Swin.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BumA-vhz21Ao",
        "outputId": "c4537d38-6c09-45e6-f6d8-cadaf0ee2faf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Connecting to MyDrive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Required Libraries\n",
        "import pandas as pd\n",
        "import os\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import warnings\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "\n",
        "import torch\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset , DataLoader\n",
        "from torchvision import transforms\n",
        "from torchvision.io import read_image\n",
        "from torchvision.models.swin_transformer import swin_s\n",
        "import torch.nn.functional as F\n",
        "from torchvision.utils import make_grid\n",
        "\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "ysEPBTok3gtD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(torch.__version__)\n",
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "print(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HCYARAVk3hkE",
        "outputId": "9ea2649c-ad41-41c8-ba7d-fd9aeb188e32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.3.0+cu121\n",
            "cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining the path to the directory containing the image folders\n",
        "path = \"drive/MyDrive/Nazir Folder/dogs_breed\"\n",
        "\n",
        "# Dictionary to store image paths and labels\n",
        "data = {\"imgpath\": [], \"labels\": []}\n",
        "\n",
        "category = os.listdir(path)\n",
        "for folder in category:\n",
        "    folderpath = os.path.join(path, folder)\n",
        "    filelist = os.listdir(folderpath)\n",
        "    # Iterate through each image file in the current category\n",
        "    for file in filelist:\n",
        "        fpath = os.path.join(folderpath, file)\n",
        "        # Append the image path and its corresponding label to the data dictionary\n",
        "        data[\"imgpath\"].append(fpath)\n",
        "        data[\"labels\"].append(folder)\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "lb = LabelEncoder()\n",
        "df['encoded_labels'] = lb.fit_transform(df['labels'])\n",
        "\n",
        "print(\"-------------Fetch files into a data frame-----------\")\n",
        "print(df.head())\n",
        "print(\"-------------Path to an image file------------------\")\n",
        "print(df.loc[175]['imgpath'])\n",
        "print(\"-----------Number of images per category--------------\")\n",
        "print(df.labels.value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdUzBviH3l9D",
        "outputId": "986fbe05-add7-4ee5-bc4a-b138d2cb3b47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------Fetch files into a data frame-----------\n",
            "                                             imgpath   labels  encoded_labels\n",
            "0  drive/MyDrive/Nazir Folder/dogs_breed/Bulldog/...  Bulldog               1\n",
            "1  drive/MyDrive/Nazir Folder/dogs_breed/Bulldog/...  Bulldog               1\n",
            "2  drive/MyDrive/Nazir Folder/dogs_breed/Bulldog/...  Bulldog               1\n",
            "3  drive/MyDrive/Nazir Folder/dogs_breed/Bulldog/...  Bulldog               1\n",
            "4  drive/MyDrive/Nazir Folder/dogs_breed/Bulldog/...  Bulldog               1\n",
            "-------------Path to an image file------------------\n",
            "drive/MyDrive/Nazir Folder/dogs_breed/Siberian Husky/husky-gallery-outdoors-1-min.jpeg\n",
            "-----------Number of images per category--------------\n",
            "labels\n",
            "Bulldog                154\n",
            "Dachshund              133\n",
            "Siberian Husky         118\n",
            "poodle                 107\n",
            "Golden Retriever       103\n",
            "Australian Shepherd    100\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the DataFrame into training and validation sets\n",
        "train_df, valid_df = train_test_split(df, train_size=0.80, shuffle=True, random_state=124)\n",
        "\n",
        "# Reset the index of the DataFrames after the split\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "valid_df = valid_df.reset_index(drop=True)\n",
        "\n",
        "print(\"#############Train Split###################\")\n",
        "print(train_df.head(5))\n",
        "print(train_df.shape)\n",
        "print(\"#############Test Split###################\")\n",
        "print(valid_df.head(5))\n",
        "print(valid_df.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Az24HK9R3uX8",
        "outputId": "556e3372-7a66-41bf-9a68-a382eaf9d56b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#############Train Split###################\n",
            "                                             imgpath               labels  \\\n",
            "0  drive/MyDrive/Nazir Folder/dogs_breed/Golden R...     Golden Retriever   \n",
            "1  drive/MyDrive/Nazir Folder/dogs_breed/Australi...  Australian Shepherd   \n",
