{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "456eacd4-cf8e-4972-ac88-446224bafb89",
   "metadata": {},
   "source": [
    "# Lecture 9: Introduction to Deep Learning for Image Analysis\n",
    "**Introduction to Image Analysis — Spring 2026** \n",
    "\n",
    "---\n",
    "\n",
    "The following code uses PyTorch. Install it in your Python environment by running\n",
    "```bash\n",
    "pip install scikit-learn torch torchvision\n",
    "```\n",
    "from a terminal.\n",
    "\n",
    "We will also use MedMNIST for the examples. Install the required package with\n",
    "```bash\n",
    "pip install medmnist\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a534817a-d8af-487f-a6a3-8ae87667446e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import torch as th\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.transforms.functional import pil_to_tensor\n",
    "from torchvision.utils import make_grid\n",
    "from torchvision import transforms\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f43440-24e8-4e82-a385-52d4ebbdc49f",
   "metadata": {},
   "source": [
    "## 0. PyTorch quick introduction\n",
    "\n",
    "PyTorch is a Python library for numerical computing and deep learning.\n",
    "\n",
    "When you work with data, PyTorch feels very similar to NumPy. The main data structure is a **tensor** (similar to a NumPy array). Many operations use almost the same syntax, so if you know NumPy, learning PyTorch is straightforward.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da0de633-a363-4170-b5cf-7ef2f771b37a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy array:\n",
      " [[1. 2.]\n",
      " [3. 4.]]\n",
      "PyTorch tensor:\n",
      " tensor([[1., 2.],\n",
      "        [3., 4.]])\n",
      "NumPy shape: (2, 2)\n",
      "PyTorch shape: torch.Size([2, 2])\n",
      "NumPy mean: 2.5\n",
      "PyTorch mean: tensor(2.5000)\n",
      "NumPy matrix product:\n",
      " [[ 7. 10.]\n",
      " [15. 22.]]\n",
      "PyTorch matrix product:\n",
      " tensor([[ 7., 10.],\n",
      "        [15., 22.]])\n"
     ]
    }
   ],
   "source": [
    "# NumPy and PyTorch feel very similar for basic array operations\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "a_np = np.array([[1.0, 2.0], [3.0, 4.0]])\n",
