Final_Project (1).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9pPoIQgApH9",
        "outputId": "891754e1-d207-4aba-e118-cf637a1a14c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "id": "F9pPoIQgApH9"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "Em6ckbzICRGp",
        "outputId": "62227f74-e059-4a8e-fc51-e2ec8c6f66d8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-31487463-292d-4b98-9621-72ae0c408722\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-31487463-292d-4b98-9621-72ae0c408722\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle (1).json to kaggle (1).json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle (1).json': b'{\"username\":\"gauravsingh37\",\"key\":\"aa555d8d832f7e239a366aae66d77d27\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.upload() #this will prompt you to upload the kaggle.json"
      ],
      "id": "Em6ckbzICRGp"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2Yl6a7vCWxG",
        "outputId": "7e5d88be-49eb-4065-f617-2c3c277f6a9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-rw-r--r-- 1 root root 69 Aug 20 00:17 kaggle.json\n",
            "/content\n",
            "ref                                                             title                                           size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
            "--------------------------------------------------------------  ---------------------------------------------  -----  -------------------  -------------  ---------  ---------------  \n",
            "ruchi798/data-science-job-salaries                              Data Science Job Salaries                        7KB  2022-06-15 08:59:12          21634        670  1.0              \n",
            "josephcheng123456/olympic-historical-dataset-from-olympediaorg  Olympic Historical Dataset From Olympedia.org   25MB  2022-08-18 14:18:14            710         27  1.0              \n",
            "deepcontractor/mcdonalds-india-menu-nutrition-facts             McDonald's India : Menu Nutrition Dataset        5KB  2022-07-28 17:08:52           1850         97  0.9411765        \n",
            "gpreda/life-expectancy-in-europe-by-age-and-sex                 Life Expectancy in Europe by Age and Sex         4MB  2022-08-12 15:28:01            593         27  1.0              \n",
            "arianazmoudeh/airbnbopendata                                    Airbnb Open Data                                10MB  2022-08-01 15:58:10           1331         43  0.9705882        \n",
            "nadzmiagthomas/australia-data-science-jobs                      Australia Data Science Jobs                      2MB  2022-08-09 11:48:07            356         25  0.9705882        \n",
            "infamouscoder/dataset-netflix-shows                             Dataset: NetFlix Shows                           1MB  2022-08-11 05:05:41            612         38  1.0              \n",
            "himanshunakrani/student-study-hours                             Student Study Hours                             276B  2022-07-20 13:17:29           2291         76  1.0              \n",
            "nancyalaswad90/diamonds-prices                                  Diamonds Prices                                711KB  2022-07-09 14:59:21           2643        109  1.0              \n",
            "zzettrkalpakbal/full-filled-brain-stroke-dataset                Brain stroke prediction dataset                 52KB  2022-07-16 09:57:08           2644         76  0.9705882        \n",
            "reihanenamdari/breast-cancer                                    Breast Cancer                                   43KB  2022-08-08 19:25:55            506         32  1.0              \n",
            "dansbecker/melbourne-housing-snapshot                           Melbourne Housing Snapshot                     451KB  2018-06-05 12:52:24          93370       1125  0.7058824        \n",
            "jpmiller/employee-attrition-for-healthcare                      Employee Attrition for Healthcare               59KB  2022-08-10 22:34:29            590         38  0.9705882        \n",
            "ramjasmaurya/commonwealth-games-2022                            Commonwealth Games 2022                         80KB  2022-08-09 03:00:59            386         28  1.0              \n",
            "jillanisofttech/brain-stroke-dataset                            Brain Stroke Dataset                            47KB  2022-08-04 18:02:56            998         34  0.9705882        \n",
            "faryarmemon/usa-housing-market-factors                          U.S. Housing Market Factors                     32KB  2022-08-03 02:19:31            602         39  1.0              \n",