            "2  drive/MyDrive/Nazir Folder/dogs_breed/Dachshun...            Dachshund   \n",
            "3  drive/MyDrive/Nazir Folder/dogs_breed/Bulldog/...              Bulldog   \n",
            "4  drive/MyDrive/Nazir Folder/dogs_breed/poodle/3...               poodle   \n",
            "\n",
            "   encoded_labels  \n",
            "0               3  \n",
            "1               0  \n",
            "2               2  \n",
            "3               1  \n",
            "4               5  \n",
            "(572, 3)\n",
            "#############Test Split###################\n",
            "                                             imgpath               labels  \\\n",
            "0  drive/MyDrive/Nazir Folder/dogs_breed/Dachshun...            Dachshund   \n",
            "1  drive/MyDrive/Nazir Folder/dogs_breed/Australi...  Australian Shepherd   \n",
            "2  drive/MyDrive/Nazir Folder/dogs_breed/Australi...  Australian Shepherd   \n",
            "3  drive/MyDrive/Nazir Folder/dogs_breed/Dachshun...            Dachshund   \n",
            "4  drive/MyDrive/Nazir Folder/dogs_breed/Bulldog/...              Bulldog   \n",
            "\n",
            "   encoded_labels  \n",
            "0               2  \n",
            "1               0  \n",
            "2               0  \n",
            "3               2  \n",
            "4               1  \n",
            "(143, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import transforms\n",
        "\n",
        "# Define image transformations for training and validation datasets\n",
        "image_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.RandomResizedCrop(size=256, scale=(0.8, 1.0)),\n",
        "        transforms.RandomRotation(degrees=15),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.CenterCrop(size=224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'valid': transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "    ])\n",
        "}"
      ],
      "metadata": {
        "id": "5cifX4hx315s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset\n",
        "from PIL import Image\n",
        "\n",
        "class DogsBreedDataset(Dataset):\n",
        "    def __init__(self, img_data, transform=None):\n",
        "        self.transform = transform\n",
        "        self.img_data = img_data\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.img_data)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        img_name = self.img_data.loc[index]['imgpath']\n",
        "        imge = Image.open(img_name).convert('RGB')\n",
        "\n",
        "        # Check the number of channels in the image\n",
        "        if imge.mode != 'RGB':\n",
        "            # If the image doesn't have RGB mode, raise an exception\n",
        "            raise ValueError(f\"Image {img_name} does not have RGB mode.\")\n",
        "\n",
        "        # Resize the image\n",
        "        image = imge.resize((224, 224))\n",
        "\n",
        "        if self.transform is not None:\n",
        "            image = self.transform(image)\n",
        "\n",
        "        label = torch.tensor(self.img_data.loc[index]['encoded_labels'])\n",
        "        return image, label\n",
        "\n",
        "# Create datasets\n",
        "training_data = DogsBreedDataset(train_df, transform=image_transforms['train'])\n",
        "validation_data = DogsBreedDataset(valid_df, transform=image_transforms['valid'])\n"
      ],
      "metadata": {
        "id": "iAiKSMx335_s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Define batch sizes\n",
        "train_batch_size = 200\n",
        "val_batch_size = 100\n",
        "\n",
        "# Create data loaders\n",
        "train_dataloader = DataLoader(training_data, batch_size=train_batch_size, shuffle=True)\n",
        "test_dataloader = DataLoader(validation_data, batch_size=val_batch_size, shuffle=True)\n",
        "\n",
        "# Print information about the data loaders\n",
        "print(\">> Number of Train Data : {} -- Batch Size : {} -- Number of Batch : {} \".format(len(train_dataloader.dataset), train_batch_size, len(train_dataloader)))\n",
        "print(\">> Number of Validation Data : {} -- Batch Size : {} -- Number of Batch : {} \".format(len(test_dataloader.dataset), val_batch_size, len(test_dataloader)))\n",
        "\n",
        "# Get one batch of data from the training data loader\n",
        "one_batch = iter(train_dataloader)\n",
        "train_features, train_labels = next(one_batch)\n",
        "\n",
        "# Print the shapes of the features and labels in the batch\n",
        "print(\"----------Batch Shape--------\")\n",
        "print(f\"Feature batch shape: {train_features.size()}\")\n",
        "print(\"----------Labels Shape--------\")\n",
        "print(f\"Labels batch shape: {train_labels.size()}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xq7wbdUe39sZ",
        "outputId": "6ca3e9b3-4397-4597-b1fd-4f7290dbdddf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">> Number of Train Data : 572 -- Batch Size : 200 -- Number of Batch : 3 \n",
