CNN_Cataract.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "Gmg5y4B9NHNv",
        "7RVq9ONuqA7y",
        "SG4CamEqqA_K",
        "OT7X50-dNwlE",
        "OALTxCBjOUby"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 1.Import Library dan Mount Drive"
      ],
      "metadata": {
        "id": "R_Yc6NpZqAzM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kbzQ6ZUGUHQb",
        "outputId": "6069a8b4-f711-4cec-d1a4-a459253b5a0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yy2hXiNUp7GY"
      },
      "outputs": [],
      "source": [
        "# Impor pustaka yang diperlukan\n",
        "import torch\n",
        "import numpy as np\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import os\n",
        "import torchvision\n",
        "import random\n",
        "import shutil\n",
        "from torchsummary import summary\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision import transforms, models\n",
        "from torch.utils.data import DataLoader, random_split\n",
        "from torchvision.transforms import Compose, Resize, ToTensor, Normalize\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. Direktori sumber dan Tujuan"
      ],
      "metadata": {
        "id": "Gmg5y4B9NHNv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the source and destination directories\n",
        "data_dir = \"/content/drive/MyDrive/dataset/coba\"\n",
        "dest_dir = \"/content/drive/MyDrive/dataset/dest\""
      ],
      "metadata": {
        "id": "O7jCwsJuHeXC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3. Pre-processing\n"
      ],
      "metadata": {
        "id": "7RVq9ONuqA7y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# membagi data menjadi 80% train, 20% uji model\n",
        "split_ratio = 0.2\n",
        "validation_split = 0.2"
      ],
      "metadata": {
        "id": "9s9gZOlN7W_O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Iterasi melalui setiap subfolder dalam direktori sumber dan membagi data menjadi set pelatihan dan pengujian.\n",
        "for root, dirs, files in os.walk(data_dir):\n",
        "    if files:\n",
        "        # Create corresponding subfolders in the destination directory\n",
        "        dest_root = os.path.join(dest_dir, os.path.relpath(root, data_dir))\n",
        "        os.makedirs(os.path.join(dest_root, 'train'), exist_ok=True)  # Create train directory\n",
        "        os.makedirs(os.path.join(dest_root, 'test'), exist_ok=True)   # Create test directory\n",
        "\n",
        "        # Randomly shuffle the files\n",
        "        random.shuffle(files)\n",
        "\n",
        "        # Calculate the number of files for the train and test sets\n",
        "        num_files = len(files)\n",
        "        num_train_files = int(num_files * split_ratio)\n",
        "        train_files = files[:num_train_files]\n",
        "        test_files = files[num_train_files:]\n",
        "\n",
        "        # Copy the train and test files to their respective subfolders\n",
        "        for file in train_files:\n",
        "            src_path = os.path.join(root, file)\n",
        "            dest_path = os.path.join(dest_root, 'train', file)\n",
        "            shutil.copy2(src_path, dest_path)\n",
        "\n",
        "        for file in test_files:\n",
        "            src_path = os.path.join(root, file)\n",
        "            dest_path = os.path.join(dest_root, 'test', file)\n",
        "            shutil.copy2(src_path, dest_path)\n",
        "\n",
        "print(\"Data splitting completed.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YSFOMaA5qv8d",
        "outputId": "3af02794-3e4d-4845-b3ef-e78f3d26ab24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data splitting completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4. Data Transform"
      ],
      "metadata": {
        "id": "SG4CamEqqA_K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = \"/content/drive/MyDrive/dataset/dest\""
      ],
      "metadata": {
        "id": "rpU8Tkx2JLgO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Membuat transformasi data untuk mengubah ukuran gambar, mengonversi gambar ke tensor PyTorch, dan normalisasi.\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize((299, 299)),  # InceptionV3 input size\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n"
      ],
      "metadata": {
        "id": "oN9Be0RAq1ki"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a dataset from your image folder with subfolders\n",
        "dataset = torchvision.datasets.ImageFolder(root=data_dir, transform=transform)\n",
        "\n",
        "# Calculate the sizes for the training and validation sets\n",
        "num_data = len(dataset)\n",
        "num_valid = int(validation_split * num_data)\n",
        "num_train = num_data - num_valid\n"
      ],
      "metadata": {
        "id": "2tiIY4U7q1h6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the root directory where your data is located\n",
        "data_root = \"/content/drive/MyDrive/dataset/dest\"  # Your dataset root directory\n",
        "\n",
        "# Define the transformation to apply to your images\n",
        "transform = Compose([\n",
        "    Resize((224, 224)),  # Resize the images to a consistent size\n",
        "    ToTensor(),           # Convert images to PyTorch tensors\n",
        "    Normalize(            # Normalize the pixel values\n",
        "        mean=[0.485, 0.456, 0.406],  # Mean values for RGB channels\n",
        "        std=[0.229, 0.224, 0.225]    # Standard deviation for RGB channels\n",
        "    )\n",
        "])\n"
      ],
      "metadata": {
        "id": "OoVqu19Aq1fb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def show_one_sample_per_class(dataset):\n",
        "    num_classes = len(dataset.classes)\n",
        "    fig, axes = plt.subplots(1, num_classes, figsize=(15, 3))\n",
        "\n",
        "    for class_idx, class_name in enumerate(dataset.classes):\n",
        "        class_indices = [idx for idx, label in enumerate(dataset.targets) if label == class_idx]\n",
        "        random_idx = np.random.choice(class_indices)\n",
        "        sample_image, sample_label = dataset[random_idx]\n",
        "\n",
        "        ax = axes[class_idx]\n",
        "        ax.imshow(np.transpose(sample_image, (1, 2, 0)))  # Transpose to (H, W, C)\n",
        "        ax.set_title(class_name)\n",
        "        ax.axis('off')\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "# Display one sample image from each unique class in the dataset\n",
        "show_one_sample_per_class(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZtwF5gSzXng",
        "outputId": "6a8d2e12-8059-4b23-e141-460de24e2131"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Split Dataset"
      ],
      "metadata": {
        "id": "OT7X50-dNwlE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghitung ukuran set pelatihan dan validasi.\n",
        "# Membagi dataset menjadi set pelatihan dan validasi.\n",
        "train_dataset, valid_dataset = random_split(dataset, [num_train, num_valid])\n"
      ],
      "metadata": {
        "id": "FEvnqB8Fq1ck"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Data Loader"
      ],
      "metadata": {
        "id": "M7_4iG_yqBB7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create data loaders for training and validation\n",
        "batch_size = 16\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)"
      ],
      "metadata": {
        "id": "QPoxkaazrAlr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 8. Arsitektur Inception V3\n",
        "- Menggunakan model InceptionV3 yang sudah dilatih.\n",
        "- Mengganti lapisan fully connected terakhir untuk jumlah kelas yang sesuai dengan dataset.\n",
        "- Memindahkan model ke perangkat yang sesuai (CPU atau GPU)."
