (0.045078 --> 0.043263).  Saving model ...\n",
            "Epoch: 23 \tTraining Loss: 0.026834 \tValidation Loss: 0.041804\n",
            "Validation loss decreased (0.043263 --> 0.041804).  Saving model ...\n",
            "Epoch: 24 \tTraining Loss: 0.025290 \tValidation Loss: 0.040653\n",
            "Validation loss decreased (0.041804 --> 0.040653).  Saving model ...\n",
            "Epoch: 25 \tTraining Loss: 0.025323 \tValidation Loss: 0.047528\n",
            "Epoch: 26 \tTraining Loss: 0.024582 \tValidation Loss: 0.039291\n",
            "Validation loss decreased (0.040653 --> 0.039291).  Saving model ...\n",
            "Epoch: 27 \tTraining Loss: 0.025073 \tValidation Loss: 0.036802\n",
            "Validation loss decreased (0.039291 --> 0.036802).  Saving model ...\n",
            "Epoch: 28 \tTraining Loss: 0.021568 \tValidation Loss: 0.042466\n",
            "Epoch: 29 \tTraining Loss: 0.021127 \tValidation Loss: 0.037528\n",
            "Epoch: 30 \tTraining Loss: 0.022580 \tValidation Loss: 0.039798\n",
            "Epoch: 31 \tTraining Loss: 0.020871 \tValidation Loss: 0.040507\n",
            "Epoch: 32 \tTraining Loss: 0.020565 \tValidation Loss: 0.043652\n",
            "Epoch: 33 \tTraining Loss: 0.020788 \tValidation Loss: 0.040007\n",
            "Epoch: 34 \tTraining Loss: 0.019383 \tValidation Loss: 0.038400\n",
            "Epoch: 35 \tTraining Loss: 0.019489 \tValidation Loss: 0.044932\n",
            "Epoch: 36 \tTraining Loss: 0.017850 \tValidation Loss: 0.037064\n",
            "Epoch: 37 \tTraining Loss: 0.017099 \tValidation Loss: 0.043304\n",
            "Epoch: 38 \tTraining Loss: 0.018006 \tValidation Loss: 0.037310\n",
            "Epoch: 39 \tTraining Loss: 0.016548 \tValidation Loss: 0.045483\n",
            "Epoch: 40 \tTraining Loss: 0.016388 \tValidation Loss: 0.041590\n",
            "Epoch: 41 \tTraining Loss: 0.017351 \tValidation Loss: 0.036639\n",
            "Validation loss decreased (0.036802 --> 0.036639).  Saving model ...\n",
            "Epoch: 42 \tTraining Loss: 0.016309 \tValidation Loss: 0.039167\n",
            "Epoch: 43 \tTraining Loss: 0.015735 \tValidation Loss: 0.035232\n",
            "Validation loss decreased (0.036639 --> 0.035232).  Saving model ...\n",
            "Epoch: 44 \tTraining Loss: 0.015030 \tValidation Loss: 0.042887\n",
            "Epoch: 45 \tTraining Loss: 0.016381 \tValidation Loss: 0.033310\n",
            "Validation loss decreased (0.035232 --> 0.033310).  Saving model ...\n",
            "Epoch: 46 \tTraining Loss: 0.012961 \tValidation Loss: 0.049247\n",
            "Epoch: 47 \tTraining Loss: 0.014781 \tValidation Loss: 0.042361\n",
            "Epoch: 48 \tTraining Loss: 0.014032 \tValidation Loss: 0.037962\n",
            "Epoch: 49 \tTraining Loss: 0.012858 \tValidation Loss: 0.050985\n",
            "Epoch: 50 \tTraining Loss: 0.013434 \tValidation Loss: 0.046468\n"
          ]
        },
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_4a2dfa00-01bf-4dc5-9336-cd87bbdd7fd7\", \"resnet_mnist.pt\", 1539801)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 33min 18s (started: 2021-04-27 06:07:17 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Train Model With Early Stopping\n",
        "train(model, \n",
        "      train_loader, \n",
        "      valid_loader, \n",
        "      epochs=50, \n",
        "      name='resnet_mnist.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GPUFYVLLxJcP",
        "outputId": "027e9fef-3b29-4c6d-f734-4e27ecffe851"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Loss: 0.027097\n",
            "\n",
            "Test Accuracy of     0: 99% (979/980)\n",
            "Test Accuracy of     1: 99% (1132/1135)\n",
            "Test Accuracy of     2: 98% (1013/1032)\n",
            "Test Accuracy of     3: 99% (1004/1010)\n",
            "Test Accuracy of     4: 99% (978/982)\n",
            "Test Accuracy of     5: 98% (876/892)\n",
            "Test Accuracy of     6: 98% (941/958)\n",
            "Test Accuracy of     7: 99% (1020/1028)\n",
            "Test Accuracy of     8: 99% (970/974)\n",
            "Test Accuracy of     9: 99% (1001/1009)\n",
            "\n",
            "Test Accuracy (Overall): 99% (9914/10000)\n",
            "time: 5.82 s (started: 2021-04-27 06:40:37 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Test Model\n",
        "test(model, \n",
        "     test_loader, \n",
        "     name='resnet_mnist.pt',\n",
        "     batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "M_r_myGr_dD1",
        "outputId": "2fe9f612-4f66-4365-f882-a35cc9fa0f01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Train: 50000\n",
            "Test: 10000\n",
            "Image structure: (100, 3, 32, 32)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 2.93 s (started: 2021-04-26 06:12:59 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Load Data\n",
        "train_loader, test_loader, valid_loader = load_data('CIFAR10', batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEmK7QW2_dGs",
        "outputId": "0800b753-2c9c-4325-fb63-fa7edc94c91d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WideResNet(\n",
            "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "  (block1): NetworkBlock(\n",
            "    (layer): Sequential(\n",
            "      (0): BasicBlock(\n",
            "        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (1): BasicBlock(\n",
            "        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (2): BasicBlock(\n",
            "        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (3): BasicBlock(\n",
            "        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (block2): NetworkBlock(\n",
            "    (layer): Sequential(\n",
            "      (0): BasicBlock(\n",
            "        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (convShortcut): Conv2d(16, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "      )\n",
            "      (1): BasicBlock(\n",
            "        (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (2): BasicBlock(\n",
            "        (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (3): BasicBlock(\n",
            "        (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (block3): NetworkBlock(\n",
            "    (layer): Sequential(\n",
            "      (0): BasicBlock(\n",
            "        (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (convShortcut): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "      )\n",
            "      (1): BasicBlock(\n",
            "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (2): BasicBlock(\n",
            "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "      (3): BasicBlock(\n",
            "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu1): ReLU(inplace=True)\n",
            "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu2): ReLU(inplace=True)\n",
            "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (relu): ReLU(inplace=True)\n",
            "  (fc): Linear(in_features=64, out_features=10, bias=True)\n",
            ")\n",
            "time: 22.9 ms (started: 2021-04-26 06:13:05 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Create Model\n",
        "model = WideResNet(channels=3, depth=28, num_classes=10)\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6A53H7yr_dLr",
        "outputId": "060476c1-6521-45b9-e825-dbf97ca533cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1 \tTraining Loss: 1.695886 \tValidation Loss: 1.551524\n",
            "Validation loss decreased (inf --> 1.551524).  Saving model ...\n",
            "Epoch: 2 \tTraining Loss: 1.255083 \tValidation Loss: 1.119166\n",
            "Validation loss decreased (1.551524 --> 1.119166).  Saving model ...\n",
            "Epoch: 3 \tTraining Loss: 1.001209 \tValidation Loss: 1.038624\n",
            "Validation loss decreased (1.119166 --> 1.038624).  Saving model ...\n",
            "Epoch: 4 \tTraining Loss: 0.860384 \tValidation Loss: 0.877658\n",
            "Validation loss decreased (1.038624 --> 0.877658).  Saving model ...\n",
            "Epoch: 5 \tTraining Loss: 0.754573 \tValidation Loss: 0.871738\n",
            "Validation loss decreased (0.877658 --> 0.871738).  Saving model ...\n",
            "Epoch: 6 \tTraining Loss: 0.677498 \tValidation Loss: 0.726532\n",
            "Validation loss decreased (0.871738 --> 0.726532).  Saving model ...\n",
            "Epoch: 7 \tTraining Loss: 0.625232 \tValidation Loss: 0.792708\n",
            "Epoch: 8 \tTraining Loss: 0.577023 \tValidation Loss: 0.763804\n",
            "Epoch: 9 \tTraining Loss: 0.541015 \tValidation Loss: 0.616476\n",
            "Validation loss decreased (0.726532 --> 0.616476).  Saving model ...\n",
            "Epoch: 10 \tTraining Loss: 0.516625 \tValidation Loss: 0.698682\n",
            "Epoch: 11 \tTraining Loss: 0.483706 \tValidation Loss: 0.597099\n",
            "Validation loss decreased (0.616476 --> 0.597099).  Saving model ...\n",
            "Epoch: 12 \tTraining Loss: 0.463058 \tValidation Loss: 0.610576\n",
            "Epoch: 13 \tTraining Loss: 0.435587 \tValidation Loss: 0.542278\n",
            "Validation loss decreased (0.597099 --> 0.542278).  Saving model ...\n",
            "Epoch: 14 \tTraining Loss: 0.419905 \tValidation Loss: 0.694611\n",
            "Epoch: 15 \tTraining Loss: 0.403003 \tValidation Loss: 0.490257\n",
            "Validation loss decreased (0.542278 --> 0.490257).  Saving model ...\n",
            "Epoch: 16 \tTraining Loss: 0.386173 \tValidation Loss: 0.485899\n",
            "Validation loss decreased (0.490257 --> 0.485899).  Saving model ...\n",
            "Epoch: 17 \tTraining Loss: 0.368936 \tValidation Loss: 0.506088\n",
            "Epoch: 18 \tTraining Loss: 0.354789 \tValidation Loss: 0.556965\n",
            "Epoch: 19 \tTraining Loss: 0.345669 \tValidation Loss: 0.502647\n",
            "Epoch: 20 \tTraining Loss: 0.329563 \tValidation Loss: 0.458187\n",
            "Validation loss decreased (0.485899 --> 0.458187).  Saving model ...\n",
            "Epoch: 21 \tTraining Loss: 0.322754 \tValidation Loss: 0.448254\n",