    "a_torch = torch.tensor([[1.0, 2.0], [3.0, 4.0]])\n",
    "\n",
    "print('NumPy array:\\n', a_np)\n",
    "print('PyTorch tensor:\\n', a_torch)\n",
    "\n",
    "# Shape\n",
    "print('NumPy shape:', a_np.shape)\n",
    "print('PyTorch shape:', a_torch.shape)\n",
    "\n",
    "print('NumPy mean:', a_np.mean())\n",
    "print('PyTorch mean:', a_torch.mean())\n",
    "\n",
    "print('NumPy matrix product:\\n', a_np @ a_np)\n",
    "print('PyTorch matrix product:\\n', a_torch @ a_torch)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd6acb29-5730-4570-b2e3-12e130cc38cd",
   "metadata": {},
   "source": [
    "PyTorch has two key features that NumPy does not provide out of the box:\n",
    "\n",
    "1. **Automatic gradients (autograd):** PyTorch can compute derivatives automatically, which is how neural networks learn during training.\n",
    "2. **GPU support:** PyTorch can run tensor operations on a GPU, which can make deep learning much faster.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ad427a-da0d-498e-a44b-3f985dbabc95",
   "metadata": {},
   "source": [
    "### Automatic gradients\n",
    "\n",
    "PyTorch can compute derivatives automatically with **autograd**.\n",
    "If a tensor has `requires_grad=True`, PyTorch tracks operations on it and can compute gradients with `.backward()`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "579189e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = 3.0\n",
      "y = 15.0\n",
      "dy/dx = 8.0\n"
     ]
    }
   ],
   "source": [
    "# Very first autograd example (scalar input)\n",
    "import torch\n",
    "\n",
    "x = torch.tensor(3.0, requires_grad=True)\n",
    "y = x**2 + 2*x\n",
    "\n",
    "y.backward()\n",
    "\n",
    "print('x =', x.item())\n",
    "print('y =', y.item())\n",
    "print('dy/dx =', x.grad.item())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c95ee64",
   "metadata": {},
   "source": [
    "Autograd works with input tensors of any shape (scalar, vector, matrix, etc.).\n",
    "You can combine many tensors to compute a final value.\n",
    "PyTorch computes gradients of that final value with respect to all inputs that have `requires_grad=True`.\n",