      ],
      "metadata": {
        "id": "886unrH7QL7f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use InceptionV3 model\n",
        "model = models.inception_v3(pretrained=True)\n",
        "# Freeze all layers\n",
        "for param in model.parameters():\n",
        "    param.requires_grad = False"
      ],
      "metadata": {
        "id": "9nI3dCLPNTsO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcf2838a-d32d-4dc6-9d3f-367d54d9ae7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=Inception_V3_Weights.IMAGENET1K_V1`. You can also use `weights=Inception_V3_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth\" to /root/.cache/torch/hub/checkpoints/inception_v3_google-0cc3c7bd.pth\n",
            "100%|██████████| 104M/104M [00:00<00:00, 122MB/s] \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Modify the final fully connected layer for your specific number of classes\n",
        "num_classes = len(dataset.classes)\n",
        "model.AuxLogits.fc = nn.Linear(768, num_classes)\n",
        "model.fc = nn.Linear(2048, num_classes)"
      ],
      "metadata": {
        "id": "ahI15S0ENVTl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Move the model to the appropriate device (CPU or GPU)\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)"
      ],
      "metadata": {
        "id": "rigvZ-JxNZ1W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "666fe079-3117-4c44-c760-85f82b54eefc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Inception3(\n",
              "  (Conv2d_1a_3x3): BasicConv2d(\n",
              "    (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
              "    (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (Conv2d_2a_3x3): BasicConv2d(\n",
              "    (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (Conv2d_2b_3x3): BasicConv2d(\n",
              "    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (Conv2d_3b_1x1): BasicConv2d(\n",
              "    (conv): Conv2d(64, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (Conv2d_4a_3x3): BasicConv2d(\n",
              "    (conv): Conv2d(80, 192, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (Mixed_5b): InceptionA(\n",
              "    (branch1x1): BasicConv2d(\n",
              "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch5x5_1): BasicConv2d(\n",
              "      (conv): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch5x5_2): BasicConv2d(\n",
              "      (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_1): BasicConv2d(\n",
              "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_2): BasicConv2d(\n",
              "      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_3): BasicConv2d(\n",
              "      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch_pool): BasicConv2d(\n",
              "      (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (Mixed_5c): InceptionA(\n",
              "    (branch1x1): BasicConv2d(\n",
              "      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch5x5_1): BasicConv2d(\n",
              "      (conv): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch5x5_2): BasicConv2d(\n",
              "      (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_1): BasicConv2d(\n",
              "      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_2): BasicConv2d(\n",
              "      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_3): BasicConv2d(\n",
              "      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch_pool): BasicConv2d(\n",
              "      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (Mixed_5d): InceptionA(\n",
              "    (branch1x1): BasicConv2d(\n",
              "      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch5x5_1): BasicConv2d(\n",
              "      (conv): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch5x5_2): BasicConv2d(\n",
              "      (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_1): BasicConv2d(\n",
              "      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_2): BasicConv2d(\n",
              "      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_3): BasicConv2d(\n",
              "      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch_pool): BasicConv2d(\n",
              "      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (Mixed_6a): InceptionB(\n",
              "    (branch3x3): BasicConv2d(\n",
              "      (conv): Conv2d(288, 384, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_1): BasicConv2d(\n",
              "      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_2): BasicConv2d(\n",
              "      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_3): BasicConv2d(\n",
              "      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
              "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (Mixed_6b): InceptionC(\n",
              "    (branch1x1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_2): BasicConv2d(\n",
              "      (conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_3): BasicConv2d(\n",
              "      (conv): Conv2d(128, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_2): BasicConv2d(\n",
              "      (conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_3): BasicConv2d(\n",
              "      (conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_4): BasicConv2d(\n",
              "      (conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_5): BasicConv2d(\n",
              "      (conv): Conv2d(128, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch_pool): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (Mixed_6c): InceptionC(\n",
              "    (branch1x1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_2): BasicConv2d(\n",
              "      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_3): BasicConv2d(\n",
              "      (conv): Conv2d(160, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_2): BasicConv2d(\n",
              "      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_3): BasicConv2d(\n",