            "Validation loss decreased (0.458187 --> 0.448254).  Saving model ...\n",
            "Epoch: 22 \tTraining Loss: 0.311142 \tValidation Loss: 0.450312\n",
            "Epoch: 23 \tTraining Loss: 0.300230 \tValidation Loss: 0.527733\n",
            "Epoch: 24 \tTraining Loss: 0.284357 \tValidation Loss: 0.502319\n",
            "Epoch: 25 \tTraining Loss: 0.278176 \tValidation Loss: 0.430926\n",
            "Validation loss decreased (0.448254 --> 0.430926).  Saving model ...\n",
            "Epoch: 26 \tTraining Loss: 0.269579 \tValidation Loss: 0.475443\n",
            "Epoch: 27 \tTraining Loss: 0.260395 \tValidation Loss: 0.426418\n",
            "Validation loss decreased (0.430926 --> 0.426418).  Saving model ...\n",
            "Epoch: 28 \tTraining Loss: 0.258172 \tValidation Loss: 0.453049\n",
            "Epoch: 29 \tTraining Loss: 0.248713 \tValidation Loss: 0.466703\n",
            "Epoch: 30 \tTraining Loss: 0.239256 \tValidation Loss: 0.446327\n",
            "Epoch: 31 \tTraining Loss: 0.237925 \tValidation Loss: 0.499558\n",
            "Epoch: 32 \tTraining Loss: 0.225505 \tValidation Loss: 0.465355\n",
            "Epoch: 33 \tTraining Loss: 0.221845 \tValidation Loss: 0.463413\n",
            "Epoch: 34 \tTraining Loss: 0.213191 \tValidation Loss: 0.496156\n",
            "Epoch: 35 \tTraining Loss: 0.211281 \tValidation Loss: 0.436932\n",
            "Epoch: 36 \tTraining Loss: 0.201560 \tValidation Loss: 0.434942\n",
            "Epoch: 37 \tTraining Loss: 0.196396 \tValidation Loss: 0.443258\n",
            "Epoch: 38 \tTraining Loss: 0.190879 \tValidation Loss: 0.504275\n",
            "Epoch: 39 \tTraining Loss: 0.186320 \tValidation Loss: 0.513384\n",
            "Epoch: 40 \tTraining Loss: 0.185276 \tValidation Loss: 0.425477\n",
            "Validation loss decreased (0.426418 --> 0.425477).  Saving model ...\n",
            "Epoch: 41 \tTraining Loss: 0.173773 \tValidation Loss: 0.480282\n",
            "Epoch: 42 \tTraining Loss: 0.174061 \tValidation Loss: 0.526548\n",
            "Epoch: 43 \tTraining Loss: 0.173335 \tValidation Loss: 0.463971\n",
            "Epoch: 44 \tTraining Loss: 0.166366 \tValidation Loss: 0.482334\n",
            "Epoch: 45 \tTraining Loss: 0.166314 \tValidation Loss: 0.492196\n",
            "Epoch: 46 \tTraining Loss: 0.157839 \tValidation Loss: 0.429166\n",
            "Epoch: 47 \tTraining Loss: 0.158323 \tValidation Loss: 0.481474\n",
            "Epoch: 48 \tTraining Loss: 0.151645 \tValidation Loss: 0.499656\n",
            "Epoch: 49 \tTraining Loss: 0.149513 \tValidation Loss: 0.448929\n",
            "Epoch: 50 \tTraining Loss: 0.147726 \tValidation Loss: 0.482444\n",
            "Epoch: 51 \tTraining Loss: 0.147611 \tValidation Loss: 0.435542\n",
            "Epoch: 52 \tTraining Loss: 0.147253 \tValidation Loss: 0.430590\n",
            "Epoch: 53 \tTraining Loss: 0.135647 \tValidation Loss: 0.557131\n",
            "Epoch: 54 \tTraining Loss: 0.135557 \tValidation Loss: 0.506478\n",
            "Epoch: 55 \tTraining Loss: 0.131388 \tValidation Loss: 0.544687\n",
            "Epoch: 56 \tTraining Loss: 0.133774 \tValidation Loss: 0.474347\n",
            "Epoch: 57 \tTraining Loss: 0.131616 \tValidation Loss: 0.491298\n",
            "Epoch: 58 \tTraining Loss: 0.125706 \tValidation Loss: 0.502981\n",
            "Epoch: 59 \tTraining Loss: 0.127604 \tValidation Loss: 0.518699\n",
            "Epoch: 60 \tTraining Loss: 0.123153 \tValidation Loss: 0.455885\n",
            "Epoch: 61 \tTraining Loss: 0.121040 \tValidation Loss: 0.525416\n",
            "Epoch: 62 \tTraining Loss: 0.114333 \tValidation Loss: 0.483149\n",
            "Epoch: 63 \tTraining Loss: 0.114503 \tValidation Loss: 0.482055\n",
            "Epoch: 64 \tTraining Loss: 0.111000 \tValidation Loss: 0.478159\n",
            "Epoch: 65 \tTraining Loss: 0.115552 \tValidation Loss: 0.472890\n",
            "Epoch: 66 \tTraining Loss: 0.109036 \tValidation Loss: 0.498432\n",
            "Epoch: 67 \tTraining Loss: 0.104480 \tValidation Loss: 0.524470\n",
            "Epoch: 68 \tTraining Loss: 0.104915 \tValidation Loss: 0.457268\n",
            "Epoch: 69 \tTraining Loss: 0.108754 \tValidation Loss: 0.476068\n",
            "Epoch: 70 \tTraining Loss: 0.101750 \tValidation Loss: 0.534646\n",
            "Epoch: 71 \tTraining Loss: 0.102991 \tValidation Loss: 0.511961\n",
            "Epoch: 72 \tTraining Loss: 0.105762 \tValidation Loss: 0.514298\n",
            "Epoch: 73 \tTraining Loss: 0.103137 \tValidation Loss: 0.515738\n",
            "Epoch: 74 \tTraining Loss: 0.096689 \tValidation Loss: 0.477464\n",
            "Epoch: 75 \tTraining Loss: 0.100597 \tValidation Loss: 0.532515\n",
            "Epoch: 76 \tTraining Loss: 0.095356 \tValidation Loss: 0.502006\n",
            "Epoch: 77 \tTraining Loss: 0.093404 \tValidation Loss: 0.534840\n",
            "Epoch: 78 \tTraining Loss: 0.090080 \tValidation Loss: 0.493540\n",
            "Epoch: 79 \tTraining Loss: 0.092579 \tValidation Loss: 0.489912\n",
            "Epoch: 80 \tTraining Loss: 0.092362 \tValidation Loss: 0.533799\n",
            "Epoch: 81 \tTraining Loss: 0.088551 \tValidation Loss: 0.516784\n",
            "Epoch: 82 \tTraining Loss: 0.086010 \tValidation Loss: 0.510621\n",
            "Epoch: 83 \tTraining Loss: 0.087136 \tValidation Loss: 0.544233\n",
            "Epoch: 84 \tTraining Loss: 0.089746 \tValidation Loss: 0.516424\n",
            "Epoch: 85 \tTraining Loss: 0.083788 \tValidation Loss: 0.547621\n",
            "Epoch: 86 \tTraining Loss: 0.084317 \tValidation Loss: 0.497204\n",
            "Epoch: 87 \tTraining Loss: 0.080167 \tValidation Loss: 0.503783\n",
            "Epoch: 88 \tTraining Loss: 0.083402 \tValidation Loss: 0.540113\n",
            "Epoch: 89 \tTraining Loss: 0.080452 \tValidation Loss: 0.500681\n",
            "Epoch: 90 \tTraining Loss: 0.083513 \tValidation Loss: 0.488303\n",
            "Epoch: 91 \tTraining Loss: 0.076094 \tValidation Loss: 0.550592\n",
            "Epoch: 92 \tTraining Loss: 0.083544 \tValidation Loss: 0.624057\n",
            "Epoch: 93 \tTraining Loss: 0.078034 \tValidation Loss: 0.511870\n",
            "Epoch: 94 \tTraining Loss: 0.078788 \tValidation Loss: 0.538620\n",
            "Epoch: 95 \tTraining Loss: 0.076600 \tValidation Loss: 0.540003\n",
            "Epoch: 96 \tTraining Loss: 0.073203 \tValidation Loss: 0.553189\n",
            "Epoch: 97 \tTraining Loss: 0.075323 \tValidation Loss: 0.529940\n",
            "Epoch: 98 \tTraining Loss: 0.074614 \tValidation Loss: 0.522163\n",
            "Epoch: 99 \tTraining Loss: 0.073592 \tValidation Loss: 0.551919\n",
            "Epoch: 100 \tTraining Loss: 0.074771 \tValidation Loss: 0.557061\n"
          ]
        },
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_136eba89-f11c-4244-aff7-daa9a2fa5bd2\", \"resnet_cifar10.pt\", 1540953)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time: 52min 8s (started: 2021-04-26 06:13:12 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Train Model With Early Stopping\n",
        "train(model, \n",
        "      train_loader, \n",
        "      valid_loader, \n",
        "      epochs=100, \n",
        "      name='resnet_cifar10.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5w6xZJ4C_dQM",
        "outputId": "cf9e808a-0fa0-4c86-9520-04282b72f70c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Loss: 0.474883\n",
            "\n",
            "Test Accuracy of     0: 90% (900/1000)\n",
            "Test Accuracy of     1: 96% (961/1000)\n",
            "Test Accuracy of     2: 82% (825/1000)\n",
            "Test Accuracy of     3: 69% (699/1000)\n",
            "Test Accuracy of     4: 86% (868/1000)\n",
            "Test Accuracy of     5: 73% (738/1000)\n",
            "Test Accuracy of     6: 87% (870/1000)\n",
            "Test Accuracy of     7: 89% (896/1000)\n",
            "Test Accuracy of     8: 91% (917/1000)\n",
            "Test Accuracy of     9: 88% (885/1000)\n",
            "\n",
            "Test Accuracy (Overall): 85% (8559/10000)\n",
            "time: 5.42 s (started: 2021-04-26 07:06:00 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# Test Model\n",
        "test(model,\n",
        "     test_loader, \n",
        "     name='resnet_cifar10.pt',\n",
        "     batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4CamyBGns_Ui"
      },
      "source": [
        "# Explore The Models (Parameters, Size etc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4x9kgtrWrtgY",
        "outputId": "da5fc21b-61dc-4a85-a194-12271aeeb2b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "No of parmeter: 14805450\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 64, 32, 32]             640\n",
            "       BatchNorm2d-2           [-1, 64, 32, 32]             128\n",
            "              ReLU-3           [-1, 64, 32, 32]               0\n",
            "            Conv2d-4           [-1, 64, 32, 32]          36,928\n",
            "       BatchNorm2d-5           [-1, 64, 32, 32]             128\n",
            "              ReLU-6           [-1, 64, 32, 32]               0\n",
            "         MaxPool2d-7           [-1, 64, 16, 16]               0\n",
            "            Conv2d-8          [-1, 128, 16, 16]          73,856\n",
            "       BatchNorm2d-9          [-1, 128, 16, 16]             256\n",
            "             ReLU-10          [-1, 128, 16, 16]               0\n",
            "           Conv2d-11          [-1, 128, 16, 16]         147,584\n",
            "      BatchNorm2d-12          [-1, 128, 16, 16]             256\n",
            "             ReLU-13          [-1, 128, 16, 16]               0\n",
            "        MaxPool2d-14            [-1, 128, 8, 8]               0\n",
            "           Conv2d-15            [-1, 256, 8, 8]         295,168\n",
            "      BatchNorm2d-16            [-1, 256, 8, 8]             512\n",
            "             ReLU-17            [-1, 256, 8, 8]               0\n",
            "           Conv2d-18            [-1, 256, 8, 8]         590,080\n",
            "      BatchNorm2d-19            [-1, 256, 8, 8]             512\n",