            "surajjha101/countries-olympics-medals-since-1896                Countries Olympics Medals since 1896             4KB  2022-07-27 06:39:06           1028         54  1.0              \n",
            "datasnaek/youtube-new                                           Trending YouTube Video Statistics              201MB  2019-06-03 00:56:47         181770       4639  0.7941176        \n",
            "zynicide/wine-reviews                                           Wine Reviews                                    51MB  2017-11-27 17:08:04         164712       3355  0.7941176        \n",
            "erqizhou/students-data-analysis                                 Students Data Analysis                           2KB  2022-07-20 03:54:13           1228         39  1.0              \n"
          ]
        }
      ],
      "source": [
        "!ls -lha kaggle.json\n",
        "!pip install -q kaggle\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 /root/.kaggle/kaggle.json\n",
        "!pwd\n",
        "!kaggle datasets list"
      ],
      "id": "X2Yl6a7vCWxG"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUOk-JbTCW4P",
        "outputId": "8401adb6-89db-414d-c611-08de54f2e30a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading facial-expression-dataset-image-folders-fer2013.zip to /content\n",
            " 66% 43.0M/65.1M [00:00<00:00, 86.1MB/s]\n",
            "100% 65.1M/65.1M [00:00<00:00, 115MB/s] \n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d astraszab/facial-expression-dataset-image-folders-fer2013"
      ],
      "id": "DUOk-JbTCW4P"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_tiH6quKu4-",
        "outputId": "2171a2bd-e507-4bef-b5f5-1a5a602f1263"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: data/train/6/28451.png  \n",
            "  inflating: data/train/6/28452.png  \n",
            "  inflating: data/train/6/28467.png  \n",
            "  inflating: data/train/6/28471.png  \n",
            "  inflating: data/train/6/28472.png  \n",
            "  inflating: data/train/6/28478.png  \n",
            "  inflating: data/train/6/2848.png   \n",
            "  inflating: data/train/6/28480.png  \n",
            "  inflating: data/train/6/28484.png  \n",
            "  inflating: data/train/6/28485.png  \n",
            "  inflating: data/train/6/28493.png  \n",
            "  inflating: data/train/6/28499.png  \n",
            "  inflating: data/train/6/28507.png  \n",
            "  inflating: data/train/6/28517.png  \n",
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          ]
        }
      ],
      "source": [
        "!unzip facial-expression-dataset-image-folders-fer2013.zip"
      ],
      "id": "Z_tiH6quKu4-"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "54349ec1"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import itertools # To join a list of lists\n",
        "import matplotlib.pyplot as plt # For general plotting\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import DataLoader, Subset\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "\n",
        "# Utility to visualize PyTorch network and shapes\n",
        "from torchsummary import summary\n",
        "np.set_printoptions(suppress=True)\n",
        "\n",
        "# Set seed to generate reproducible \"pseudo-randomness\" (handles scipy's \"randomness\" too)\n",
        "# random_seed = 7\n",
        "# np.random.seed(random_seed)\n",
        "# torch.manual_seed(7)\n",
        "\n",
        "plt.rc('font', size=22)          # controls default text sizes\n",
        "plt.rc('axes', titlesize=18)     # fontsize of the axes title\n",
        "plt.rc('axes', labelsize=18)     # fontsize of the x and y labels\n",
        "plt.rc('xtick', labelsize=14)    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize=14)    # fontsize of the tick labels\n",
        "plt.rc('legend', fontsize=16)    # legend fontsize\n",
        "plt.rc('figure', titlesize=22)   # fontsize of the figure title"
      ],
      "id": "54349ec1"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "6ec1401b",
        "outputId": "3bcdd3f4-53cf-4297-b822-c18ff7bd65dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset has N = 28709 samples\n",
            "Test dataset has N = 3589 samples\n",
            "Validation dataset has N = 3589 samples\n",
            "\n",
            "Labels corresponding to randomly drawn images from the training set:\n",
            "happy\t fear \t neutral\t sad  \t fear \t angry\t neutral\t angry\t\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Define how transform the data for processing in batches\n",