              "      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_4): BasicConv2d(\n",
              "      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_5): BasicConv2d(\n",
              "      (conv): Conv2d(160, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch_pool): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (Mixed_6d): InceptionC(\n",
              "    (branch1x1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_2): BasicConv2d(\n",
              "      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_3): BasicConv2d(\n",
              "      (conv): Conv2d(160, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_2): BasicConv2d(\n",
              "      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_3): BasicConv2d(\n",
              "      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_4): BasicConv2d(\n",
              "      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_5): BasicConv2d(\n",
              "      (conv): Conv2d(160, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch_pool): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (Mixed_6e): InceptionC(\n",
              "    (branch1x1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_2): BasicConv2d(\n",
              "      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7_3): BasicConv2d(\n",
              "      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_2): BasicConv2d(\n",
              "      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_3): BasicConv2d(\n",
              "      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_4): BasicConv2d(\n",
              "      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7dbl_5): BasicConv2d(\n",
              "      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch_pool): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (AuxLogits): InceptionAux(\n",
              "    (conv0): BasicConv2d(\n",
              "      (conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (conv1): BasicConv2d(\n",
              "      (conv): Conv2d(128, 768, kernel_size=(5, 5), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(768, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (fc): Linear(in_features=768, out_features=4, bias=True)\n",
              "  )\n",
              "  (Mixed_7a): InceptionD(\n",
              "    (branch3x3_1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3_2): BasicConv2d(\n",
              "      (conv): Conv2d(192, 320, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
              "      (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7x3_1): BasicConv2d(\n",
              "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7x3_2): BasicConv2d(\n",
              "      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7x3_3): BasicConv2d(\n",
              "      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch7x7x3_4): BasicConv2d(\n",
              "      (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (Mixed_7b): InceptionE(\n",
              "    (branch1x1): BasicConv2d(\n",
              "      (conv): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3_1): BasicConv2d(\n",
              "      (conv): Conv2d(1280, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3_2a): BasicConv2d(\n",
              "      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3_2b): BasicConv2d(\n",
              "      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_1): BasicConv2d(\n",
              "      (conv): Conv2d(1280, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_2): BasicConv2d(\n",
              "      (conv): Conv2d(448, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_3a): BasicConv2d(\n",
              "      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_3b): BasicConv2d(\n",
              "      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch_pool): BasicConv2d(\n",
              "      (conv): Conv2d(1280, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (Mixed_7c): InceptionE(\n",
              "    (branch1x1): BasicConv2d(\n",
              "      (conv): Conv2d(2048, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3_1): BasicConv2d(\n",
              "      (conv): Conv2d(2048, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3_2a): BasicConv2d(\n",
              "      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3_2b): BasicConv2d(\n",
              "      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_1): BasicConv2d(\n",
              "      (conv): Conv2d(2048, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_2): BasicConv2d(\n",
              "      (conv): Conv2d(448, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_3a): BasicConv2d(\n",
              "      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch3x3dbl_3b): BasicConv2d(\n",
              "      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch_pool): BasicConv2d(\n",
              "      (conv): Conv2d(2048, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (dropout): Dropout(p=0.5, inplace=False)\n",
              "  (fc): Linear(in_features=2048, out_features=4, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summary(model, input_size=(3, 299, 299))"
      ],
      "metadata": {
        "id": "jDdY_HOusyF_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cffef450-6394-44d9-d102-2c2675dc2f94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1         [-1, 32, 149, 149]             864\n",
            "       BatchNorm2d-2         [-1, 32, 149, 149]              64\n",
            "       BasicConv2d-3         [-1, 32, 149, 149]               0\n",
            "            Conv2d-4         [-1, 32, 147, 147]           9,216\n",
            "       BatchNorm2d-5         [-1, 32, 147, 147]              64\n",
            "       BasicConv2d-6         [-1, 32, 147, 147]               0\n",
            "            Conv2d-7         [-1, 64, 147, 147]          18,432\n",
            "       BatchNorm2d-8         [-1, 64, 147, 147]             128\n",