            "             ReLU-20            [-1, 256, 8, 8]               0\n",
            "           Conv2d-21            [-1, 256, 8, 8]         590,080\n",
            "      BatchNorm2d-22            [-1, 256, 8, 8]             512\n",
            "             ReLU-23            [-1, 256, 8, 8]               0\n",
            "        MaxPool2d-24            [-1, 256, 4, 4]               0\n",
            "           Conv2d-25            [-1, 512, 4, 4]       1,180,160\n",
            "      BatchNorm2d-26            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-27            [-1, 512, 4, 4]               0\n",
            "           Conv2d-28            [-1, 512, 4, 4]       2,359,808\n",
            "      BatchNorm2d-29            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-30            [-1, 512, 4, 4]               0\n",
            "           Conv2d-31            [-1, 512, 4, 4]       2,359,808\n",
            "      BatchNorm2d-32            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-33            [-1, 512, 4, 4]               0\n",
            "        MaxPool2d-34            [-1, 512, 2, 2]               0\n",
            "           Conv2d-35            [-1, 512, 2, 2]       2,359,808\n",
            "      BatchNorm2d-36            [-1, 512, 2, 2]           1,024\n",
            "             ReLU-37            [-1, 512, 2, 2]               0\n",
            "           Conv2d-38            [-1, 512, 2, 2]       2,359,808\n",
            "      BatchNorm2d-39            [-1, 512, 2, 2]           1,024\n",
            "             ReLU-40            [-1, 512, 2, 2]               0\n",
            "           Conv2d-41            [-1, 512, 2, 2]       2,359,808\n",
            "      BatchNorm2d-42            [-1, 512, 2, 2]           1,024\n",
            "             ReLU-43            [-1, 512, 2, 2]               0\n",
            "        MaxPool2d-44            [-1, 512, 1, 1]               0\n",
            "           Linear-45                  [-1, 128]          65,664\n",
            "             ReLU-46                  [-1, 128]               0\n",
            "          Dropout-47                  [-1, 128]               0\n",
            "           Linear-48                  [-1, 128]          16,512\n",
            "             ReLU-49                  [-1, 128]               0\n",
            "          Dropout-50                  [-1, 128]               0\n",
            "           Linear-51                   [-1, 10]           1,290\n",
            "================================================================\n",
            "Total params: 14,805,450\n",
            "Trainable params: 14,805,450\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 6.57\n",
            "Params size (MB): 56.48\n",
            "Estimated Total Size (MB): 63.05\n",
            "----------------------------------------------------------------\n",
            "time: 206 ms (started: 2021-05-16 15:22:58 +00:00)\n"
          ]
        }
      ],
      "source": [
        "from torchvision import models\n",
        "from torchsummary import summary\n",
        "\n",
        "# VGG grey\n",
        "model = VGG(arch='VGG16', \n",
        "            channels=1,\n",
        "            num_classes=10)\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "model = model.to(device)\n",
        "summary(model,(1, 32, 32))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95zPNUS6s6Ho",
        "outputId": "bd13c2ef-e1fb-4ac5-c861-1ffbe871d0b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 64, 32, 32]           1,792\n",
            "       BatchNorm2d-2           [-1, 64, 32, 32]             128\n",
            "              ReLU-3           [-1, 64, 32, 32]               0\n",
            "            Conv2d-4           [-1, 64, 32, 32]          36,928\n",
            "       BatchNorm2d-5           [-1, 64, 32, 32]             128\n",
            "              ReLU-6           [-1, 64, 32, 32]               0\n",
            "         MaxPool2d-7           [-1, 64, 16, 16]               0\n",
            "            Conv2d-8          [-1, 128, 16, 16]          73,856\n",
            "       BatchNorm2d-9          [-1, 128, 16, 16]             256\n",
            "             ReLU-10          [-1, 128, 16, 16]               0\n",
            "           Conv2d-11          [-1, 128, 16, 16]         147,584\n",
            "      BatchNorm2d-12          [-1, 128, 16, 16]             256\n",
            "             ReLU-13          [-1, 128, 16, 16]               0\n",
            "        MaxPool2d-14            [-1, 128, 8, 8]               0\n",
            "           Conv2d-15            [-1, 256, 8, 8]         295,168\n",
            "      BatchNorm2d-16            [-1, 256, 8, 8]             512\n",
            "             ReLU-17            [-1, 256, 8, 8]               0\n",
            "           Conv2d-18            [-1, 256, 8, 8]         590,080\n",
            "      BatchNorm2d-19            [-1, 256, 8, 8]             512\n",
            "             ReLU-20            [-1, 256, 8, 8]               0\n",
            "           Conv2d-21            [-1, 256, 8, 8]         590,080\n",
            "      BatchNorm2d-22            [-1, 256, 8, 8]             512\n",
            "             ReLU-23            [-1, 256, 8, 8]               0\n",
            "        MaxPool2d-24            [-1, 256, 4, 4]               0\n",
            "           Conv2d-25            [-1, 512, 4, 4]       1,180,160\n",
            "      BatchNorm2d-26            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-27            [-1, 512, 4, 4]               0\n",
            "           Conv2d-28            [-1, 512, 4, 4]       2,359,808\n",
            "      BatchNorm2d-29            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-30            [-1, 512, 4, 4]               0\n",
            "           Conv2d-31            [-1, 512, 4, 4]       2,359,808\n",
            "      BatchNorm2d-32            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-33            [-1, 512, 4, 4]               0\n",
            "        MaxPool2d-34            [-1, 512, 2, 2]               0\n",
            "           Conv2d-35            [-1, 512, 2, 2]       2,359,808\n",
            "      BatchNorm2d-36            [-1, 512, 2, 2]           1,024\n",
            "             ReLU-37            [-1, 512, 2, 2]               0\n",
            "           Conv2d-38            [-1, 512, 2, 2]       2,359,808\n",
            "      BatchNorm2d-39            [-1, 512, 2, 2]           1,024\n",
            "             ReLU-40            [-1, 512, 2, 2]               0\n",
            "           Conv2d-41            [-1, 512, 2, 2]       2,359,808\n",
            "      BatchNorm2d-42            [-1, 512, 2, 2]           1,024\n",
            "             ReLU-43            [-1, 512, 2, 2]               0\n",
            "        MaxPool2d-44            [-1, 512, 1, 1]               0\n",
            "           Linear-45                  [-1, 128]          65,664\n",
            "             ReLU-46                  [-1, 128]               0\n",
            "          Dropout-47                  [-1, 128]               0\n",
            "           Linear-48                  [-1, 128]          16,512\n",
            "             ReLU-49                  [-1, 128]               0\n",
            "          Dropout-50                  [-1, 128]               0\n",
            "           Linear-51                   [-1, 10]           1,290\n",
            "================================================================\n",
            "Total params: 14,806,602\n",
            "Trainable params: 14,806,602\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 6.57\n",
            "Params size (MB): 56.48\n",
            "Estimated Total Size (MB): 63.07\n",
            "----------------------------------------------------------------\n",
            "time: 183 ms (started: 2021-05-16 15:23:33 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# VGG colour\n",
        "model = VGG(arch='VGG16', \n",
        "            channels=3, \n",
        "            num_classes=10)\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "model = model.to(device)\n",
        "summary(model,(3, 32, 32))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnZ7eiuwsuJx",
        "outputId": "23a1acd3-bac7-45a4-f41b-a4f95818abd6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1          [-1, 192, 32, 32]           1,920\n",
            "       BatchNorm2d-2          [-1, 192, 32, 32]             384\n",
            "              ReLU-3          [-1, 192, 32, 32]               0\n",
            "            Conv2d-4           [-1, 64, 32, 32]          12,352\n",
            "       BatchNorm2d-5           [-1, 64, 32, 32]             128\n",
            "              ReLU-6           [-1, 64, 32, 32]               0\n",
            "            Conv2d-7           [-1, 96, 32, 32]          18,528\n",
            "       BatchNorm2d-8           [-1, 96, 32, 32]             192\n",
            "              ReLU-9           [-1, 96, 32, 32]               0\n",
            "           Conv2d-10          [-1, 128, 32, 32]         110,720\n",
            "      BatchNorm2d-11          [-1, 128, 32, 32]             256\n",
            "             ReLU-12          [-1, 128, 32, 32]               0\n",
            "           Conv2d-13           [-1, 16, 32, 32]           3,088\n",
            "      BatchNorm2d-14           [-1, 16, 32, 32]              32\n",
            "             ReLU-15           [-1, 16, 32, 32]               0\n",
            "           Conv2d-16           [-1, 32, 32, 32]           4,640\n",
            "      BatchNorm2d-17           [-1, 32, 32, 32]              64\n",
            "             ReLU-18           [-1, 32, 32, 32]               0\n",
            "           Conv2d-19           [-1, 32, 32, 32]           9,248\n",
            "      BatchNorm2d-20           [-1, 32, 32, 32]              64\n",
            "             ReLU-21           [-1, 32, 32, 32]               0\n",