        "transform = transforms.Compose([\n",
        "        transforms.Resize([280,280]), # Resizing the image\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize(mean=(0.5,0.5,0.5), std=(0.5,0.5,0.5)) # 0.5 due to how torchvision data is in range [0,1]\n",
        "    ])\n",
        "# Specify the batch size for SGD\n",
        "batch_size = 64\n",
        "\n",
        "# Use the ImageFolder class, which is a generic dataloder useful when images stored in labelled directories\n",
        "# Apply transform to images specified above, importantly converting \"ToTensor\"\n",
        "train_dataset = torchvision.datasets.ImageFolder(root='data/train', transform=transform)\n",
        "test_dataset = torchvision.datasets.ImageFolder(root='data/test', transform=transform)\n",
        "valid_dataset = torchvision.datasets.ImageFolder(root='data/val', transform=transform)\n",
        "\n",
        "classes = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')\n",
        "print(\"Train dataset has N = {} samples\".format(len(train_dataset)))\n",
        "print(\"Test dataset has N = {} samples\".format(len(test_dataset)))\n",
        "print(\"Validation dataset has N = {} samples\".format(len(valid_dataset)))\n",
        "\n",
        "# Define iterable for our newly created dataset and shuffle samples \n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)\n",
        "\n",
        "### Plotting stuff for visualization ###\n",
        "fig = plt.figure(figsize=(20,20))\n",
        "# Utility function to show an image\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    img_np = img.cpu().numpy()\n",
        "    plt.imshow(np.transpose(img_np, (1, 2, 0)))\n",
        "\n",
        "\n",
        "# Get some random training dataset images\n",
        "dataiter = iter(train_dataloader)\n",
        "# Extract a batch\n",
        "images, labels = dataiter.next()\n",
        "images, labels = images.to(device), labels.to(device)\n",
        "\n",
        "# Show 8 images for display\n",
        "num_display = 8\n",
        "imshow(torchvision.utils.make_grid(images[:num_display]))\n",
        "# Print labels as a concatenated string\n",
        "print(\"\\nLabels corresponding to randomly drawn images from the training set:\")\n",
        "print(\" \".join(f'{classes[labels[j]]:5s}\\t' for j in range(num_display)))"
      ],
      "id": "6ec1401b"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56a28e44",
        "outputId": "f185dad2-f7c0-4ed4-9200-dd6d1afc4341"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1         [-1, 16, 280, 280]             448\n",
            "         MaxPool2d-2         [-1, 16, 140, 140]               0\n",
            "            Conv2d-3         [-1, 32, 140, 140]           4,640\n",
            "       BatchNorm2d-4         [-1, 32, 140, 140]              64\n",
            "         MaxPool2d-5           [-1, 32, 70, 70]               0\n",
            "           Dropout-6           [-1, 32, 70, 70]               0\n",
            "            Conv2d-7           [-1, 64, 70, 70]          18,496\n",
            "       BatchNorm2d-8           [-1, 64, 70, 70]             128\n",
            "         MaxPool2d-9           [-1, 64, 35, 35]               0\n",
            "          Dropout-10           [-1, 64, 35, 35]               0\n",
            "           Conv2d-11          [-1, 128, 35, 35]          73,856\n",
            "      BatchNorm2d-12          [-1, 128, 35, 35]             256\n",
            "        MaxPool2d-13          [-1, 128, 17, 17]               0\n",
            "          Dropout-14          [-1, 128, 17, 17]               0\n",
            "           Conv2d-15          [-1, 256, 17, 17]         295,168\n",
            "      BatchNorm2d-16          [-1, 256, 17, 17]             512\n",
            "        MaxPool2d-17            [-1, 256, 8, 8]               0\n",
            "          Dropout-18            [-1, 256, 8, 8]               0\n",
            "           Conv2d-19            [-1, 512, 8, 8]       1,180,160\n",
            "      BatchNorm2d-20            [-1, 512, 8, 8]           1,024\n",
            "        MaxPool2d-21            [-1, 512, 4, 4]               0\n",
            "          Dropout-22            [-1, 512, 4, 4]               0\n",
            "           Linear-23                   [-1, 16]         131,088\n",
            "          Dropout-24                   [-1, 16]               0\n",
            "      BatchNorm1d-25                   [-1, 16]              32\n",
            "           Linear-26                   [-1, 16]             272\n",
            "          Dropout-27                   [-1, 16]               0\n",