            "       BasicConv2d-9         [-1, 64, 147, 147]               0\n",
            "        MaxPool2d-10           [-1, 64, 73, 73]               0\n",
            "           Conv2d-11           [-1, 80, 73, 73]           5,120\n",
            "      BatchNorm2d-12           [-1, 80, 73, 73]             160\n",
            "      BasicConv2d-13           [-1, 80, 73, 73]               0\n",
            "           Conv2d-14          [-1, 192, 71, 71]         138,240\n",
            "      BatchNorm2d-15          [-1, 192, 71, 71]             384\n",
            "      BasicConv2d-16          [-1, 192, 71, 71]               0\n",
            "        MaxPool2d-17          [-1, 192, 35, 35]               0\n",
            "           Conv2d-18           [-1, 64, 35, 35]          12,288\n",
            "      BatchNorm2d-19           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-20           [-1, 64, 35, 35]               0\n",
            "           Conv2d-21           [-1, 48, 35, 35]           9,216\n",
            "      BatchNorm2d-22           [-1, 48, 35, 35]              96\n",
            "      BasicConv2d-23           [-1, 48, 35, 35]               0\n",
            "           Conv2d-24           [-1, 64, 35, 35]          76,800\n",
            "      BatchNorm2d-25           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-26           [-1, 64, 35, 35]               0\n",
            "           Conv2d-27           [-1, 64, 35, 35]          12,288\n",
            "      BatchNorm2d-28           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-29           [-1, 64, 35, 35]               0\n",
            "           Conv2d-30           [-1, 96, 35, 35]          55,296\n",
            "      BatchNorm2d-31           [-1, 96, 35, 35]             192\n",
            "      BasicConv2d-32           [-1, 96, 35, 35]               0\n",
            "           Conv2d-33           [-1, 96, 35, 35]          82,944\n",
            "      BatchNorm2d-34           [-1, 96, 35, 35]             192\n",
            "      BasicConv2d-35           [-1, 96, 35, 35]               0\n",
            "           Conv2d-36           [-1, 32, 35, 35]           6,144\n",
            "      BatchNorm2d-37           [-1, 32, 35, 35]              64\n",
            "      BasicConv2d-38           [-1, 32, 35, 35]               0\n",
            "       InceptionA-39          [-1, 256, 35, 35]               0\n",
            "           Conv2d-40           [-1, 64, 35, 35]          16,384\n",
            "      BatchNorm2d-41           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-42           [-1, 64, 35, 35]               0\n",
            "           Conv2d-43           [-1, 48, 35, 35]          12,288\n",
            "      BatchNorm2d-44           [-1, 48, 35, 35]              96\n",
            "      BasicConv2d-45           [-1, 48, 35, 35]               0\n",
            "           Conv2d-46           [-1, 64, 35, 35]          76,800\n",
            "      BatchNorm2d-47           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-48           [-1, 64, 35, 35]               0\n",
            "           Conv2d-49           [-1, 64, 35, 35]          16,384\n",
            "      BatchNorm2d-50           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-51           [-1, 64, 35, 35]               0\n",
            "           Conv2d-52           [-1, 96, 35, 35]          55,296\n",
            "      BatchNorm2d-53           [-1, 96, 35, 35]             192\n",
            "      BasicConv2d-54           [-1, 96, 35, 35]               0\n",
            "           Conv2d-55           [-1, 96, 35, 35]          82,944\n",
            "      BatchNorm2d-56           [-1, 96, 35, 35]             192\n",
            "      BasicConv2d-57           [-1, 96, 35, 35]               0\n",
            "           Conv2d-58           [-1, 64, 35, 35]          16,384\n",
            "      BatchNorm2d-59           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-60           [-1, 64, 35, 35]               0\n",
            "       InceptionA-61          [-1, 288, 35, 35]               0\n",
            "           Conv2d-62           [-1, 64, 35, 35]          18,432\n",
            "      BatchNorm2d-63           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-64           [-1, 64, 35, 35]               0\n",
            "           Conv2d-65           [-1, 48, 35, 35]          13,824\n",
            "      BatchNorm2d-66           [-1, 48, 35, 35]              96\n",
            "      BasicConv2d-67           [-1, 48, 35, 35]               0\n",
            "           Conv2d-68           [-1, 64, 35, 35]          76,800\n",
            "      BatchNorm2d-69           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-70           [-1, 64, 35, 35]               0\n",
            "           Conv2d-71           [-1, 64, 35, 35]          18,432\n",
            "      BatchNorm2d-72           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-73           [-1, 64, 35, 35]               0\n",
            "           Conv2d-74           [-1, 96, 35, 35]          55,296\n",
            "      BatchNorm2d-75           [-1, 96, 35, 35]             192\n",
            "      BasicConv2d-76           [-1, 96, 35, 35]               0\n",
            "           Conv2d-77           [-1, 96, 35, 35]          82,944\n",
            "      BatchNorm2d-78           [-1, 96, 35, 35]             192\n",
            "      BasicConv2d-79           [-1, 96, 35, 35]               0\n",
            "           Conv2d-80           [-1, 64, 35, 35]          18,432\n",
            "      BatchNorm2d-81           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-82           [-1, 64, 35, 35]               0\n",
            "       InceptionA-83          [-1, 288, 35, 35]               0\n",
            "           Conv2d-84          [-1, 384, 17, 17]         995,328\n",
            "      BatchNorm2d-85          [-1, 384, 17, 17]             768\n",
            "      BasicConv2d-86          [-1, 384, 17, 17]               0\n",
            "           Conv2d-87           [-1, 64, 35, 35]          18,432\n",