            "        MaxPool2d-22          [-1, 192, 32, 32]               0\n",
            "           Conv2d-23           [-1, 32, 32, 32]           6,176\n",
            "      BatchNorm2d-24           [-1, 32, 32, 32]              64\n",
            "             ReLU-25           [-1, 32, 32, 32]               0\n",
            "        Inception-26          [-1, 256, 32, 32]               0\n",
            "           Conv2d-27          [-1, 128, 32, 32]          32,896\n",
            "      BatchNorm2d-28          [-1, 128, 32, 32]             256\n",
            "             ReLU-29          [-1, 128, 32, 32]               0\n",
            "           Conv2d-30          [-1, 128, 32, 32]          32,896\n",
            "      BatchNorm2d-31          [-1, 128, 32, 32]             256\n",
            "             ReLU-32          [-1, 128, 32, 32]               0\n",
            "           Conv2d-33          [-1, 192, 32, 32]         221,376\n",
            "      BatchNorm2d-34          [-1, 192, 32, 32]             384\n",
            "             ReLU-35          [-1, 192, 32, 32]               0\n",
            "           Conv2d-36           [-1, 32, 32, 32]           8,224\n",
            "      BatchNorm2d-37           [-1, 32, 32, 32]              64\n",
            "             ReLU-38           [-1, 32, 32, 32]               0\n",
            "           Conv2d-39           [-1, 96, 32, 32]          27,744\n",
            "      BatchNorm2d-40           [-1, 96, 32, 32]             192\n",
            "             ReLU-41           [-1, 96, 32, 32]               0\n",
            "           Conv2d-42           [-1, 96, 32, 32]          83,040\n",
            "      BatchNorm2d-43           [-1, 96, 32, 32]             192\n",
            "             ReLU-44           [-1, 96, 32, 32]               0\n",
            "        MaxPool2d-45          [-1, 256, 32, 32]               0\n",
            "           Conv2d-46           [-1, 64, 32, 32]          16,448\n",
            "      BatchNorm2d-47           [-1, 64, 32, 32]             128\n",
            "             ReLU-48           [-1, 64, 32, 32]               0\n",
            "        Inception-49          [-1, 480, 32, 32]               0\n",
            "        MaxPool2d-50          [-1, 480, 16, 16]               0\n",
            "           Conv2d-51          [-1, 192, 16, 16]          92,352\n",
            "      BatchNorm2d-52          [-1, 192, 16, 16]             384\n",
            "             ReLU-53          [-1, 192, 16, 16]               0\n",
            "           Conv2d-54           [-1, 96, 16, 16]          46,176\n",
            "      BatchNorm2d-55           [-1, 96, 16, 16]             192\n",
            "             ReLU-56           [-1, 96, 16, 16]               0\n",
            "           Conv2d-57          [-1, 208, 16, 16]         179,920\n",
            "      BatchNorm2d-58          [-1, 208, 16, 16]             416\n",
            "             ReLU-59          [-1, 208, 16, 16]               0\n",
            "           Conv2d-60           [-1, 16, 16, 16]           7,696\n",
            "      BatchNorm2d-61           [-1, 16, 16, 16]              32\n",
            "             ReLU-62           [-1, 16, 16, 16]               0\n",
            "           Conv2d-63           [-1, 48, 16, 16]           6,960\n",
            "      BatchNorm2d-64           [-1, 48, 16, 16]              96\n",
            "             ReLU-65           [-1, 48, 16, 16]               0\n",
            "           Conv2d-66           [-1, 48, 16, 16]          20,784\n",
            "      BatchNorm2d-67           [-1, 48, 16, 16]              96\n",
            "             ReLU-68           [-1, 48, 16, 16]               0\n",
            "        MaxPool2d-69          [-1, 480, 16, 16]               0\n",
            "           Conv2d-70           [-1, 64, 16, 16]          30,784\n",
            "      BatchNorm2d-71           [-1, 64, 16, 16]             128\n",
            "             ReLU-72           [-1, 64, 16, 16]               0\n",
            "        Inception-73          [-1, 512, 16, 16]               0\n",
            "           Conv2d-74          [-1, 160, 16, 16]          82,080\n",
            "      BatchNorm2d-75          [-1, 160, 16, 16]             320\n",
            "             ReLU-76          [-1, 160, 16, 16]               0\n",
            "           Conv2d-77          [-1, 112, 16, 16]          57,456\n",
            "      BatchNorm2d-78          [-1, 112, 16, 16]             224\n",
            "             ReLU-79          [-1, 112, 16, 16]               0\n",
            "           Conv2d-80          [-1, 224, 16, 16]         226,016\n",
            "      BatchNorm2d-81          [-1, 224, 16, 16]             448\n",
            "             ReLU-82          [-1, 224, 16, 16]               0\n",
            "           Conv2d-83           [-1, 24, 16, 16]          12,312\n",
            "      BatchNorm2d-84           [-1, 24, 16, 16]              48\n",
            "             ReLU-85           [-1, 24, 16, 16]               0\n",
            "           Conv2d-86           [-1, 64, 16, 16]          13,888\n",
            "      BatchNorm2d-87           [-1, 64, 16, 16]             128\n",
            "             ReLU-88           [-1, 64, 16, 16]               0\n",
            "           Conv2d-89           [-1, 64, 16, 16]          36,928\n",
            "      BatchNorm2d-90           [-1, 64, 16, 16]             128\n",
            "             ReLU-91           [-1, 64, 16, 16]               0\n",
            "        MaxPool2d-92          [-1, 512, 16, 16]               0\n",
            "           Conv2d-93           [-1, 64, 16, 16]          32,832\n",
            "      BatchNorm2d-94           [-1, 64, 16, 16]             128\n",
            "             ReLU-95           [-1, 64, 16, 16]               0\n",
            "        Inception-96          [-1, 512, 16, 16]               0\n",
            "           Conv2d-97          [-1, 128, 16, 16]          65,664\n",
            "      BatchNorm2d-98          [-1, 128, 16, 16]             256\n",
            "             ReLU-99          [-1, 128, 16, 16]               0\n",
            "          Conv2d-100          [-1, 128, 16, 16]          65,664\n",
            "     BatchNorm2d-101          [-1, 128, 16, 16]             256\n",
            "            ReLU-102          [-1, 128, 16, 16]               0\n",
            "          Conv2d-103          [-1, 256, 16, 16]         295,168\n",
            "     BatchNorm2d-104          [-1, 256, 16, 16]             512\n",
            "            ReLU-105          [-1, 256, 16, 16]               0\n",
            "          Conv2d-106           [-1, 24, 16, 16]          12,312\n",
            "     BatchNorm2d-107           [-1, 24, 16, 16]              48\n",
            "            ReLU-108           [-1, 24, 16, 16]               0\n",
            "          Conv2d-109           [-1, 64, 16, 16]          13,888\n",
            "     BatchNorm2d-110           [-1, 64, 16, 16]             128\n",
            "            ReLU-111           [-1, 64, 16, 16]               0\n",
            "          Conv2d-112           [-1, 64, 16, 16]          36,928\n",
            "     BatchNorm2d-113           [-1, 64, 16, 16]             128\n",
            "            ReLU-114           [-1, 64, 16, 16]               0\n",
            "       MaxPool2d-115          [-1, 512, 16, 16]               0\n",
            "          Conv2d-116           [-1, 64, 16, 16]          32,832\n",
            "     BatchNorm2d-117           [-1, 64, 16, 16]             128\n",
            "            ReLU-118           [-1, 64, 16, 16]               0\n",
            "       Inception-119          [-1, 512, 16, 16]               0\n",
            "          Conv2d-120          [-1, 112, 16, 16]          57,456\n",
            "     BatchNorm2d-121          [-1, 112, 16, 16]             224\n",
            "            ReLU-122          [-1, 112, 16, 16]               0\n",
            "          Conv2d-123          [-1, 144, 16, 16]          73,872\n",
            "     BatchNorm2d-124          [-1, 144, 16, 16]             288\n",
            "            ReLU-125          [-1, 144, 16, 16]               0\n",
            "          Conv2d-126          [-1, 288, 16, 16]         373,536\n",
            "     BatchNorm2d-127          [-1, 288, 16, 16]             576\n",
            "            ReLU-128          [-1, 288, 16, 16]               0\n",
            "          Conv2d-129           [-1, 32, 16, 16]          16,416\n",
            "     BatchNorm2d-130           [-1, 32, 16, 16]              64\n",
            "            ReLU-131           [-1, 32, 16, 16]               0\n",
            "          Conv2d-132           [-1, 64, 16, 16]          18,496\n",
            "     BatchNorm2d-133           [-1, 64, 16, 16]             128\n",
            "            ReLU-134           [-1, 64, 16, 16]               0\n",
            "          Conv2d-135           [-1, 64, 16, 16]          36,928\n",
            "     BatchNorm2d-136           [-1, 64, 16, 16]             128\n",
            "            ReLU-137           [-1, 64, 16, 16]               0\n",
            "       MaxPool2d-138          [-1, 512, 16, 16]               0\n",
            "          Conv2d-139           [-1, 64, 16, 16]          32,832\n",
            "     BatchNorm2d-140           [-1, 64, 16, 16]             128\n",
            "            ReLU-141           [-1, 64, 16, 16]               0\n",
            "       Inception-142          [-1, 528, 16, 16]               0\n",
            "          Conv2d-143          [-1, 256, 16, 16]         135,424\n",
            "     BatchNorm2d-144          [-1, 256, 16, 16]             512\n",
            "            ReLU-145          [-1, 256, 16, 16]               0\n",
            "          Conv2d-146          [-1, 160, 16, 16]          84,640\n",
            "     BatchNorm2d-147          [-1, 160, 16, 16]             320\n",
            "            ReLU-148          [-1, 160, 16, 16]               0\n",
