Created using Colab

notebooks/01_model_training_and_fine_tuning.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "authorship_tag": "ABX9TyM5Fah77XuALjsYEAXaCFqE",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/mobadara/precision-diagnostics-xai/blob/main/notebooks/01_model_training_and_fine_tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Model Training and Fine-Tuning**\n",
+        "\n",
+        "**Objective:** Ingest the Kaggle Chest X-Ray dataset, apply strategic data augmentation, handle severe class imbalance via loss weighting, and fine-tune a pre-trained DenseNet121 architecture to detect pneumonia.\n",
+        "\n",
+        "## **Environment Setup**\n",
+        "First, we import the necessary PyTorch libraries and configure our hardware accelerator. Because image processing is computationally expensive, we must ensure PyTorch detects and utilizes the Colab GPU (CUDA)."
+      ],
+      "metadata": {
+        "id": "24IiOoFPN2oN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IGGkAlCRNlro",
+        "outputId": "919ad755-854c-49b1-a812-43d413128bb3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Compute Device: cuda\n",
+            "GPU Model: Tesla T4\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "from torchvision import datasets, transforms, models\n",
+        "from torch.utils.data import DataLoader\n",
+        "import os\n",
+        "import numpy as np\n",
+        "\n",
+        "# Configure the device\n",
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+        "print(f'Compute Device: {device}')\n",
+        "if device.type == 'cuda':\n",
+        "    print(f'GPU Model: {torch.cuda.get_device_name(0)}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **Data Preprocessing and Augmentation Pipeline**\n",
+        "As determined in our EDA, we must standardize all input resolutions to 224x224.\n",
+        "\n",
+        "To prevent overfitting without introducing artificial artifacts, we apply light data augmentation **only to the training set** (rotation and color jitter). The validation and test sets remain strictly unaltered to provide an honest evaluation metric."
+      ],
+      "metadata": {
+        "id": "2SGyO1PxOmDX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the dataset paths\n",
+        "data_dir = '../dataset/chest_xray'\n",
+        "train_dir = os.path.join(data_dir, 'train')\n",
+        "val_dir = os.path.join(data_dir, 'val')\n",
+        "test_dir = os.path.join(data_dir, 'test')\n",
+        "\n",
+        "# 1. Training Transforms (with Augmentation)\n",
+        "train_transforms = transforms.Compose([\n",
+        "    transforms.Resize((224, 224)),\n",
+        "    transforms.RandomRotation(10), # Slight rotation to simulate patient misalignment\n",
+        "    transforms.ColorJitter(brightness=0.2, contrast=0.2), # Account for different X-ray exposures\n",
+        "    transforms.ToTensor(),\n",
+        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Standard ImageNet normalization\n",
+        "])\n",
+        "\n",
+        "# 2. Validation/Testing Transforms (Strictly unaltered)\n",
+        "eval_transforms = transforms.Compose([\n",
+        "    transforms.Resize((224, 224)),\n",
+        "    transforms.ToTensor(),\n",
+        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+        "])\n",
+        "\n",
+        "print(\"Transforms configured successfully.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ikceEotXOU_1",
+        "outputId": "367466cb-bd4f-4de8-b8c6-1142abda4b49"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Transforms configured successfully.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "I6obJOa1QYMy"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}