            "      BatchNorm1d-28                   [-1, 16]              32\n",
            "           Linear-29                    [-1, 7]             119\n",
            "================================================================\n",
            "Total params: 1,706,295\n",
            "Trainable params: 1,706,295\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.90\n",
            "Forward/backward pass size (MB): 34.87\n",
            "Params size (MB): 6.51\n",
            "Estimated Total Size (MB): 42.28\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "# Note that PyTorch uses NCHW (samples, channels, height, width) image format convention\n",
        "class ConvNet(nn.Module):\n",
        "    def __init__(self, in_channels, num_filters, out_classes, kernel_size=3):\n",
        "        super().__init__()\n",
        "        # Conv2D layer with 'same' padding so image retains shape\n",
        "        self.conv1 = nn.Conv2d(in_channels, num_filters, kernel_size, padding='same')\n",
        "        # self.conv2_bn = nn.BatchNorm2d(num_filters)\n",
        "        self.drop = nn.Dropout(p=0.2)\n",
        "        self.drop2 =nn.Dropout(p=0.2)\n",
        "        #self.dropout = \n",
        "        # MaxPooling layer with 2x2 kernel size and stride 2\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(num_filters, num_filters*2, kernel_size, padding='same')\n",
        "        self.conv2_bn = nn.BatchNorm2d(num_filters*2)\n",
        "        self.conv3 = nn.Conv2d(num_filters*2, num_filters*4, kernel_size, padding='same')\n",
        "        self.conv3_bn = nn.BatchNorm2d(num_filters*4)\n",
        "        self.conv4 = nn.Conv2d(num_filters*4, num_filters*8, kernel_size, padding='same')\n",
        "        self.conv4_bn = nn.BatchNorm2d(num_filters*8)\n",
        "        self.conv5 = nn.Conv2d(num_filters*8, num_filters*16, kernel_size, padding='same')\n",
        "        self.conv5_bn = nn.BatchNorm2d(num_filters*16)\n",
        "        self.conv6 = nn.Conv2d(num_filters*16, num_filters*32, kernel_size, padding='same')\n",
        "        self.conv6_bn = nn.BatchNorm2d(num_filters*32)\n",
        "        self.fc1 = nn.Linear(num_filters*32*4*4, num_filters)\n",
        "        self.fc1_norm= nn.BatchNorm1d(num_filters)\n",
        "        self.fc2 = nn.Linear(num_filters, num_filters)\n",
        "        self.fc2_norm= nn.BatchNorm1d(num_filters)\n",
        "        self.fc3 = nn.Linear(num_filters, out_classes)\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Non-linear ReLU activations between convolutional layers\n",
        "        # Conv->ReLU->Pooling\n",
        "        # 280x280 image -> 140x140 after pooling\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        # print(x.shape)\n",
        "        # 140x140 feature map -> 70x70 after pooling\n",
        "        x = self.drop(self.pool(self.conv2_bn(F.relu(self.conv2(x)))))\n",
        "        # print(x.shape)\n",
        "        # Flatten all dimensions except batch (start_dim=1)\n",
        "        x = self.drop(self.pool(self.conv3_bn(F.relu(self.conv3(x)))))\n",
        "        # print(x.shape)\n",
        "        x = self.drop(self.pool(self.conv4_bn(F.relu(self.conv4(x)))))\n",
        "        x = self.drop(self.pool(self.conv5_bn(F.relu(self.conv5(x)))))\n",
        "        x = self.drop(self.pool(self.conv6_bn(F.relu(self.conv6(x)))))\n",
        "        x = torch.flatten(x, 1) \n",
        "        # print(x.shape)\n",
        "        x = self.fc1_norm(F.relu(self.drop2(self.fc1(x))))\n",
        "        x = self.fc2_norm(F.relu(self.drop2(self.fc2(x))))\n",
        "        x = self.fc3(x)\n",
        "        # print(\"shape is: \", self.conv1.size, self.conv2.size, self.conv3.size, self.fc1.size, self.fc2.size)\n",
        "        \n",
        "        return x\n",
        "\n",
        "input_channels = images.shape[1]\n",
        "model = ConvNet(input_channels, num_filters=16, out_classes=len(classes))\n",
        "\n",
        "# Dummy inputs so we can plot a summary of the neural network's architecture and no. of parameters\n",
        "model=model.to(device)\n",
        "summary(model, input_size=(input_channels, 280, 280))\n",
        "\n",
        "# Double check if model exists\n",
        "# if os.path.exists(os.getcwd() + '/cnn_fmd.pth'):\n",
        "#     model.load_state_dict(torch.load(os.getcwd() + '/cnn_fmd.pth'))\n",
        "#     print(\"Loaded model from disk!\")"
      ],
      "id": "56a28e44"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DTe-UYFiV2vo",
        "outputId": "f42aef61-201e-40e8-d61c-21cbed2ee1dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1\n",