            "          Conv2d-149          [-1, 320, 16, 16]         461,120\n",
            "     BatchNorm2d-150          [-1, 320, 16, 16]             640\n",
            "            ReLU-151          [-1, 320, 16, 16]               0\n",
            "          Conv2d-152           [-1, 32, 16, 16]          16,928\n",
            "     BatchNorm2d-153           [-1, 32, 16, 16]              64\n",
            "            ReLU-154           [-1, 32, 16, 16]               0\n",
            "          Conv2d-155          [-1, 128, 16, 16]          36,992\n",
            "     BatchNorm2d-156          [-1, 128, 16, 16]             256\n",
            "            ReLU-157          [-1, 128, 16, 16]               0\n",
            "          Conv2d-158          [-1, 128, 16, 16]         147,584\n",
            "     BatchNorm2d-159          [-1, 128, 16, 16]             256\n",
            "            ReLU-160          [-1, 128, 16, 16]               0\n",
            "       MaxPool2d-161          [-1, 528, 16, 16]               0\n",
            "          Conv2d-162          [-1, 128, 16, 16]          67,712\n",
            "     BatchNorm2d-163          [-1, 128, 16, 16]             256\n",
            "            ReLU-164          [-1, 128, 16, 16]               0\n",
            "       Inception-165          [-1, 832, 16, 16]               0\n",
            "       MaxPool2d-166            [-1, 832, 8, 8]               0\n",
            "          Conv2d-167            [-1, 256, 8, 8]         213,248\n",
            "     BatchNorm2d-168            [-1, 256, 8, 8]             512\n",
            "            ReLU-169            [-1, 256, 8, 8]               0\n",
            "          Conv2d-170            [-1, 160, 8, 8]         133,280\n",
            "     BatchNorm2d-171            [-1, 160, 8, 8]             320\n",
            "            ReLU-172            [-1, 160, 8, 8]               0\n",
            "          Conv2d-173            [-1, 320, 8, 8]         461,120\n",
            "     BatchNorm2d-174            [-1, 320, 8, 8]             640\n",
            "            ReLU-175            [-1, 320, 8, 8]               0\n",
            "          Conv2d-176             [-1, 32, 8, 8]          26,656\n",
            "     BatchNorm2d-177             [-1, 32, 8, 8]              64\n",
            "            ReLU-178             [-1, 32, 8, 8]               0\n",
            "          Conv2d-179            [-1, 128, 8, 8]          36,992\n",
            "     BatchNorm2d-180            [-1, 128, 8, 8]             256\n",
            "            ReLU-181            [-1, 128, 8, 8]               0\n",
            "          Conv2d-182            [-1, 128, 8, 8]         147,584\n",
            "     BatchNorm2d-183            [-1, 128, 8, 8]             256\n",
            "            ReLU-184            [-1, 128, 8, 8]               0\n",
            "       MaxPool2d-185            [-1, 832, 8, 8]               0\n",
            "          Conv2d-186            [-1, 128, 8, 8]         106,624\n",
            "     BatchNorm2d-187            [-1, 128, 8, 8]             256\n",
            "            ReLU-188            [-1, 128, 8, 8]               0\n",
            "       Inception-189            [-1, 832, 8, 8]               0\n",
            "          Conv2d-190            [-1, 384, 8, 8]         319,872\n",
            "     BatchNorm2d-191            [-1, 384, 8, 8]             768\n",
            "            ReLU-192            [-1, 384, 8, 8]               0\n",
            "          Conv2d-193            [-1, 192, 8, 8]         159,936\n",
            "     BatchNorm2d-194            [-1, 192, 8, 8]             384\n",
            "            ReLU-195            [-1, 192, 8, 8]               0\n",
            "          Conv2d-196            [-1, 384, 8, 8]         663,936\n",
            "     BatchNorm2d-197            [-1, 384, 8, 8]             768\n",
            "            ReLU-198            [-1, 384, 8, 8]               0\n",
            "          Conv2d-199             [-1, 48, 8, 8]          39,984\n",
            "     BatchNorm2d-200             [-1, 48, 8, 8]              96\n",
            "            ReLU-201             [-1, 48, 8, 8]               0\n",
            "          Conv2d-202            [-1, 128, 8, 8]          55,424\n",
            "     BatchNorm2d-203            [-1, 128, 8, 8]             256\n",
            "            ReLU-204            [-1, 128, 8, 8]               0\n",
            "          Conv2d-205            [-1, 128, 8, 8]         147,584\n",
            "     BatchNorm2d-206            [-1, 128, 8, 8]             256\n",
            "            ReLU-207            [-1, 128, 8, 8]               0\n",
            "       MaxPool2d-208            [-1, 832, 8, 8]               0\n",
            "          Conv2d-209            [-1, 128, 8, 8]         106,624\n",
            "     BatchNorm2d-210            [-1, 128, 8, 8]             256\n",
            "            ReLU-211            [-1, 128, 8, 8]               0\n",
            "       Inception-212           [-1, 1024, 8, 8]               0\n",
            "       AvgPool2d-213           [-1, 1024, 1, 1]               0\n",
            "          Linear-214                   [-1, 10]          10,250\n",
            "================================================================\n",
            "Total params: 6,162,794\n",
            "Trainable params: 6,162,794\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 81.42\n",
            "Params size (MB): 23.51\n",
            "Estimated Total Size (MB): 104.94\n",
            "----------------------------------------------------------------\n",
            "time: 270 ms (started: 2021-05-16 15:24:20 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# GoogleNet Grey\n",
        "model = GoogLeNet(channels=1)\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "model = model.to(device)\n",
        "summary(model,(1, 32, 32))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "na5LxT0Ts77m",
        "outputId": "a1bce973-490f-4952-c09d-d4233b41e281"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1          [-1, 192, 32, 32]           5,376\n",
            "       BatchNorm2d-2          [-1, 192, 32, 32]             384\n",
            "              ReLU-3          [-1, 192, 32, 32]               0\n",
            "            Conv2d-4           [-1, 64, 32, 32]          12,352\n",
            "       BatchNorm2d-5           [-1, 64, 32, 32]             128\n",
            "              ReLU-6           [-1, 64, 32, 32]               0\n",
            "            Conv2d-7           [-1, 96, 32, 32]          18,528\n",
            "       BatchNorm2d-8           [-1, 96, 32, 32]             192\n",
            "              ReLU-9           [-1, 96, 32, 32]               0\n",
            "           Conv2d-10          [-1, 128, 32, 32]         110,720\n",
            "      BatchNorm2d-11          [-1, 128, 32, 32]             256\n",
            "             ReLU-12          [-1, 128, 32, 32]               0\n",
            "           Conv2d-13           [-1, 16, 32, 32]           3,088\n",
            "      BatchNorm2d-14           [-1, 16, 32, 32]              32\n",
            "             ReLU-15           [-1, 16, 32, 32]               0\n",
            "           Conv2d-16           [-1, 32, 32, 32]           4,640\n",
            "      BatchNorm2d-17           [-1, 32, 32, 32]              64\n",
            "             ReLU-18           [-1, 32, 32, 32]               0\n",
            "           Conv2d-19           [-1, 32, 32, 32]           9,248\n",
            "      BatchNorm2d-20           [-1, 32, 32, 32]              64\n",
            "             ReLU-21           [-1, 32, 32, 32]               0\n",
            "        MaxPool2d-22          [-1, 192, 32, 32]               0\n",
            "           Conv2d-23           [-1, 32, 32, 32]           6,176\n",
            "      BatchNorm2d-24           [-1, 32, 32, 32]              64\n",
            "             ReLU-25           [-1, 32, 32, 32]               0\n",
            "        Inception-26          [-1, 256, 32, 32]               0\n",
            "           Conv2d-27          [-1, 128, 32, 32]          32,896\n",
            "      BatchNorm2d-28          [-1, 128, 32, 32]             256\n",
            "             ReLU-29          [-1, 128, 32, 32]               0\n",
            "           Conv2d-30          [-1, 128, 32, 32]          32,896\n",
            "      BatchNorm2d-31          [-1, 128, 32, 32]             256\n",
            "             ReLU-32          [-1, 128, 32, 32]               0\n",
            "           Conv2d-33          [-1, 192, 32, 32]         221,376\n",
            "      BatchNorm2d-34          [-1, 192, 32, 32]             384\n",
            "             ReLU-35          [-1, 192, 32, 32]               0\n",
            "           Conv2d-36           [-1, 32, 32, 32]           8,224\n",
            "      BatchNorm2d-37           [-1, 32, 32, 32]              64\n",
            "             ReLU-38           [-1, 32, 32, 32]               0\n",
            "           Conv2d-39           [-1, 96, 32, 32]          27,744\n",
            "      BatchNorm2d-40           [-1, 96, 32, 32]             192\n",
            "             ReLU-41           [-1, 96, 32, 32]               0\n",
            "           Conv2d-42           [-1, 96, 32, 32]          83,040\n",
            "      BatchNorm2d-43           [-1, 96, 32, 32]             192\n",
            "             ReLU-44           [-1, 96, 32, 32]               0\n",
            "        MaxPool2d-45          [-1, 256, 32, 32]               0\n",
            "           Conv2d-46           [-1, 64, 32, 32]          16,448\n",
            "      BatchNorm2d-47           [-1, 64, 32, 32]             128\n",
            "             ReLU-48           [-1, 64, 32, 32]               0\n",
            "        Inception-49          [-1, 480, 32, 32]               0\n",
            "        MaxPool2d-50          [-1, 480, 16, 16]               0\n",