    "That final value must be a scalar.\n",
    "\n",
    "For example, we can compute the gradient of $f(\\mathbf{w}) = \\sum_i \\mathbf{w}_i^2$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f5403ac-7944-431c-800b-2e1fc3c57fac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss.shape = torch.Size([])\n",
      "w = tensor([1., 2., 3.], requires_grad=True)\n",
      "loss = 14.0\n",
      "gradients d(loss)/dw = tensor([2., 4., 6.])\n"
     ]
    }
   ],
   "source": [
    "# Autograd with a vector: squared norm ||w||^2\n",
    "import torch\n",
    "\n",
    "w = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)\n",
    "\n",
    "# Scalar output: sum of squares\n",
    "loss = (w**2).sum()\n",
    "\n",
    "# loss is a single number (a scalar, its shape is []):\n",
    "print(\"loss.shape =\", loss.shape)\n",
    "loss.backward()\n",
    "\n",
    "print('w =', w)\n",
    "print('loss =', loss.item())\n",
    "print('gradients d(loss)/dw =', w.grad)  # expected: 2*w\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4efd797-28e5-4fed-88f3-a29ca9873705",
   "metadata": {},
   "source": [
    "### GPU computation\n",
    "\n",
    "PyTorch can move tensors and models to GPU to speed up computations.\n",
    "In this lecture, we will keep everything on CPU to keep the code simple.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75b60527-9418-4941-8023-7b643d0ed1ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected device: cpu\n",
      "Tensor: tensor([1., 2., 3.])\n",
      "Tensor device: cpu\n"
     ]
    }
   ],
   "source": [
    "# Move a tensor to GPU (if available)\n",
    "import torch\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "x = torch.tensor([1.0, 2.0, 3.0]).to(device)\n",
    "\n",
    "print('Selected device:', device)\n",
    "print('Tensor:', x)\n",
    "print('Tensor device:', x.device)\n",
    "\n",
    "# By default, PyTorch tensors are created on the CPU unless you explicitly set a device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9c3e497-858c-42d4-9a64-c02a0b7ce83c",
   "metadata": {},
   "source": [
    "Useful PyTorch resources:\n",
    "\n",
    "- PyTorch tutorials: https://docs.pytorch.org/tutorials/\n",