            "-------------------------------\n",
            "loss: 2.179982  [    0/28709]\n",
            "loss: 2.093157  [  640/28709]\n",
            "loss: 2.120187  [ 1280/28709]\n",
            "loss: 2.084296  [ 1920/28709]\n",
            "loss: 2.100147  [ 2560/28709]\n",
            "loss: 2.213719  [ 3200/28709]\n",
            "loss: 2.086516  [ 3840/28709]\n",
            "loss: 2.018636  [ 4480/28709]\n",
            "loss: 2.105625  [ 5120/28709]\n",
            "loss: 1.890409  [ 5760/28709]\n",
            "loss: 1.961200  [ 6400/28709]\n",
            "loss: 2.081982  [ 7040/28709]\n",
            "loss: 2.108464  [ 7680/28709]\n",
            "loss: 2.073572  [ 8320/28709]\n",
            "loss: 2.073661  [ 8960/28709]\n",
            "loss: 2.079431  [ 9600/28709]\n",
            "loss: 2.141845  [10240/28709]\n",
            "loss: 2.087440  [10880/28709]\n",
            "loss: 2.109288  [11520/28709]\n",
            "loss: 2.006692  [12160/28709]\n",
            "loss: 1.970860  [12800/28709]\n",
            "loss: 2.167706  [13440/28709]\n",
            "loss: 2.122305  [14080/28709]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-6c5d7f9566d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Epoch {t+1}\\n-------------------------------\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m     \u001b[0mmodel_train_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m     \u001b[0mmodel_test_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_dataloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-15-6c5d7f9566d3>\u001b[0m in \u001b[0;36mmodel_train_loader\u001b[0;34m(model, dataloader, criterion, optimizer)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0;31m# Compute prediction error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    679\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    682\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    719\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    720\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 721\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    722\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    723\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    230\u001b[0m         \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_transform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, pic)\u001b[0m\n\u001b[1;32m    132\u001b[0m             \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mConverted\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \"\"\"\n\u001b[0;32m--> 134\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torchvision/transforms/functional.py\u001b[0m in \u001b[0;36mto_tensor\u001b[0;34m(pic)\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;31m# handle PIL Image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0mmode_to_nptype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"I\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"I;16\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"F\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode_to_nptype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"1\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: int() argument must be a string, a bytes-like object or a number, not 'Image'"
          ]
        }
      ],
      "source": [
        "def model_train_loader(model, dataloader, criterion, optimizer):\n",
        "    size = len(dataloader.dataset)\n",
        "\n",
        "    model.train()\n",
        "    for batch, (X, y) in enumerate(dataloader):\n",
        "        # Compute prediction error\n",
        "        X=X.to(device)\n",
        "        y=y.to(device)\n",
        "        predictions = model(X)\n",
        "        loss = criterion(predictions, y)\n",
        "\n",
        "        # Backpropagation\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # Report loss every 10 batches\n",
        "        if batch % 10 == 0:\n",
        "            loss, current = loss.item(), batch * len(X)\n",
        "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")\n",
        "    \n",
        "            \n",
        "def model_test_loader(model, dataloader, criterion):\n",
        "    size = len(dataloader.dataset)\n",
        "    num_batches = len(dataloader)\n",
        "    # Tracking test loss (cross-entropy) and correct classification rate (accuracy)\n",
        "    test_loss, correct = 0, 0\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        model.eval()\n",
        "        for X, y in dataloader:\n",
        "            X=X.to(device)\n",
        "            y=y.to(device)\n",