            "           Conv2d-51          [-1, 192, 16, 16]          92,352\n",
            "      BatchNorm2d-52          [-1, 192, 16, 16]             384\n",
            "             ReLU-53          [-1, 192, 16, 16]               0\n",
            "           Conv2d-54           [-1, 96, 16, 16]          46,176\n",
            "      BatchNorm2d-55           [-1, 96, 16, 16]             192\n",
            "             ReLU-56           [-1, 96, 16, 16]               0\n",
            "           Conv2d-57          [-1, 208, 16, 16]         179,920\n",
            "      BatchNorm2d-58          [-1, 208, 16, 16]             416\n",
            "             ReLU-59          [-1, 208, 16, 16]               0\n",
            "           Conv2d-60           [-1, 16, 16, 16]           7,696\n",
            "      BatchNorm2d-61           [-1, 16, 16, 16]              32\n",
            "             ReLU-62           [-1, 16, 16, 16]               0\n",
            "           Conv2d-63           [-1, 48, 16, 16]           6,960\n",
            "      BatchNorm2d-64           [-1, 48, 16, 16]              96\n",
            "             ReLU-65           [-1, 48, 16, 16]               0\n",
            "           Conv2d-66           [-1, 48, 16, 16]          20,784\n",
            "      BatchNorm2d-67           [-1, 48, 16, 16]              96\n",
            "             ReLU-68           [-1, 48, 16, 16]               0\n",
            "        MaxPool2d-69          [-1, 480, 16, 16]               0\n",
            "           Conv2d-70           [-1, 64, 16, 16]          30,784\n",
            "      BatchNorm2d-71           [-1, 64, 16, 16]             128\n",
            "             ReLU-72           [-1, 64, 16, 16]               0\n",
            "        Inception-73          [-1, 512, 16, 16]               0\n",
            "           Conv2d-74          [-1, 160, 16, 16]          82,080\n",
            "      BatchNorm2d-75          [-1, 160, 16, 16]             320\n",
            "             ReLU-76          [-1, 160, 16, 16]               0\n",
            "           Conv2d-77          [-1, 112, 16, 16]          57,456\n",
            "      BatchNorm2d-78          [-1, 112, 16, 16]             224\n",
            "             ReLU-79          [-1, 112, 16, 16]               0\n",
            "           Conv2d-80          [-1, 224, 16, 16]         226,016\n",
            "      BatchNorm2d-81          [-1, 224, 16, 16]             448\n",
            "             ReLU-82          [-1, 224, 16, 16]               0\n",
            "           Conv2d-83           [-1, 24, 16, 16]          12,312\n",
            "      BatchNorm2d-84           [-1, 24, 16, 16]              48\n",
            "             ReLU-85           [-1, 24, 16, 16]               0\n",
            "           Conv2d-86           [-1, 64, 16, 16]          13,888\n",
            "      BatchNorm2d-87           [-1, 64, 16, 16]             128\n",
            "             ReLU-88           [-1, 64, 16, 16]               0\n",
            "           Conv2d-89           [-1, 64, 16, 16]          36,928\n",
            "      BatchNorm2d-90           [-1, 64, 16, 16]             128\n",
            "             ReLU-91           [-1, 64, 16, 16]               0\n",
            "        MaxPool2d-92          [-1, 512, 16, 16]               0\n",
            "           Conv2d-93           [-1, 64, 16, 16]          32,832\n",
            "      BatchNorm2d-94           [-1, 64, 16, 16]             128\n",
            "             ReLU-95           [-1, 64, 16, 16]               0\n",
            "        Inception-96          [-1, 512, 16, 16]               0\n",
            "           Conv2d-97          [-1, 128, 16, 16]          65,664\n",
            "      BatchNorm2d-98          [-1, 128, 16, 16]             256\n",
            "             ReLU-99          [-1, 128, 16, 16]               0\n",
            "          Conv2d-100          [-1, 128, 16, 16]          65,664\n",
            "     BatchNorm2d-101          [-1, 128, 16, 16]             256\n",
            "            ReLU-102          [-1, 128, 16, 16]               0\n",
            "          Conv2d-103          [-1, 256, 16, 16]         295,168\n",
            "     BatchNorm2d-104          [-1, 256, 16, 16]             512\n",
            "            ReLU-105          [-1, 256, 16, 16]               0\n",
            "          Conv2d-106           [-1, 24, 16, 16]          12,312\n",
            "     BatchNorm2d-107           [-1, 24, 16, 16]              48\n",
            "            ReLU-108           [-1, 24, 16, 16]               0\n",
            "          Conv2d-109           [-1, 64, 16, 16]          13,888\n",
            "     BatchNorm2d-110           [-1, 64, 16, 16]             128\n",
            "            ReLU-111           [-1, 64, 16, 16]               0\n",
            "          Conv2d-112           [-1, 64, 16, 16]          36,928\n",
            "     BatchNorm2d-113           [-1, 64, 16, 16]             128\n",
            "            ReLU-114           [-1, 64, 16, 16]               0\n",
            "       MaxPool2d-115          [-1, 512, 16, 16]               0\n",
            "          Conv2d-116           [-1, 64, 16, 16]          32,832\n",
            "     BatchNorm2d-117           [-1, 64, 16, 16]             128\n",
            "            ReLU-118           [-1, 64, 16, 16]               0\n",
            "       Inception-119          [-1, 512, 16, 16]               0\n",
            "          Conv2d-120          [-1, 112, 16, 16]          57,456\n",
            "     BatchNorm2d-121          [-1, 112, 16, 16]             224\n",
            "            ReLU-122          [-1, 112, 16, 16]               0\n",
            "          Conv2d-123          [-1, 144, 16, 16]          73,872\n",
            "     BatchNorm2d-124          [-1, 144, 16, 16]             288\n",
            "            ReLU-125          [-1, 144, 16, 16]               0\n",
            "          Conv2d-126          [-1, 288, 16, 16]         373,536\n",
            "     BatchNorm2d-127          [-1, 288, 16, 16]             576\n",
            "            ReLU-128          [-1, 288, 16, 16]               0\n",
            "          Conv2d-129           [-1, 32, 16, 16]          16,416\n",
            "     BatchNorm2d-130           [-1, 32, 16, 16]              64\n",
            "            ReLU-131           [-1, 32, 16, 16]               0\n",
            "          Conv2d-132           [-1, 64, 16, 16]          18,496\n",
            "     BatchNorm2d-133           [-1, 64, 16, 16]             128\n",
            "            ReLU-134           [-1, 64, 16, 16]               0\n",
            "          Conv2d-135           [-1, 64, 16, 16]          36,928\n",
            "     BatchNorm2d-136           [-1, 64, 16, 16]             128\n",
            "            ReLU-137           [-1, 64, 16, 16]               0\n",
            "       MaxPool2d-138          [-1, 512, 16, 16]               0\n",
            "          Conv2d-139           [-1, 64, 16, 16]          32,832\n",
            "     BatchNorm2d-140           [-1, 64, 16, 16]             128\n",
            "            ReLU-141           [-1, 64, 16, 16]               0\n",
            "       Inception-142          [-1, 528, 16, 16]               0\n",
            "          Conv2d-143          [-1, 256, 16, 16]         135,424\n",
            "     BatchNorm2d-144          [-1, 256, 16, 16]             512\n",
            "            ReLU-145          [-1, 256, 16, 16]               0\n",
            "          Conv2d-146          [-1, 160, 16, 16]          84,640\n",
            "     BatchNorm2d-147          [-1, 160, 16, 16]             320\n",
            "            ReLU-148          [-1, 160, 16, 16]               0\n",
            "          Conv2d-149          [-1, 320, 16, 16]         461,120\n",
            "     BatchNorm2d-150          [-1, 320, 16, 16]             640\n",
            "            ReLU-151          [-1, 320, 16, 16]               0\n",
            "          Conv2d-152           [-1, 32, 16, 16]          16,928\n",
            "     BatchNorm2d-153           [-1, 32, 16, 16]              64\n",
            "            ReLU-154           [-1, 32, 16, 16]               0\n",
            "          Conv2d-155          [-1, 128, 16, 16]          36,992\n",
            "     BatchNorm2d-156          [-1, 128, 16, 16]             256\n",
            "            ReLU-157          [-1, 128, 16, 16]               0\n",
            "          Conv2d-158          [-1, 128, 16, 16]         147,584\n",
            "     BatchNorm2d-159          [-1, 128, 16, 16]             256\n",
            "            ReLU-160          [-1, 128, 16, 16]               0\n",
            "       MaxPool2d-161          [-1, 528, 16, 16]               0\n",
            "          Conv2d-162          [-1, 128, 16, 16]          67,712\n",
            "     BatchNorm2d-163          [-1, 128, 16, 16]             256\n",
            "            ReLU-164          [-1, 128, 16, 16]               0\n",
            "       Inception-165          [-1, 832, 16, 16]               0\n",
            "       MaxPool2d-166            [-1, 832, 8, 8]               0\n",
            "          Conv2d-167            [-1, 256, 8, 8]         213,248\n",
            "     BatchNorm2d-168            [-1, 256, 8, 8]             512\n",
            "            ReLU-169            [-1, 256, 8, 8]               0\n",
            "          Conv2d-170            [-1, 160, 8, 8]         133,280\n",
            "     BatchNorm2d-171            [-1, 160, 8, 8]             320\n",
            "            ReLU-172            [-1, 160, 8, 8]               0\n",
            "          Conv2d-173            [-1, 320, 8, 8]         461,120\n",
            "     BatchNorm2d-174            [-1, 320, 8, 8]             640\n",
            "            ReLU-175            [-1, 320, 8, 8]               0\n",
            "          Conv2d-176             [-1, 32, 8, 8]          26,656\n",
            "     BatchNorm2d-177             [-1, 32, 8, 8]              64\n",