    "- Intro to PyTorch: https://docs.pytorch.org/tutorials/beginner/basics/intro.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6000034-0b6c-40d5-a495-eba08b18b89f",
   "metadata": {},
   "source": [
    "## 1. Load image\n",
    "\n",
    "We start by loading one image that we will use to explore convolution layers and build intuition.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f06717e-01a5-489d-962d-42b76e290709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCACAAIABAREA/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/9oACAEBAAA/APFlyqg/L1wTjj8KcoJIK88Y6UwlO4POeMdaVF27QWIPT/8AVSgOTnockexFBYqwD7cKcAAUzZkqSBjGeatIWX5lRcLyQQM/55okZnXHl8nsMVW27TuX8iKk5CZ7Hj6UjKIz8p+9yWIGaapGcgEhOOD0pxCjIIOcdc5pu4Y+6G4zxSll5+XpggHimlR1zn3oBYEBs4H8IFBfaMck9h3FIZAxBKjJ7AU4N0G7j1z1FI2SSTyeg9qPkHdTkdxinBiFO0KxznnqaVmIO0/IDn3zSFs5Y9AOD1z25p4cBuuR1wByaYsm8sSq4HSnK2WIPQ9fUigEkZ5O4YA6U1m2HGNxI5/+tS8bwpOTjge/pUZ3YwecHnnjH1p2TuG3gnnIPSkJUyehOD6c/WkGRIMPn3FK0gcg9OeTTndJTkDBOMn1qMLtIG7C9sU/kcHHtuphbk89OM+uaPvMQ4yT+FPXCYOTyOADzTNwIOQM+/bmnbsqxOG9wc0oPCZ5wCfpTQz5HGADzS87t23HX8KVVyfmzgg8+tNYkJwAAPbmlO0qeCVAzTVIJyM46HPYUgKhgFBPOKkVsDJyCRgYx/ntTSxHLcAHjjj6UYB5OST/AA0MqhsDhR3PejePLPHGOBijhCmcfXvQCCOTuBHXvSsBnI/nTTjaQTnb1JpV45PJ7jpQGK7WBxjkjHU00fw5556D0pwDH5QSB79KcylkOMtTCCoPy5GMUq9Bjt1HoaNp3DaDwOBzSEY65PXFKSrYxz+JpqfMSx78cnpQxIIJJ57kUKQGOT1OMGl5U5/EjFIWBJBGOMkAdafuBB5HPOaarbWK8lfegNtPytn8KXdkjbwMdOmaTbnAPSlLHOCCDj8KaNwPPGP1pQdxBOck8nNJnDcHmkPU46Y6n0pyHj5Vx2xSbs5QjJ+lIpBY5P60/IGSAM4pCSDknAAxkUhbagBOc9QecUpYbjySB0oJ3YzyBx70AAsu4Hgc81s6f4W1O/fCQNEoxky8DFb9l4MsYMfbrppPVY+M1tQ6Po1qw8vSo5D0DSknNSNbaaXRn0qzcA4I8sdfwrO1Dwzpd0+6OFrRznBQnH5GuP1LQbrTXJCiWI/xqOn1HasluTkA8dDSDHlgDgngmlByCVAzjk96OSMYOe3FA4zz1pRjkgDdTGzu3YxkdKU5PzAA8+lHIVSWyT2604EkEHvXqEt4oZmknwWOCBUDXoxuj7ActWpaaRqV/GlwYvLgYgK0hxn6Cqt/o2pWc8ivbZQHcpBwWHYgVmNqDxyOknnQjPQjvVlbxJozuAdMYJTnA965jWtBCyfaLBPMjPWNeoPtXNHuOhHUkd6VcDkMOvJpevKn1703IHHXGOehp244Az1Pemg4A9B0NKCepxwelK/A6Aj2FNyCd2eMetek2Wlz3zZggZ1/vEHANdLofh+xsrhrzW7qNljUmOBDnLeh7YrI1XxTNqF0Y94hSH7qxjAq3Y+OpfIaKWX5+iZAIXjrzVqW+0vxUstmyFZlXdHJGOGfHOR71wFwHtZv3ZK/jUkV982ZCVxzuWqFzY2+oXUkiv5bsc9OGPrUCeGbhwTFPEQOe4NSDwrc7SDNCM9Dg1IPCFyThbu3J9wR/Sq03hfUoSQEjlUD+B8msqa2nt/lmgeNj/eWocljt5+vbNLzkE/maXA2knHFek3Hii7dTFG4hh/uRjaKwrjWZdxPmkenNUPNe4Lu57ZqpDeBJAWOMGtvS9SNleRTRnqwZvzrY1