        "            predictions = model(X)\n",
        "            test_loss += criterion(predictions, y)\n",
        "            correct += (predictions.argmax(1) == y).type(torch.float).sum().item()\n",
        "            \n",
        "    test_loss /= num_batches\n",
        "    correct /= size\n",
        "    print(f\"\\nTest Error\\n\\tAccuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")\n",
        "\n",
        "# The nn.CrossEntropyLoss() loss function automatically performs a log_softmax() to output\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "# One is enough for my toy example...\n",
        "num_epochs = 100\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.000025)#, momentum=0.9, nesterov=True)\n",
        "\n",
        "for t in range(num_epochs):\n",
        "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
        "    model_train_loader(model, train_dataloader, criterion, optimizer)\n",
        "    model_test_loader(model, test_dataloader, criterion)\n",
        "    \n",
        "# Saving the model file 'cnn_fmd.pth' to my current working directory (cwd)\n",
        "print(\"Saving model to disk!\")\n",
        "torch.save(model.state_dict(), os.getcwd() + '/cnn_fmd.pth')"
      ],
      "id": "DTe-UYFiV2vo"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "96dc7980",
        "outputId": "69533a4e-943e-42f1-b777-54331df521f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "GroundTruth:\n",
            " neutral\t fear \t sad  \t happy\t happy\t surprise\t happy\t neutral\t\n",
            "\n",
            "Predicted:\n",
            " neutral\t surprise\t neutral\t happy\t fear \t happy\t happy\t happy\t\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "### Plotting stuff ###\n",
        "fig = plt.figure(figsize=(20,20))\n",
        "\n",
        "# Get some random test dataset images\n",
        "dataiter = iter(test_dataloader)\n",
        "# Extract a batch\n",
        "images, labels = dataiter.next()\n",
        "# Show images\n",
        "imshow(torchvision.utils.make_grid(images[:num_display]))\n",
        "\n",
        "outputs = model(images)\n",
        "# Argmax take the most probable class\n",
        "_, predicted = torch.max(outputs, 1)\n",
        "\n",
        "# Print labels\n",
        "print(\"\\nGroundTruth:\\n\", \" \".join(f'{classes[labels[j]]:5s}\\t' for j in range(8)))\n",
        "\n",
        "print(\"\\nPredicted:\\n\", \" \".join(f'{classes[predicted[j]]:5s}\\t' for j in range(8)))"
      ],
      "id": "96dc7980"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d502a756",
        "outputId": "7e3a7224-5611-46d4-952a-0f280d2283c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the test set images: 37 %\n",
            "Confusion Matrix (columns: True class, rows: Predicted class):\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "correct = 0\n",
        "\n",
        "predicted_list = []\n",
        "labels_list = []\n",
        "\n",
        "# Since we're not training, we don't need to calculate the gradients for our outputs\n",
        "with torch.no_grad():\n",
        "    i = 0\n",
        "    for data in test_dataloader:\n",
        "        i += 1\n",
        "        images, labels = data\n",
        "        # Forward pass through network\n",
        "        outputs = model(images)\n",
        "        # Take most probable class\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        predicted_list.append(predicted.detach().numpy())\n",
        "        labels_list.append(labels.detach().numpy())\n",
        "        \n",
        "# Hacky conversions from a list of lists of arrays into joined numpy arrays\n",
        "predicted_list = np.array(list(itertools.chain.from_iterable(predicted_list)))\n",
        "labels_list = np.array(list(itertools.chain.from_iterable(labels_list)))\n",
        "# Count up number of labels and correct predictions\n",
        "correct = sum(predicted_list == labels_list)\n",
        "\n",
        "print(f\"Accuracy of the network on the test set images: {100 * correct // len(test_dataloader.dataset)} %\")\n",
        "\n",
        "print(\"Confusion Matrix (columns: True class, rows: Predicted class):\")\n",
        "conf_mat = confusion_matrix(labels_list, predicted_list);\n",
        "conf_display = ConfusionMatrixDisplay.from_predictions(labels_list, predicted_list, display_labels=classes, colorbar=False);\n",
        "plt.xlabel(\"True Labels\");\n",
        "plt.ylabel(\"Predicted Labels\");"
      ],
      "id": "d502a756"
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "Final Project.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}