            "      BatchNorm2d-88           [-1, 64, 35, 35]             128\n",
            "      BasicConv2d-89           [-1, 64, 35, 35]               0\n",
            "           Conv2d-90           [-1, 96, 35, 35]          55,296\n",
            "      BatchNorm2d-91           [-1, 96, 35, 35]             192\n",
            "      BasicConv2d-92           [-1, 96, 35, 35]               0\n",
            "           Conv2d-93           [-1, 96, 17, 17]          82,944\n",
            "      BatchNorm2d-94           [-1, 96, 17, 17]             192\n",
            "      BasicConv2d-95           [-1, 96, 17, 17]               0\n",
            "       InceptionB-96          [-1, 768, 17, 17]               0\n",
            "           Conv2d-97          [-1, 192, 17, 17]         147,456\n",
            "      BatchNorm2d-98          [-1, 192, 17, 17]             384\n",
            "      BasicConv2d-99          [-1, 192, 17, 17]               0\n",
            "          Conv2d-100          [-1, 128, 17, 17]          98,304\n",
            "     BatchNorm2d-101          [-1, 128, 17, 17]             256\n",
            "     BasicConv2d-102          [-1, 128, 17, 17]               0\n",
            "          Conv2d-103          [-1, 128, 17, 17]         114,688\n",
            "     BatchNorm2d-104          [-1, 128, 17, 17]             256\n",
            "     BasicConv2d-105          [-1, 128, 17, 17]               0\n",
            "          Conv2d-106          [-1, 192, 17, 17]         172,032\n",
            "     BatchNorm2d-107          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-108          [-1, 192, 17, 17]               0\n",
            "          Conv2d-109          [-1, 128, 17, 17]          98,304\n",
            "     BatchNorm2d-110          [-1, 128, 17, 17]             256\n",
            "     BasicConv2d-111          [-1, 128, 17, 17]               0\n",
            "          Conv2d-112          [-1, 128, 17, 17]         114,688\n",
            "     BatchNorm2d-113          [-1, 128, 17, 17]             256\n",
            "     BasicConv2d-114          [-1, 128, 17, 17]               0\n",
            "          Conv2d-115          [-1, 128, 17, 17]         114,688\n",
            "     BatchNorm2d-116          [-1, 128, 17, 17]             256\n",
            "     BasicConv2d-117          [-1, 128, 17, 17]               0\n",
            "          Conv2d-118          [-1, 128, 17, 17]         114,688\n",
            "     BatchNorm2d-119          [-1, 128, 17, 17]             256\n",
            "     BasicConv2d-120          [-1, 128, 17, 17]               0\n",
            "          Conv2d-121          [-1, 192, 17, 17]         172,032\n",
            "     BatchNorm2d-122          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-123          [-1, 192, 17, 17]               0\n",
            "          Conv2d-124          [-1, 192, 17, 17]         147,456\n",
            "     BatchNorm2d-125          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-126          [-1, 192, 17, 17]               0\n",
            "      InceptionC-127          [-1, 768, 17, 17]               0\n",
            "          Conv2d-128          [-1, 192, 17, 17]         147,456\n",
            "     BatchNorm2d-129          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-130          [-1, 192, 17, 17]               0\n",
            "          Conv2d-131          [-1, 160, 17, 17]         122,880\n",
            "     BatchNorm2d-132          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-133          [-1, 160, 17, 17]               0\n",
            "          Conv2d-134          [-1, 160, 17, 17]         179,200\n",
            "     BatchNorm2d-135          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-136          [-1, 160, 17, 17]               0\n",
            "          Conv2d-137          [-1, 192, 17, 17]         215,040\n",
            "     BatchNorm2d-138          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-139          [-1, 192, 17, 17]               0\n",
            "          Conv2d-140          [-1, 160, 17, 17]         122,880\n",
            "     BatchNorm2d-141          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-142          [-1, 160, 17, 17]               0\n",
            "          Conv2d-143          [-1, 160, 17, 17]         179,200\n",
            "     BatchNorm2d-144          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-145          [-1, 160, 17, 17]               0\n",
            "          Conv2d-146          [-1, 160, 17, 17]         179,200\n",
            "     BatchNorm2d-147          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-148          [-1, 160, 17, 17]               0\n",
            "          Conv2d-149          [-1, 160, 17, 17]         179,200\n",
            "     BatchNorm2d-150          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-151          [-1, 160, 17, 17]               0\n",
            "          Conv2d-152          [-1, 192, 17, 17]         215,040\n",
            "     BatchNorm2d-153          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-154          [-1, 192, 17, 17]               0\n",
            "          Conv2d-155          [-1, 192, 17, 17]         147,456\n",
            "     BatchNorm2d-156          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-157          [-1, 192, 17, 17]               0\n",
            "      InceptionC-158          [-1, 768, 17, 17]               0\n",
            "          Conv2d-159          [-1, 192, 17, 17]         147,456\n",
            "     BatchNorm2d-160          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-161          [-1, 192, 17, 17]               0\n",
            "          Conv2d-162          [-1, 160, 17, 17]         122,880\n",
            "     BatchNorm2d-163          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-164          [-1, 160, 17, 17]               0\n",
            "          Conv2d-165          [-1, 160, 17, 17]         179,200\n",