238+Vb0RpFb3JJUryARjNYQhZGYYyoPUdDUYIVi3KkdCauQXALAE7Ce46Grgu3BPAZe1Si8RmyTjHYjipFmV2IRkzmhpI34lRXBGDkZrOl8PaddklC0D46r0/KsybwjeR5e2dJk5IHQ4rNuNFv7bl7Z+xOBn+VbDzZYA+npWXfSqDjbRG5cNnkY9KoZDswzgk1MkjRsCGyAK7DRdRS9tDaXIVk2nbnqDWdJOlrK6MCT9Kk86GUKilSSO4qBoyWVU6k8CnrLOny7en61agkJQs0YyPSnhA0fMb8d8Uw70GUckehFKt6VADqQfVakjvkDZVmQjpVqLUZOCZFf/eFcqdwbjGMetZ924J7kg06K4C7vmPTioQ4IPr16UplBTGBwOorQ0+5EBDIQDnPFb/9p2ky/vrdWJ5zVpb7TiQfIQjggYxinSW1nfkPbOIG7qT0PtVaXRpUHykt15Xnir0T2+mQoHj8yYjLcDp2BqeLW7NBse3RQRjBp11b2l9Ev2IOWBO1euPX9ayLuyubfO6FgR1OKzwW3YKL78GptvmAssRI9q5/zjkVVnfceTjmmIuc8800KxI55zT8fKCSfepouBnPGauKx45+lSRvtyc80+O62kDJqf7bKCNsjD6Gj+0ZtjZOQcZJqH7YrMGYVcttR8p8h2UHsprUGtq8RWXJXuTzUJGl3Ee3fKpJ6nvTFh05Y9qyzK2fvFuK45pDgAHtgVG8mT8w6dKVDjOBSZOcHIpxOBz6UI5AOO1WhKeKVJD3pwkG6nb/AJhzR5mVamEjgZqSN/m654qwHBTNKJNoGD+FOE7OfmJFYGflpG68UvQe9IB2pxIJ+vrQDjknOP0p+8qBijzODnI5pysQBnjnNO80E9TnvSiQbWyeOKYXB9Qc06OUAk56ip1mGypEmUgHPFHnKCSTxWSGwOTRkDGaN3y0bumaC3X60F+3alL4NIG75/ClD9TnNJvz7Uu/GQMUm/p60okPr1HNO833ApySgDPejzsnkiv/2Q==",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIAAAACACAAAAADmVT4XAAA/mklEQVR4AWLk/8/C/u8H9//fDEyMf/7zf//N9JuF6/MPlr/cHEyfmfm+sv5m/cbExvrzHxMz869PvCw/mRj+/P7JysL5m/2ryDs+zj9/GH+xMH399Yf1NwPrL4FfLH85GH8xsH/m+PaDiZ3rG+8bfqafDP8Z+P59YvzNwvZZ8O/f//8Zf7F952BiYvrz/7sgC98vRkY2VpZ//7j//v3P9YeV+Sszx1cG/l8Mv/7+YWRhZGf8wcTxifUXw39ORva/bGzMP36wMjFwcf77z8vKw8PIx/qb8Tfjrw/MXD+ZWRj+8fF8/87AyPCPg4GJgYmFRZDx53+2f0w//jLx/vrMzMLMzMr+ie8v429+nk8C//8yMv3/x/qHkYeV7Q8LB/P33/9Z/vwT+sby/z/zr288bL9+/uH8xvv7KwcLy/9f39h4/zIy/P/1R+Afw/f/nH++sTMysjH9Zv/7l53tN+N31v//mX5+ZWBiYvjN85v9JwMjAzfj+38/eVk4PjAws/5j/cnIzP7vNxPLTyb+d9+YeZh+cPz9z/6H7Qsrx3dGJqbf/7kE/79++4eNgfP//y+/fvz9/Z2V+zvjv89srFxMv4W5//34zs3C+Ofbr9+ff/z9+eU/y58/DL+/sv77/4/p899fPxlY/nEzMjP9YhTkYP33m+nH7//s/1j+/P7KLsTDw/iOlYOJ6ScTEzPbH2YOxj88zD84BTh+/GH/zcjy+zcL26fvjKyMLOwcv379Z2Fm+yPwi+MnK8u///8ZmX+zMTJx/+L7xcTJyPLjFzM7/89f7H/+MLMycbL/+8P6jY2V4yf7f+a/HN9ZGT/+Yhb6xcj8jfkf23+WX0wsX/6w/PvP//svKyfbX44v7D9ZGLk+/Wdl+s75l+PrP5Z/DEy/mX/9YWRk/cfxgZ2V9febX4ySzD/+cf1mZf79j4XpKxsz469/7H9+sf39JfKOifU/IxPzn4//eHnfs7MwfWXl+PL/L99XNoavHMzMP37x/BT9+ZP5K/u//4zMDN9Z/jFw/2H/xP7rLwcb+zvm/6xMzP++f+f8zfKXgfPfN7Z/zKz///zg5P71h4nl9y/eHz9/gSKVl/n7ZxaG78ycLN9+M/3jYPzD8I+JmfHn/98Mv/79+sTAzPL7L9eX/3xs37+ysjNwfv33