            ">> Number of Validation Data : 143 -- Batch Size : 100 -- Number of Batch : 2 \n",
            "----------Batch Shape--------\n",
            "Feature batch shape: torch.Size([200, 3, 224, 224])\n",
            "----------Labels Shape--------\n",
            "Labels batch shape: torch.Size([200])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoImageProcessor, AutoModelForImageClassification\n",
        "\n",
        "model_name = 'microsoft/swin-tiny-patch4-window7-224'\n",
        "model = AutoModelForImageClassification.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "LRvhOZSe4pW4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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          ]
        },
        "outputId": "cb004b95-e841-4859-9910-383d0fa2a02f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/71.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "model.safetensors:   0%|          | 0.00/113M [00:00<?, ?B/s]"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Model: {model}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5eYp8ZFQQwl",
        "outputId": "addc1ec5-e677-4dac-a393-ec0863542056"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: SwinForImageClassification(\n",
            "  (swin): SwinModel(\n",
            "    (embeddings): SwinEmbeddings(\n",
            "      (patch_embeddings): SwinPatchEmbeddings(\n",
            "        (projection): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))\n",
            "      )\n",
            "      (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "      (dropout): Dropout(p=0.0, inplace=False)\n",
            "    )\n",
            "    (encoder): SwinEncoder(\n",
            "      (layers): ModuleList(\n",
            "        (0): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-1): 2 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (key): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (value): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=96, out_features=384, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=384, out_features=96, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (downsample): SwinPatchMerging(\n",
            "            (reduction): Linear(in_features=384, out_features=192, bias=False)\n",
            "            (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          )\n",
            "        )\n",
            "        (1): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-1): 2 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (key): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (value): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=192, out_features=768, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=768, out_features=192, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (downsample): SwinPatchMerging(\n",
            "            (reduction): Linear(in_features=768, out_features=384, bias=False)\n",
            "            (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "          )\n",
            "        )\n",
            "        (2): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-5): 6 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (key): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (value): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (downsample): SwinPatchMerging(\n",
            "            (reduction): Linear(in_features=1536, out_features=768, bias=False)\n",
            "            (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
            "          )\n",
            "        )\n",
            "        (3): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-1): 2 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "    (pooler): AdaptiveAvgPool1d(output_size=1)\n",
            "  )\n",
            "  (classifier): Linear(in_features=768, out_features=1000, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "n_inputs = model.classifier.in_features\n",
        "model.classifier = nn.Sequential(\n",
        "    nn.Linear(768, 6)\n",
        ")\n",
        "model = model.to(device)"
      ],
      "metadata": {
        "id": "ZrQHyrds5gZA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Head: {model}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7SThYdK8tXS",
        "outputId": "8817b057-eb84-4d8e-def3-37bbec067906"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Head: SwinForImageClassification(\n",
            "  (swin): SwinModel(\n",
            "    (embeddings): SwinEmbeddings(\n",
            "      (patch_embeddings): SwinPatchEmbeddings(\n",
            "        (projection): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))\n",
            "      )\n",
            "      (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "      (dropout): Dropout(p=0.0, inplace=False)\n",
            "    )\n",
            "    (encoder): SwinEncoder(\n",
            "      (layers): ModuleList(\n",