            "     BatchNorm2d-166          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-167          [-1, 160, 17, 17]               0\n",
            "          Conv2d-168          [-1, 192, 17, 17]         215,040\n",
            "     BatchNorm2d-169          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-170          [-1, 192, 17, 17]               0\n",
            "          Conv2d-171          [-1, 160, 17, 17]         122,880\n",
            "     BatchNorm2d-172          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-173          [-1, 160, 17, 17]               0\n",
            "          Conv2d-174          [-1, 160, 17, 17]         179,200\n",
            "     BatchNorm2d-175          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-176          [-1, 160, 17, 17]               0\n",
            "          Conv2d-177          [-1, 160, 17, 17]         179,200\n",
            "     BatchNorm2d-178          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-179          [-1, 160, 17, 17]               0\n",
            "          Conv2d-180          [-1, 160, 17, 17]         179,200\n",
            "     BatchNorm2d-181          [-1, 160, 17, 17]             320\n",
            "     BasicConv2d-182          [-1, 160, 17, 17]               0\n",
            "          Conv2d-183          [-1, 192, 17, 17]         215,040\n",
            "     BatchNorm2d-184          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-185          [-1, 192, 17, 17]               0\n",
            "          Conv2d-186          [-1, 192, 17, 17]         147,456\n",
            "     BatchNorm2d-187          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-188          [-1, 192, 17, 17]               0\n",
            "      InceptionC-189          [-1, 768, 17, 17]               0\n",
            "          Conv2d-190          [-1, 192, 17, 17]         147,456\n",
            "     BatchNorm2d-191          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-192          [-1, 192, 17, 17]               0\n",
            "          Conv2d-193          [-1, 192, 17, 17]         147,456\n",
            "     BatchNorm2d-194          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-195          [-1, 192, 17, 17]               0\n",
            "          Conv2d-196          [-1, 192, 17, 17]         258,048\n",
            "     BatchNorm2d-197          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-198          [-1, 192, 17, 17]               0\n",
            "          Conv2d-199          [-1, 192, 17, 17]         258,048\n",
            "     BatchNorm2d-200          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-201          [-1, 192, 17, 17]               0\n",
            "          Conv2d-202          [-1, 192, 17, 17]         147,456\n",
            "     BatchNorm2d-203          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-204          [-1, 192, 17, 17]               0\n",
            "          Conv2d-205          [-1, 192, 17, 17]         258,048\n",
            "     BatchNorm2d-206          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-207          [-1, 192, 17, 17]               0\n",
            "          Conv2d-208          [-1, 192, 17, 17]         258,048\n",
            "     BatchNorm2d-209          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-210          [-1, 192, 17, 17]               0\n",
            "          Conv2d-211          [-1, 192, 17, 17]         258,048\n",
            "     BatchNorm2d-212          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-213          [-1, 192, 17, 17]               0\n",
            "          Conv2d-214          [-1, 192, 17, 17]         258,048\n",
            "     BatchNorm2d-215          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-216          [-1, 192, 17, 17]               0\n",
            "          Conv2d-217          [-1, 192, 17, 17]         147,456\n",
            "     BatchNorm2d-218          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-219          [-1, 192, 17, 17]               0\n",
            "      InceptionC-220          [-1, 768, 17, 17]               0\n",
            "          Conv2d-221            [-1, 128, 5, 5]          98,304\n",
            "     BatchNorm2d-222            [-1, 128, 5, 5]             256\n",
            "     BasicConv2d-223            [-1, 128, 5, 5]               0\n",
            "          Conv2d-224            [-1, 768, 1, 1]       2,457,600\n",
            "     BatchNorm2d-225            [-1, 768, 1, 1]           1,536\n",
            "     BasicConv2d-226            [-1, 768, 1, 1]               0\n",
            "          Linear-227                    [-1, 4]           3,076\n",
            "    InceptionAux-228                    [-1, 4]               0\n",
            "          Conv2d-229          [-1, 192, 17, 17]         147,456\n",
            "     BatchNorm2d-230          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-231          [-1, 192, 17, 17]               0\n",
            "          Conv2d-232            [-1, 320, 8, 8]         552,960\n",
            "     BatchNorm2d-233            [-1, 320, 8, 8]             640\n",
            "     BasicConv2d-234            [-1, 320, 8, 8]               0\n",
            "          Conv2d-235          [-1, 192, 17, 17]         147,456\n",
            "     BatchNorm2d-236          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-237          [-1, 192, 17, 17]               0\n",
            "          Conv2d-238          [-1, 192, 17, 17]         258,048\n",
            "     BatchNorm2d-239          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-240          [-1, 192, 17, 17]               0\n",
            "          Conv2d-241          [-1, 192, 17, 17]         258,048\n",
            "     BatchNorm2d-242          [-1, 192, 17, 17]             384\n",
            "     BasicConv2d-243          [-1, 192, 17, 17]               0\n",
            "          Conv2d-244            [-1, 192, 8, 8]         331,776\n",
            "     BatchNorm2d-245            [-1, 192, 8, 8]             384\n",