            "            ReLU-178             [-1, 32, 8, 8]               0\n",
            "          Conv2d-179            [-1, 128, 8, 8]          36,992\n",
            "     BatchNorm2d-180            [-1, 128, 8, 8]             256\n",
            "            ReLU-181            [-1, 128, 8, 8]               0\n",
            "          Conv2d-182            [-1, 128, 8, 8]         147,584\n",
            "     BatchNorm2d-183            [-1, 128, 8, 8]             256\n",
            "            ReLU-184            [-1, 128, 8, 8]               0\n",
            "       MaxPool2d-185            [-1, 832, 8, 8]               0\n",
            "          Conv2d-186            [-1, 128, 8, 8]         106,624\n",
            "     BatchNorm2d-187            [-1, 128, 8, 8]             256\n",
            "            ReLU-188            [-1, 128, 8, 8]               0\n",
            "       Inception-189            [-1, 832, 8, 8]               0\n",
            "          Conv2d-190            [-1, 384, 8, 8]         319,872\n",
            "     BatchNorm2d-191            [-1, 384, 8, 8]             768\n",
            "            ReLU-192            [-1, 384, 8, 8]               0\n",
            "          Conv2d-193            [-1, 192, 8, 8]         159,936\n",
            "     BatchNorm2d-194            [-1, 192, 8, 8]             384\n",
            "            ReLU-195            [-1, 192, 8, 8]               0\n",
            "          Conv2d-196            [-1, 384, 8, 8]         663,936\n",
            "     BatchNorm2d-197            [-1, 384, 8, 8]             768\n",
            "            ReLU-198            [-1, 384, 8, 8]               0\n",
            "          Conv2d-199             [-1, 48, 8, 8]          39,984\n",
            "     BatchNorm2d-200             [-1, 48, 8, 8]              96\n",
            "            ReLU-201             [-1, 48, 8, 8]               0\n",
            "          Conv2d-202            [-1, 128, 8, 8]          55,424\n",
            "     BatchNorm2d-203            [-1, 128, 8, 8]             256\n",
            "            ReLU-204            [-1, 128, 8, 8]               0\n",
            "          Conv2d-205            [-1, 128, 8, 8]         147,584\n",
            "     BatchNorm2d-206            [-1, 128, 8, 8]             256\n",
            "            ReLU-207            [-1, 128, 8, 8]               0\n",
            "       MaxPool2d-208            [-1, 832, 8, 8]               0\n",
            "          Conv2d-209            [-1, 128, 8, 8]         106,624\n",
            "     BatchNorm2d-210            [-1, 128, 8, 8]             256\n",
            "            ReLU-211            [-1, 128, 8, 8]               0\n",
            "       Inception-212           [-1, 1024, 8, 8]               0\n",
            "       AvgPool2d-213           [-1, 1024, 1, 1]               0\n",
            "          Linear-214                   [-1, 10]          10,250\n",
            "================================================================\n",
            "Total params: 6,166,250\n",
            "Trainable params: 6,166,250\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 81.42\n",
            "Params size (MB): 23.52\n",
            "Estimated Total Size (MB): 104.96\n",
            "----------------------------------------------------------------\n",
            "time: 269 ms (started: 2021-05-16 15:24:56 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# GoogleNet colour\n",
        "model = GoogLeNet(channels=3)\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "model = model.to(device)\n",
        "summary(model,(3, 32, 32))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ad6f30attVrJ",
        "outputId": "4e8748a8-0e88-49c5-9738-5075febfca3f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 16, 32, 32]             144\n",
            "       BatchNorm2d-2           [-1, 16, 32, 32]              32\n",
            "              ReLU-3           [-1, 16, 32, 32]               0\n",
            "            Conv2d-4           [-1, 16, 32, 32]           2,304\n",
            "       BatchNorm2d-5           [-1, 16, 32, 32]              32\n",
            "              ReLU-6           [-1, 16, 32, 32]               0\n",
            "            Conv2d-7           [-1, 16, 32, 32]           2,304\n",
            "        BasicBlock-8           [-1, 16, 32, 32]               0\n",
            "       BatchNorm2d-9           [-1, 16, 32, 32]              32\n",
            "             ReLU-10           [-1, 16, 32, 32]               0\n",
            "           Conv2d-11           [-1, 16, 32, 32]           2,304\n",
            "      BatchNorm2d-12           [-1, 16, 32, 32]              32\n",
            "             ReLU-13           [-1, 16, 32, 32]               0\n",
            "           Conv2d-14           [-1, 16, 32, 32]           2,304\n",
            "       BasicBlock-15           [-1, 16, 32, 32]               0\n",
            "      BatchNorm2d-16           [-1, 16, 32, 32]              32\n",
            "             ReLU-17           [-1, 16, 32, 32]               0\n",
            "           Conv2d-18           [-1, 16, 32, 32]           2,304\n",
            "      BatchNorm2d-19           [-1, 16, 32, 32]              32\n",
            "             ReLU-20           [-1, 16, 32, 32]               0\n",
            "           Conv2d-21           [-1, 16, 32, 32]           2,304\n",
            "       BasicBlock-22           [-1, 16, 32, 32]               0\n",
            "      BatchNorm2d-23           [-1, 16, 32, 32]              32\n",
            "             ReLU-24           [-1, 16, 32, 32]               0\n",
            "           Conv2d-25           [-1, 16, 32, 32]           2,304\n",
            "      BatchNorm2d-26           [-1, 16, 32, 32]              32\n",
            "             ReLU-27           [-1, 16, 32, 32]               0\n",
            "           Conv2d-28           [-1, 16, 32, 32]           2,304\n",
            "       BasicBlock-29           [-1, 16, 32, 32]               0\n",
            "     NetworkBlock-30           [-1, 16, 32, 32]               0\n",
            "      BatchNorm2d-31           [-1, 16, 32, 32]              32\n",
            "             ReLU-32           [-1, 16, 32, 32]               0\n",
            "           Conv2d-33           [-1, 32, 16, 16]           4,608\n",
            "      BatchNorm2d-34           [-1, 32, 16, 16]              64\n",
            "             ReLU-35           [-1, 32, 16, 16]               0\n",
            "           Conv2d-36           [-1, 32, 16, 16]           9,216\n",
            "           Conv2d-37           [-1, 32, 16, 16]             512\n",
            "       BasicBlock-38           [-1, 32, 16, 16]               0\n",
            "      BatchNorm2d-39           [-1, 32, 16, 16]              64\n",
            "             ReLU-40           [-1, 32, 16, 16]               0\n",
            "           Conv2d-41           [-1, 32, 16, 16]           9,216\n",
            "      BatchNorm2d-42           [-1, 32, 16, 16]              64\n",
            "             ReLU-43           [-1, 32, 16, 16]               0\n",
            "           Conv2d-44           [-1, 32, 16, 16]           9,216\n",
            "       BasicBlock-45           [-1, 32, 16, 16]               0\n",
            "      BatchNorm2d-46           [-1, 32, 16, 16]              64\n",
            "             ReLU-47           [-1, 32, 16, 16]               0\n",
            "           Conv2d-48           [-1, 32, 16, 16]           9,216\n",
            "      BatchNorm2d-49           [-1, 32, 16, 16]              64\n",
            "             ReLU-50           [-1, 32, 16, 16]               0\n",
            "           Conv2d-51           [-1, 32, 16, 16]           9,216\n",
            "       BasicBlock-52           [-1, 32, 16, 16]               0\n",
            "      BatchNorm2d-53           [-1, 32, 16, 16]              64\n",
            "             ReLU-54           [-1, 32, 16, 16]               0\n",
            "           Conv2d-55           [-1, 32, 16, 16]           9,216\n",
            "      BatchNorm2d-56           [-1, 32, 16, 16]              64\n",
            "             ReLU-57           [-1, 32, 16, 16]               0\n",
            "           Conv2d-58           [-1, 32, 16, 16]           9,216\n",
            "       BasicBlock-59           [-1, 32, 16, 16]               0\n",
            "     NetworkBlock-60           [-1, 32, 16, 16]               0\n",
            "      BatchNorm2d-61           [-1, 32, 16, 16]              64\n",
            "             ReLU-62           [-1, 32, 16, 16]               0\n",
            "           Conv2d-63             [-1, 64, 8, 8]          18,432\n",
            "      BatchNorm2d-64             [-1, 64, 8, 8]             128\n",
            "             ReLU-65             [-1, 64, 8, 8]               0\n",
            "           Conv2d-66             [-1, 64, 8, 8]          36,864\n",
            "           Conv2d-67             [-1, 64, 8, 8]           2,048\n",
            "       BasicBlock-68             [-1, 64, 8, 8]               0\n",
            "      BatchNorm2d-69             [-1, 64, 8, 8]             128\n",
            "             ReLU-70             [-1, 64, 8, 8]               0\n",
            "           Conv2d-71             [-1, 64, 8, 8]          36,864\n",
            "      BatchNorm2d-72             [-1, 64, 8, 8]             128\n",
            "             ReLU-73             [-1, 64, 8, 8]               0\n",
            "           Conv2d-74             [-1, 64, 8, 8]          36,864\n",
            "       BasicBlock-75             [-1, 64, 8, 8]               0\n",
            "      BatchNorm2d-76             [-1, 64, 8, 8]             128\n",
            "             ReLU-77             [-1, 64, 8, 8]               0\n",
            "           Conv2d-78             [-1, 64, 8, 8]          36,864\n",
            "      BatchNorm2d-79             [-1, 64, 8, 8]             128\n",
            "             ReLU-80             [-1, 64, 8, 8]               0\n",