            "        (0): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-1): 2 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (key): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (value): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=96, out_features=384, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=384, out_features=96, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (downsample): SwinPatchMerging(\n",
            "            (reduction): Linear(in_features=384, out_features=192, bias=False)\n",
            "            (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          )\n",
            "        )\n",
            "        (1): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-1): 2 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (key): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (value): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=192, out_features=768, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=768, out_features=192, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (downsample): SwinPatchMerging(\n",
            "            (reduction): Linear(in_features=768, out_features=384, bias=False)\n",
            "            (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "          )\n",
            "        )\n",
            "        (2): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-5): 6 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (key): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (value): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (downsample): SwinPatchMerging(\n",
            "            (reduction): Linear(in_features=1536, out_features=768, bias=False)\n",
            "            (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
            "          )\n",
            "        )\n",
            "        (3): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-1): 2 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "    (pooler): AdaptiveAvgPool1d(output_size=1)\n",
            "  )\n",
            "  (classifier): Sequential(\n",
            "    (0): Linear(in_features=768, out_features=6, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Move the model to the specified device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = model.to(device)\n",
        "\n",
        "# Define the loss function and optimizer\n",
        "criterion = nn.CrossEntropyLoss().to(device)\n",
        "optimizer = optim.AdamW(model.classifier.parameters(), lr=0.001)\n",
        "\n",
        "print(\"Model is ready for training.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Qc2x79v6C0X",
        "outputId": "926c5bd3-eb47-4c28-b030-b5963d47c2ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model is ready for training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "def train_model(model, optimizer, criterion, data_loader, device):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "    correct = 0\n",
        "\n",
        "    for i, (inputs, labels) in enumerate(data_loader):  # Unpack data directly\n",
        "        # Move inputs and labels to the appropriate device\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Zero the parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(inputs).logits  # Accessing logits if using Hugging Face model\n",
        "\n",
        "        # Compute loss\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward pass and optimization\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # Calculate predictions\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "\n",
        "    # Calculate average loss and accuracy\n",
        "    avg_train_loss = running_loss / len(data_loader.dataset)\n",
        "    avg_train_acc = (correct / len(data_loader.dataset)) * 100\n",
        "\n",
        "    print(f\">>> Train loss: {avg_train_loss:.4f} ---- Accuracy Train: {avg_train_acc:.2f}%\")\n",
        "\n",
        "    return avg_train_loss, avg_train_acc\n"
      ],
      "metadata": {
        "id": "24gm_pMk6jav"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score, recall_score, roc_auc_score, matthews_corrcoef\n",
        "from sklearn.preprocessing import label_binarize\n",
        "\n",
        "def test_model(model, criterion, data_loader, device):\n",
        "    model.eval()\n",
        "\n",
        "    val_loss = 0.0\n",
        "    correct = 0\n",
        "    all_labels = []\n",
        "    all_predictions = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in data_loader:\n",
        "            # Move inputs and labels to the appropriate device\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            # Forward pass\n",
        "            outputs = model(inputs).logits  # Accessing logits if using Hugging Face model\n",
        "            loss = criterion(outputs, labels)\n",
        "            val_loss += loss.item() * inputs.size(0)\n",
        "\n",
        "            # Calculate predictions\n",
        "            _, predicted = torch.max(outputs, 1)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "            # Collect all labels and predictions for metric calculation\n",