            "     BasicConv2d-246            [-1, 192, 8, 8]               0\n",
            "      InceptionD-247           [-1, 1280, 8, 8]               0\n",
            "          Conv2d-248            [-1, 320, 8, 8]         409,600\n",
            "     BatchNorm2d-249            [-1, 320, 8, 8]             640\n",
            "     BasicConv2d-250            [-1, 320, 8, 8]               0\n",
            "          Conv2d-251            [-1, 384, 8, 8]         491,520\n",
            "     BatchNorm2d-252            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-253            [-1, 384, 8, 8]               0\n",
            "          Conv2d-254            [-1, 384, 8, 8]         442,368\n",
            "     BatchNorm2d-255            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-256            [-1, 384, 8, 8]               0\n",
            "          Conv2d-257            [-1, 384, 8, 8]         442,368\n",
            "     BatchNorm2d-258            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-259            [-1, 384, 8, 8]               0\n",
            "          Conv2d-260            [-1, 448, 8, 8]         573,440\n",
            "     BatchNorm2d-261            [-1, 448, 8, 8]             896\n",
            "     BasicConv2d-262            [-1, 448, 8, 8]               0\n",
            "          Conv2d-263            [-1, 384, 8, 8]       1,548,288\n",
            "     BatchNorm2d-264            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-265            [-1, 384, 8, 8]               0\n",
            "          Conv2d-266            [-1, 384, 8, 8]         442,368\n",
            "     BatchNorm2d-267            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-268            [-1, 384, 8, 8]               0\n",
            "          Conv2d-269            [-1, 384, 8, 8]         442,368\n",
            "     BatchNorm2d-270            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-271            [-1, 384, 8, 8]               0\n",
            "          Conv2d-272            [-1, 192, 8, 8]         245,760\n",
            "     BatchNorm2d-273            [-1, 192, 8, 8]             384\n",
            "     BasicConv2d-274            [-1, 192, 8, 8]               0\n",
            "      InceptionE-275           [-1, 2048, 8, 8]               0\n",
            "          Conv2d-276            [-1, 320, 8, 8]         655,360\n",
            "     BatchNorm2d-277            [-1, 320, 8, 8]             640\n",
            "     BasicConv2d-278            [-1, 320, 8, 8]               0\n",
            "          Conv2d-279            [-1, 384, 8, 8]         786,432\n",
            "     BatchNorm2d-280            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-281            [-1, 384, 8, 8]               0\n",
            "          Conv2d-282            [-1, 384, 8, 8]         442,368\n",
            "     BatchNorm2d-283            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-284            [-1, 384, 8, 8]               0\n",
            "          Conv2d-285            [-1, 384, 8, 8]         442,368\n",
            "     BatchNorm2d-286            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-287            [-1, 384, 8, 8]               0\n",
            "          Conv2d-288            [-1, 448, 8, 8]         917,504\n",
            "     BatchNorm2d-289            [-1, 448, 8, 8]             896\n",
            "     BasicConv2d-290            [-1, 448, 8, 8]               0\n",
            "          Conv2d-291            [-1, 384, 8, 8]       1,548,288\n",
            "     BatchNorm2d-292            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-293            [-1, 384, 8, 8]               0\n",
            "          Conv2d-294            [-1, 384, 8, 8]         442,368\n",
            "     BatchNorm2d-295            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-296            [-1, 384, 8, 8]               0\n",
            "          Conv2d-297            [-1, 384, 8, 8]         442,368\n",
            "     BatchNorm2d-298            [-1, 384, 8, 8]             768\n",
            "     BasicConv2d-299            [-1, 384, 8, 8]               0\n",
            "          Conv2d-300            [-1, 192, 8, 8]         393,216\n",
            "     BatchNorm2d-301            [-1, 192, 8, 8]             384\n",
            "     BasicConv2d-302            [-1, 192, 8, 8]               0\n",
            "      InceptionE-303           [-1, 2048, 8, 8]               0\n",
            "AdaptiveAvgPool2d-304           [-1, 2048, 1, 1]               0\n",
            "         Dropout-305           [-1, 2048, 1, 1]               0\n",
            "          Linear-306                    [-1, 4]           8,196\n",
            "================================================================\n",
            "Total params: 24,354,536\n",
            "Trainable params: 11,272\n",
            "Non-trainable params: 24,343,264\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 1.02\n",
            "Forward/backward pass size (MB): 228.64\n",
            "Params size (MB): 92.91\n",
            "Estimated Total Size (MB): 322.56\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#9. Loss Function dan Optimizer\n",
        "- Mendefinisikan fungsi loss (Cross-Entropy) dan optimizer (Adam).\n",
        "- Membuat penjadwalan laju pembelajaran (learning rate scheduler)."
      ],
      "metadata": {
        "id": "OALTxCBjOUby"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define loss function and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "Ff-BjzooNbtL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a learning rate scheduler\n",
        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)\n"
      ],
      "metadata": {
        "id": "htNjeHSuNdm4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#10. Training Model(Loop)\n",
        "- Melakukan pelatihan model selama beberapa epoch.\n",
        "- Menghitung loss dan akurasi pada set pelatihan dan validasi.\n",
        "- Menyimpan loss dan akurasi pada setiap epoch."