            "           Conv2d-81             [-1, 64, 8, 8]          36,864\n",
            "       BasicBlock-82             [-1, 64, 8, 8]               0\n",
            "      BatchNorm2d-83             [-1, 64, 8, 8]             128\n",
            "             ReLU-84             [-1, 64, 8, 8]               0\n",
            "           Conv2d-85             [-1, 64, 8, 8]          36,864\n",
            "      BatchNorm2d-86             [-1, 64, 8, 8]             128\n",
            "             ReLU-87             [-1, 64, 8, 8]               0\n",
            "           Conv2d-88             [-1, 64, 8, 8]          36,864\n",
            "       BasicBlock-89             [-1, 64, 8, 8]               0\n",
            "     NetworkBlock-90             [-1, 64, 8, 8]               0\n",
            "      BatchNorm2d-91             [-1, 64, 8, 8]             128\n",
            "             ReLU-92             [-1, 64, 8, 8]               0\n",
            "           Linear-93                   [-1, 10]             650\n",
            "================================================================\n",
            "Total params: 369,210\n",
            "Trainable params: 369,210\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 6.81\n",
            "Params size (MB): 1.41\n",
            "Estimated Total Size (MB): 8.22\n",
            "----------------------------------------------------------------\n",
            "time: 90.1 ms (started: 2021-05-16 15:25:51 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# WideResNet grey\n",
        "model = WideResNet(channels=1, depth=28, num_classes=10)\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "model = model.to(device)\n",
        "summary(model,(1, 32, 32))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J6PGFMh6tV0B",
        "outputId": "a0e9bbe4-e95b-41de-c5b8-8685061e4fc7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 16, 32, 32]             432\n",
            "       BatchNorm2d-2           [-1, 16, 32, 32]              32\n",
            "              ReLU-3           [-1, 16, 32, 32]               0\n",
            "            Conv2d-4           [-1, 16, 32, 32]           2,304\n",
            "       BatchNorm2d-5           [-1, 16, 32, 32]              32\n",
            "              ReLU-6           [-1, 16, 32, 32]               0\n",
            "            Conv2d-7           [-1, 16, 32, 32]           2,304\n",
            "        BasicBlock-8           [-1, 16, 32, 32]               0\n",
            "       BatchNorm2d-9           [-1, 16, 32, 32]              32\n",
            "             ReLU-10           [-1, 16, 32, 32]               0\n",
            "           Conv2d-11           [-1, 16, 32, 32]           2,304\n",
            "      BatchNorm2d-12           [-1, 16, 32, 32]              32\n",
            "             ReLU-13           [-1, 16, 32, 32]               0\n",
            "           Conv2d-14           [-1, 16, 32, 32]           2,304\n",
            "       BasicBlock-15           [-1, 16, 32, 32]               0\n",
            "      BatchNorm2d-16           [-1, 16, 32, 32]              32\n",
            "             ReLU-17           [-1, 16, 32, 32]               0\n",
            "           Conv2d-18           [-1, 16, 32, 32]           2,304\n",
            "      BatchNorm2d-19           [-1, 16, 32, 32]              32\n",
            "             ReLU-20           [-1, 16, 32, 32]               0\n",
            "           Conv2d-21           [-1, 16, 32, 32]           2,304\n",
            "       BasicBlock-22           [-1, 16, 32, 32]               0\n",
            "      BatchNorm2d-23           [-1, 16, 32, 32]              32\n",
            "             ReLU-24           [-1, 16, 32, 32]               0\n",
            "           Conv2d-25           [-1, 16, 32, 32]           2,304\n",
            "      BatchNorm2d-26           [-1, 16, 32, 32]              32\n",
            "             ReLU-27           [-1, 16, 32, 32]               0\n",
            "           Conv2d-28           [-1, 16, 32, 32]           2,304\n",
            "       BasicBlock-29           [-1, 16, 32, 32]               0\n",
            "     NetworkBlock-30           [-1, 16, 32, 32]               0\n",
            "      BatchNorm2d-31           [-1, 16, 32, 32]              32\n",
            "             ReLU-32           [-1, 16, 32, 32]               0\n",
            "           Conv2d-33           [-1, 32, 16, 16]           4,608\n",
            "      BatchNorm2d-34           [-1, 32, 16, 16]              64\n",
            "             ReLU-35           [-1, 32, 16, 16]               0\n",
            "           Conv2d-36           [-1, 32, 16, 16]           9,216\n",
            "           Conv2d-37           [-1, 32, 16, 16]             512\n",
            "       BasicBlock-38           [-1, 32, 16, 16]               0\n",
            "      BatchNorm2d-39           [-1, 32, 16, 16]              64\n",
            "             ReLU-40           [-1, 32, 16, 16]               0\n",
            "           Conv2d-41           [-1, 32, 16, 16]           9,216\n",
            "      BatchNorm2d-42           [-1, 32, 16, 16]              64\n",
            "             ReLU-43           [-1, 32, 16, 16]               0\n",
            "           Conv2d-44           [-1, 32, 16, 16]           9,216\n",
            "       BasicBlock-45           [-1, 32, 16, 16]               0\n",
            "      BatchNorm2d-46           [-1, 32, 16, 16]              64\n",
            "             ReLU-47           [-1, 32, 16, 16]               0\n",
            "           Conv2d-48           [-1, 32, 16, 16]           9,216\n",
            "      BatchNorm2d-49           [-1, 32, 16, 16]              64\n",
            "             ReLU-50           [-1, 32, 16, 16]               0\n",
            "           Conv2d-51           [-1, 32, 16, 16]           9,216\n",
            "       BasicBlock-52           [-1, 32, 16, 16]               0\n",
            "      BatchNorm2d-53           [-1, 32, 16, 16]              64\n",
            "             ReLU-54           [-1, 32, 16, 16]               0\n",
            "           Conv2d-55           [-1, 32, 16, 16]           9,216\n",
            "      BatchNorm2d-56           [-1, 32, 16, 16]              64\n",
            "             ReLU-57           [-1, 32, 16, 16]               0\n",
            "           Conv2d-58           [-1, 32, 16, 16]           9,216\n",
            "       BasicBlock-59           [-1, 32, 16, 16]               0\n",
            "     NetworkBlock-60           [-1, 32, 16, 16]               0\n",
            "      BatchNorm2d-61           [-1, 32, 16, 16]              64\n",
            "             ReLU-62           [-1, 32, 16, 16]               0\n",
            "           Conv2d-63             [-1, 64, 8, 8]          18,432\n",
            "      BatchNorm2d-64             [-1, 64, 8, 8]             128\n",
            "             ReLU-65             [-1, 64, 8, 8]               0\n",
            "           Conv2d-66             [-1, 64, 8, 8]          36,864\n",
            "           Conv2d-67             [-1, 64, 8, 8]           2,048\n",
            "       BasicBlock-68             [-1, 64, 8, 8]               0\n",
            "      BatchNorm2d-69             [-1, 64, 8, 8]             128\n",
            "             ReLU-70             [-1, 64, 8, 8]               0\n",
            "           Conv2d-71             [-1, 64, 8, 8]          36,864\n",
            "      BatchNorm2d-72             [-1, 64, 8, 8]             128\n",
            "             ReLU-73             [-1, 64, 8, 8]               0\n",
            "           Conv2d-74             [-1, 64, 8, 8]          36,864\n",
            "       BasicBlock-75             [-1, 64, 8, 8]               0\n",
            "      BatchNorm2d-76             [-1, 64, 8, 8]             128\n",
            "             ReLU-77             [-1, 64, 8, 8]               0\n",
            "           Conv2d-78             [-1, 64, 8, 8]          36,864\n",
            "      BatchNorm2d-79             [-1, 64, 8, 8]             128\n",
            "             ReLU-80             [-1, 64, 8, 8]               0\n",
            "           Conv2d-81             [-1, 64, 8, 8]          36,864\n",
            "       BasicBlock-82             [-1, 64, 8, 8]               0\n",
            "      BatchNorm2d-83             [-1, 64, 8, 8]             128\n",
            "             ReLU-84             [-1, 64, 8, 8]               0\n",
            "           Conv2d-85             [-1, 64, 8, 8]          36,864\n",
            "      BatchNorm2d-86             [-1, 64, 8, 8]             128\n",
            "             ReLU-87             [-1, 64, 8, 8]               0\n",
            "           Conv2d-88             [-1, 64, 8, 8]          36,864\n",
            "       BasicBlock-89             [-1, 64, 8, 8]               0\n",
            "     NetworkBlock-90             [-1, 64, 8, 8]               0\n",
            "      BatchNorm2d-91             [-1, 64, 8, 8]             128\n",
            "             ReLU-92             [-1, 64, 8, 8]               0\n",
            "           Linear-93                   [-1, 10]             650\n",
            "================================================================\n",
            "Total params: 369,498\n",
            "Trainable params: 369,498\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 6.81\n",
            "Params size (MB): 1.41\n",
            "Estimated Total Size (MB): 8.23\n",
            "----------------------------------------------------------------\n",
            "time: 90.2 ms (started: 2021-05-16 15:26:16 +00:00)\n"
          ]
        }
      ],
      "source": [
        "# WideResNet colour\n",
        "model = WideResNet(channels=3, depth=28, num_classes=10)\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "model = model.to(device)\n",
        "summary(model,(3, 32, 32))"
      ]
    }
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