        "            all_labels.extend(labels.cpu().numpy())\n",
        "            all_predictions.extend(predicted.cpu().numpy())\n",
        "\n",
        "    # Calculate average validation loss and accuracy\n",
        "    avg_val_acc = (correct / len(data_loader.dataset)) * 100\n",
        "    avg_val_loss = val_loss / len(data_loader.dataset)\n",
        "\n",
        "    # Calculate precision, recall, sensitivity, specificity, MCC, and AUC\n",
        "    precision = precision_score(all_labels, all_predictions, average='weighted')\n",
        "    recall = recall_score(all_labels, all_predictions, average='weighted')\n",
        "    sensitivity = recall_score(all_labels, all_predictions, average='macro')\n",
        "    specificity = recall_score(all_labels, all_predictions, average='macro')\n",
        "    mcc = matthews_corrcoef(all_labels, all_predictions)\n",
        "\n",
        "    # AUC calculation\n",
        "    if len(set(all_labels)) > 2:  # Multiclass\n",
        "        auc = roc_auc_score(label_binarize(all_labels, classes=np.unique(all_labels)), label_binarize(all_predictions, classes=np.unique(all_labels)), average='macro', multi_class='ovr')\n",
        "    else:  # Binary\n",
        "        auc = roc_auc_score(all_labels, all_predictions)\n",
        "\n",
        "    # Print scores\n",
        "    print(\">>> Validation loss {:.4f} ---- Validation Accuracy {:.2f}%\".format(avg_val_loss, avg_val_acc))\n",
        "    print(f\"Recall: {recall:.4f}\")\n",
        "    print(f\"Precision: {precision:.4f}\")\n",
        "    print(f\"Sensitivity: {sensitivity:.4f}\")\n",
        "    print(f\"Specificity: {specificity:.4f}\")\n",
        "    print(f\"Matthews Correlation Coefficient: {mcc:.4f}\")\n",
        "    print(f\"Area Under Curve: {auc:.4f}\")\n",
        "\n",
        "    return avg_val_loss, avg_val_acc\n"
      ],
      "metadata": {
        "id": "7U2bYYrN6qRN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 20\n",
        "history = {\"train_loss\": [], \"train_acc\": [], \"val_loss\": [], \"val_acc\": []}\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    print(f\"------------------ Training Epoch {epoch+1} ------------------\")\n",
        "    train_loss, train_acc = train_model(model, optimizer, criterion, train_dataloader, device)\n",
        "    val_loss, val_acc = test_model(model, criterion, test_dataloader, device)\n",
        "\n",
        "    history[\"train_loss\"].append(train_loss)\n",
        "    history[\"train_acc\"].append(train_acc)\n",
        "    history[\"val_loss\"].append(val_loss)\n",
        "    history[\"val_acc\"].append(val_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c07SZAM-6ruf",
        "outputId": "19ce2f8e-3dd7-409f-a828-943f47b13961"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------ Training Epoch 1 ------------------\n",
            ">>> Train loss: 1.7354 ---- Accuracy Train: 27.80%\n",
            ">>> Validation loss 1.4622 ---- Validation Accuracy 64.34%\n",
            "Recall: 0.6434\n",
            "Precision: 0.6574\n",
            "Sensitivity: 0.6422\n",
            "Specificity: 0.6422\n",
            "Matthews Correlation Coefficient: 0.5729\n",
            "Area Under Curve: 0.7851\n",
            "------------------ Training Epoch 2 ------------------\n",
            ">>> Train loss: 1.3699 ---- Accuracy Train: 75.52%\n",
            ">>> Validation loss 1.1168 ---- Validation Accuracy 88.11%\n",
            "Recall: 0.8811\n",
            "Precision: 0.8919\n",
            "Sensitivity: 0.8710\n",
            "Specificity: 0.8710\n",
            "Matthews Correlation Coefficient: 0.8584\n",
            "Area Under Curve: 0.9237\n",
            "------------------ Training Epoch 3 ------------------\n",
            ">>> Train loss: 1.0564 ---- Accuracy Train: 91.78%\n",
            ">>> Validation loss 0.8352 ---- Validation Accuracy 93.01%\n",
            "Recall: 0.9301\n",
            "Precision: 0.9351\n",
            "Sensitivity: 0.9284\n",
            "Specificity: 0.9284\n",
            "Matthews Correlation Coefficient: 0.9167\n",
            "Area Under Curve: 0.9572\n",
            "------------------ Training Epoch 4 ------------------\n",
            ">>> Train loss: 0.7990 ---- Accuracy Train: 95.45%\n",
            ">>> Validation loss 0.6238 ---- Validation Accuracy 96.50%\n",
            "Recall: 0.9650\n",
            "Precision: 0.9679\n",
            "Sensitivity: 0.9651\n",
            "Specificity: 0.9651\n",
            "Matthews Correlation Coefficient: 0.9583\n",
            "Area Under Curve: 0.9791\n",
            "------------------ Training Epoch 5 ------------------\n",
            ">>> Train loss: 0.6143 ---- Accuracy Train: 96.33%\n",
            ">>> Validation loss 0.4704 ---- Validation Accuracy 97.20%\n",
            "Recall: 0.9720\n",
            "Precision: 0.9735\n",
            "Sensitivity: 0.9710\n",
            "Specificity: 0.9710\n",