      ],
      "metadata": {
        "id": "_W-Z9fzwOg7K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training loop\n",
        "num_epochs = 15\n",
        "train_losses = []\n",
        "valid_losses = []\n",
        "train_accuracies = []\n",
        "valid_accuracies = []"
      ],
      "metadata": {
        "id": "vXzvVeowQ-Ci"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training loop\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    train_loss = 0.0\n",
        "    correct_train = 0\n",
        "    total_train = 0\n",
        "\n",
        "    for inputs, labels in train_loader:\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        predicted = outputs.logits  # Ambil output kelas dari logits\n",
        "        loss = criterion(predicted, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        train_loss += loss.item() * inputs.size(0)\n",
        "        _, predicted_class = torch.max(predicted, 1)  # Peroleh kelas prediksi\n",
        "        total_train += labels.size(0)\n",
        "        correct_train += (predicted_class == labels).sum().item()\n",
        "\n",
        "    train_loss = train_loss / len(train_loader.dataset)\n",
        "    train_accuracy = 100 * correct_train / total_train\n",
        "    train_losses.append(train_loss)\n",
        "    train_accuracies.append(train_accuracy)\n",
        "\n",
        "    model.eval()\n",
        "    valid_loss = 0.0\n",
        "    correct_valid = 0\n",
        "    total_valid = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in valid_loader:\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            predicted = outputs  # Ambil output kelas dari logits\n",
        "            loss = criterion(predicted, labels)\n",
        "            valid_loss += loss.item() * inputs.size(0)\n",
        "            _, predicted_class = torch.max(predicted, 1)  # Peroleh kelas prediksi\n",
        "            total_valid += labels.size(0)\n",
        "            correct_valid += (predicted_class == labels).sum().item()\n",
        "\n",
        "    valid_loss = valid_loss / len(valid_loader.dataset)\n",
        "    valid_accuracy = 100 * correct_valid / total_valid\n",
        "    valid_losses.append(valid_loss)\n",
        "    valid_accuracies.append(valid_accuracy)\n",
        "\n",
        "    print(f'Epoch [{epoch + 1}/{num_epochs}] => '\n",
        "          f'Train Loss: {train_loss:.4f}, Valid Loss: {valid_loss:.4f}, '\n",
        "          f'Train Accuracy: {train_accuracy:.2f}%, Valid Accuracy: {valid_accuracy:.2f}%')\n",
        "\n",
        "    scheduler.step()"
      ],
      "metadata": {
        "id": "F_ps7JwjNhp9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8b00eb9-ce3e-4347-fe63-9ecd33db818c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/15] => Train Loss: 1.2175, Valid Loss: 1.0221, Train Accuracy: 50.63%, Valid Accuracy: 61.58%\n",
            "Epoch [2/15] => Train Loss: 1.0391, Valid Loss: 0.9381, Train Accuracy: 57.81%, Valid Accuracy: 67.23%\n",
            "Epoch [3/15] => Train Loss: 0.9343, Valid Loss: 0.9155, Train Accuracy: 61.60%, Valid Accuracy: 67.23%\n",
            "Epoch [4/15] => Train Loss: 0.8959, Valid Loss: 0.8352, Train Accuracy: 65.40%, Valid Accuracy: 71.19%\n",
            "Epoch [5/15] => Train Loss: 0.8321, Valid Loss: 0.7895, Train Accuracy: 66.39%, Valid Accuracy: 72.88%\n",
            "Epoch [6/15] => Train Loss: 0.8279, Valid Loss: 0.7909, Train Accuracy: 67.37%, Valid Accuracy: 71.75%\n",
            "Epoch [7/15] => Train Loss: 0.7717, Valid Loss: 0.7735, Train Accuracy: 70.89%, Valid Accuracy: 71.75%\n",
            "Epoch [8/15] => Train Loss: 0.7877, Valid Loss: 0.7672, Train Accuracy: 69.06%, Valid Accuracy: 68.93%\n",
            "Epoch [9/15] => Train Loss: 0.7841, Valid Loss: 0.7721, Train Accuracy: 69.34%, Valid Accuracy: 71.75%\n",
            "Epoch [10/15] => Train Loss: 0.7664, Valid Loss: 0.7475, Train Accuracy: 70.60%, Valid Accuracy: 72.88%\n",
            "Epoch [11/15] => Train Loss: 0.7479, Valid Loss: 0.7489, Train Accuracy: 71.31%, Valid Accuracy: 72.88%\n",
            "Epoch [12/15] => Train Loss: 0.7219, Valid Loss: 0.7476, Train Accuracy: 72.29%, Valid Accuracy: 71.75%\n",
            "Epoch [13/15] => Train Loss: 0.7496, Valid Loss: 0.7602, Train Accuracy: 69.48%, Valid Accuracy: 70.06%\n",
            "Epoch [14/15] => Train Loss: 0.7692, Valid Loss: 0.7298, Train Accuracy: 69.76%, Valid Accuracy: 72.88%\n",
            "Epoch [15/15] => Train Loss: 0.7414, Valid Loss: 0.7278, Train Accuracy: 72.15%, Valid Accuracy: 71.75%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#11. Grafik Loss dan Akurasi\n",
        "- Menampilkan grafik loss dan akurasi pelatihan dan validasi.\n"
      ],
      "metadata": {
        "id": "utO5asabOrSw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the training and validation loss\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(range(1, num_epochs + 1), train_losses, label='Train Loss')\n",
        "plt.plot(range(1, num_epochs + 1), valid_losses, label='Valid Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Plot the training and validation accuracy\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(range(1, num_epochs + 1), train_accuracies, label='Train Accuracy')\n",
        "plt.plot(range(1, num_epochs + 1), valid_accuracies, label='Valid Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "print(\"Training completed.\")"
      ],
      "metadata": {
        "id": "BPFFXovtRBdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "outputId": "e7119a31-0a5d-4077-8eae-193472f7f280"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setelah bagian pelatihan selesai\n",
        "test_dataset = torchvision.datasets.ImageFolder(root=data_dir, transform=transform)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)  # Use shuffle=False for testing\n",
        "\n",
        "# Hitung akurasi pada dataset pengujian\n",
        "test_correct = 0\n",
        "test_total = 0\n",
        "model.eval()  # Set model ke mode evaluasi\n",
        "\n",
        "with torch.no_grad():\n",
        "    for inputs, labels in test_loader:\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        outputs = model(inputs)\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        test_total += labels.size(0)\n",
        "        test_correct += (predicted == labels).sum().item()\n",
        "\n",
        "test_accuracy = 100 * test_correct / test_total\n",
        "print(f'Accuracy on test data: {test_accuracy:.2f}%')"
      ],
      "metadata": {
        "id": "1yMhYYQUP9uo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9df5e794-dae4-40f8-b030-e68ac36cdcd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test data: 63.40%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zc_ppA_VNnjx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}