            "Matthews Correlation Coefficient: 0.9664\n",
            "Area Under Curve: 0.9827\n",
            "------------------ Training Epoch 6 ------------------\n",
            ">>> Train loss: 0.4730 ---- Accuracy Train: 97.38%\n",
            ">>> Validation loss 0.3635 ---- Validation Accuracy 97.90%\n",
            "Recall: 0.9790\n",
            "Precision: 0.9798\n",
            "Sensitivity: 0.9783\n",
            "Specificity: 0.9783\n",
            "Matthews Correlation Coefficient: 0.9748\n",
            "Area Under Curve: 0.9870\n",
            "------------------ Training Epoch 7 ------------------\n",
            ">>> Train loss: 0.3808 ---- Accuracy Train: 97.55%\n",
            ">>> Validation loss 0.2899 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 8 ------------------\n",
            ">>> Train loss: 0.3245 ---- Accuracy Train: 96.68%\n",
            ">>> Validation loss 0.2387 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 9 ------------------\n",
            ">>> Train loss: 0.2699 ---- Accuracy Train: 97.38%\n",
            ">>> Validation loss 0.2025 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 10 ------------------\n",
            ">>> Train loss: 0.2341 ---- Accuracy Train: 98.08%\n",
            ">>> Validation loss 0.1764 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 11 ------------------\n",
            ">>> Train loss: 0.2040 ---- Accuracy Train: 98.43%\n",
            ">>> Validation loss 0.1573 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 12 ------------------\n",
            ">>> Train loss: 0.1854 ---- Accuracy Train: 97.38%\n",
            ">>> Validation loss 0.1431 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 13 ------------------\n",
            ">>> Train loss: 0.1765 ---- Accuracy Train: 97.73%\n",
            ">>> Validation loss 0.1321 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 14 ------------------\n",
            ">>> Train loss: 0.1505 ---- Accuracy Train: 97.55%\n",
            ">>> Validation loss 0.1236 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 15 ------------------\n",
            ">>> Train loss: 0.1595 ---- Accuracy Train: 97.90%\n",
            ">>> Validation loss 0.1168 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 16 ------------------\n",
            ">>> Train loss: 0.1381 ---- Accuracy Train: 97.55%\n",
            ">>> Validation loss 0.1114 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 17 ------------------\n",
            ">>> Train loss: 0.1223 ---- Accuracy Train: 98.78%\n",
            ">>> Validation loss 0.1069 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 18 ------------------\n",
            ">>> Train loss: 0.1240 ---- Accuracy Train: 97.73%\n",
            ">>> Validation loss 0.1031 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 19 ------------------\n",
            ">>> Train loss: 0.1102 ---- Accuracy Train: 98.08%\n",
            ">>> Validation loss 0.0999 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n",
            "------------------ Training Epoch 20 ------------------\n",
            ">>> Train loss: 0.1058 ---- Accuracy Train: 98.60%\n",
            ">>> Validation loss 0.0973 ---- Validation Accuracy 98.60%\n",
            "Recall: 0.9860\n",
            "Precision: 0.9865\n",
            "Sensitivity: 0.9881\n",
            "Specificity: 0.9881\n",
            "Matthews Correlation Coefficient: 0.9833\n",
            "Area Under Curve: 0.9926\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tr_acc = history[\"train_acc\"]\n",
        "tr_loss = history[\"train_loss\"]\n",
        "val_acc = history[\"val_acc\"]\n",
        "val_loss = history[\"val_loss\"]\n",
        "\n",
        "\n",
        "index_loss = np.argmin(val_loss)\n",
        "val_lowest = val_loss[index_loss]\n",
        "index_acc = np.argmax(val_acc)\n",
        "acc_highest = val_acc[index_acc]\n",
        "Epochs = [i+1 for i in range(len(tr_acc))]\n",
        "loss_label = f'best epoch= {str(index_loss + 1)}'\n",
        "acc_label = f'best epoch= {str(index_acc + 1)}'\n",
        "\n",
        "# Plot training history\n",
        "plt.figure(figsize= (20, 8))\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(Epochs, tr_loss, 'r', label= 'Training loss')\n",
        "plt.plot(Epochs, val_loss, 'g', label= 'Validation loss')\n",
        "plt.scatter(index_loss + 1, val_lowest, s= 150, c= 'blue', label= loss_label)\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(Epochs, tr_acc, 'r', label= 'Training Accuracy')\n",
        "plt.plot(Epochs, val_acc, 'g', label= 'Validation Accuracy')\n",
        "plt.scatter(index_acc + 1 , acc_highest, s= 150, c= 'blue', label= acc_label)\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "21nA2cqC-Ori",
        "outputId": "91f88a73-90ec-4fc1-856d-d12fd77ec64b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test_model1(model, criterion, test_dataloader, device)"
      ],
      "metadata": {
        "id": "vE0Sf3Pw-h4c"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}