j5H159dfvAx/GRj+cPL+4fzFKPqBle83478f/7hZf3z7wfab8Scz0/9fHOxs75n/8HD8ZfnP9ZPj21c2sS9//nKx/2b9/Jfrz38GZoavbP85mP7/+Puf5Q8ry+9fnEyMzAz/GX6xsIp+ZuL8xcTz9/1vXg4GDpYvf/9+4/jD9J+N6e8v1v8c33/9+s7KyPaH9R8Dx5d/jMz//rL+5Pv3SejbH76/HN8ZeZm+MHH//sP2h+U31/e//1mZPrN84v7AwPr7D8sXkY8//3/7zv+TnZHhFxPz//9sjAz/2Zj/Mf9i/cnCwvjv188fXIwMf7iYf/3kesvAycjAysDA8ffnNxaWH99+sf/l+MLM++cLI+dPkOH/Gb5/Zf79g+0r+4e//H9/sv1j/Pb3F/v3/1/Z2L784/n57z8741c2XgZ+1t/f//1n/8HM85f79y/m/79YmZn/feXk/s3M/4+B9d8vxj9Mf7i///rD9p/pH+PfL8y8DKys7P9Yvvz8z/L33z8mPjbGL6xsHH8YWAQYvv36zcL07TPzt//c/3+zMLMwMTOwcv//x/2flZ2DjeU/+593P5n/sXz/wfSFg+PvV2a2H785uf+zsPxlYWH9wcHD/vv/TwYW1h//fnxhYPrL9Ofzr/+/mX+x8rL/Zvj/7Q8THzs3IzfH/3/MTIx/GRn+cHz7w8X96ysDCwsbwxeR3yzf/v7gZX/HzvSf6QsL15+/v5j/8fz+xMP8m4H7D/f7/6zMrP+4vjEz/+T+/vfv7/9M3Ay//vzmYfnBxPqNkekf76+/TMz/2H+wf/3P8v8f6y/2X3+YfnIwsDF94fjFwMbO8PMvxydmRsZfrIxSv7h+/mBj/8bI8vsXMzsHy7cfbAwMfL/fM7H/YvvL8puN9Tsr6//vrAy/2b5y/GNl/PH/D9s/ZhbGn/9Yf/J+ZfzLzP2H8dc3Bk7uP6w/f3Aw/fjPxfiV5Tc3B9v3Tz9Aof2H7Ssb46+/HNxfmL5z/Odh+PaT6/8vzh8/uf/9/cfIzMj+k4X1z08mTpbfDD9ZWX/x/v//kevvPyaGz3/+MTIyM7D/Z/3O+I3tx/9/jFysnxn+Mn7h/Cf069/PP/9+s3D85vrCwPgTpECQhe0f47+Pvxl4WP79ZGTiZGH9z/L962/mr//5GX+wsjH8ZGFjYmBg5mD68+PPDwYWhp9vmVi+/udn+MDKzvqV5bfgtx/Mf1mZ/v5m4GT4wcj8mYeB4etP7v9/GH4z/Gb4x8bGzvabk+kP0wdm1r9M/379/sLxm4vlG98vll//+P/+Z/zO9pPz92duwY/MLByfv4iwMf1n+svE8v37p/8sDCy/WVh+/vrN+J+R5Rvjn19sXF/+/2X///c/6/9/zD+Ef/IKfP7/nYHlz1tGzh9sXxl4GH/++cbyn435F8tXJib2r///Mv1l/sH+8x8zE8tvpl+MLMzM7D9YWNm/Mf5/x8L8h/EL8//fv1m52Fj/M//gZPjw5/9fTp4/Xz//5f7/699fpp9MLD8ZWBh/ff37h//Pr1/srH9+/xf49v8PAwfzzx+M3zlAGf/7n19szExM//6x/Gb/y8HM9Iudk4ePi5OX+dNfDoZvAux/frH8YfjH8+vXnx8//v3/yfn7+28GFqaf//6xsDD8/vrtL8+f9wz//zIz/WPh+vvr99+///4xM7Ezc//7yfn3/z921h+/mX4wM/3i4fj35zf7Z8b/bBxszP/+cTB8AxWSjH+4mRi+/Wb98luAhYnru+Dnf//+//3D9usfI9c/hp////3h+MvM+4X3JxsHIz/Pe/b/jH+Y2Fj4PnxjYfv1/xcTIyPrb+7/f/9xsTF9Y2Bn+Pvz/19ulu88f3/+Zfjy/zMH++//jDz/ORl/MDD8YPzzUeAXM9e//z8Z/7F9ZGMAZYnfjCxsnL9Yf/1lZvjJ/OcT0z+m/++Zf39n/cvy+x/zv7f//rPxsXMwfPvG8PMPE+O/v+///GFmYf7PwML+F5QaGBhZGX+x/v//48+/T38YWDiZ+X/9/83A8ZeR8TcDw7+//zgZ/3z5zcH49fsfZlZWbvb/nEzf/v7+yyDI+ovpJyjtM7D9/sf8H6T3P+NfZlZuln9M/74x/P3Jwvyfi/sPG+9/lm//Wf6z/mLmY/7MxPz526+/v39zMTAxcf/9+pODi5v1519WdqYfP3+ygFIP44//jGzvfv9n+/v195d/v37++8PCzsLFI/DrJyv7z19//zAx/OFhZWHi+fn/719Gtv/ffzCxcv7/zcTy/z8n+9+/3Izsf/6xcPxjYf3OwvaNk4HlO8v//98Y/zH8ZmH4+4/rL8sfDmZW1j9MzH9ZGBi//eFk+8H3h5mN9wfTf9bfvxnYmPh+/f3/7xvnD6b/f/4ys7D9/sfwhZmDnfkP+38O3t8MDBJ/v/9l/sLK8ebvb0Yujt8//v//x8308983Ng6G378ZfzH/+/+H4Rfjvw9sDCxf/rMw8377ysbxmYPhByP7/7//uP/8+vWDnfXvXwYWBjbWryxsDL/+/eT894npCzPbPwamf2z/WP6CqudfbL/+//vP8/snAyvrn3/sf1hYfvxj/P//3z9mxr/fvzEyfWH69+kPB+MXlv9MTH9ZP/P8Y2X4z/L173f2Pyy/GZk//uX9y8D4k5eJj4uJ6TsjB+NXDuY/jEyMIP/+5+Jm4eTmYuRk+/vv5z/Gb7/ZeDl/f/775zfzdw5WLkYGZjYGJoafnExsnEycfP/+//nF/AeUdxm+MzDzs/9m4eL/w/jp38//rDx/fzCzCHKx//nD8O0zy59/v34w/GT98+c3B/c/tj9/2P5++cPF/JOJl/Pfu/983IysLCzMv0F17N8vP758ZGL7/fPv369Mf5lAJTwT4/9PDD++//3//++vH5wM//79+fyb4ycj0x9Opv+Mv/6yMoKazEwsbH8YP/5iZ2L68u8vNyv7719fGFmZ/n75x8zEzMXEwsLIxsLwl+UzCy/rDyYOzn9cLH/4Wb6xMrC8ZWFn5vr9/xvnV/b/zEzsH5nYGP9//cXw/icTK9Off3z/GX7/5/nznYHlH8dvlt9/mZl//eT59p+R5R8D2///DB9Yf7Ny/Wb++/s3M8/v34x/f7Ox/fzG+fMPE6goZfrFzvj7x3/G3+xsX5iYmX/++8vCwvmZ9f8vtj/s//7yvmdiZWD4/5OJ/Q/T/1//GFn/sjEysf7/9o/x328Gfhbmn2ysDL9Zuf/+/PuP+8cvViY2lr+MzIxCnKw/mTiY//5nYvjK/v8LMwvDn+9f2f78+8/5/9M3Ji4Wjj/f/jH+YGUA+Zqb4z8bMyszPy/j75+/GRlYWf4w/P/37j8bqJxhZPz/neHfP6Y/HCJMzKw/fvz6xcj8+y+zIDvrT9bfjPwMjP/+Mf1m//n7NwPHj7+8f3//Z2H+x8HB9fsvy9f/QgzvWFiZWVg5Qe1npj9sLMwc7D9Zmdj+c7P+5WNlYvrzj4n5HxsP49cfzDw/GNkZ//3m/PfjDzPHl5+g2o+Z8RsTMwPL73+cv5i5v/9g+Paf6d2ff0L/GNl/s3N//8Ty789v1p+f2f/yM3Fx/frPx/HvLxf3F2ZWnl+MTN9//GL+94Hx/d+fX3/+Zfnxm/XrfwaW36x8rHx/GNl/f2ViY/j96wcj679/TP/ZuFiY3//59+MvNzPLHxbG/8ycjP9+/GL+ycT56x/fX4Z/f36wMf9lYP7H/OfL769MvP/ZfnL9ZRRm/sH+5fNXln9c3Fw/frDysDKxs/3/++UjGzfDP7a//5iZ/zL/Y+LgE+L5wfKPkeHrP6a3LP84WFiZGLgZ2f7+/88M8vrfP0y/mbhYOb7//vGfixmUGZn/CbOzsP/9w8Pyk5GL5+dfBpYvPxkZ2FkZ2JkZfnD8/sbI9IuF7dc/pm/MLAyMf//+EvrH9uM7Ewsn4xfWr8w/OZj/cfzh/snN9oWH5RcL87/fzOx/fjH8+8HB+J+D7ScrO8cvtj9fGDgZvzEy/2diZP3Ewcj0n/E7789/35n/gJqffzg//Wdl//H7HwfD739sXD9Z/v9j/sfEyMzzg+Hbj39sDD//8jLxvGP6xMjyi53j1w/Gv/8EGRn/sDIw/Pj25c+3P9/+/fr3iZXx65e/3xk/fvnx9883pl9/vn75w8PA+usPO/P/v7+//f344/s/lu+/GP/xcPz7/IeZkfHzP8afbEwcjMy/GZiY/7PwMv7+8o/933/mP78Z2H5/Z//DyfiX5f///3+Zudh+/2PkY2T6ysDCxszE+pfl/1cGVsZ////9+8vO9I+DQfjNX1ANzvqH+yfTj/+s/9gZGHh+szP///fvDwPLn7/M3xn+MzP/ZWD/w838iYWd7dePf8x/GX+w/Ob8+oud7R/T3/8Mn39zsv5iYGRmYOT8yvqL7Qf7538/WVj+/WT9x/SWi+33XyaWP7//Mv/9zsD0i5mZD+QkFkbmv8w8DOx/f7P+YfvDzML09zcn3/9/f//8Y2Bi+8/O8+cv6/+/P3nZGTjZ+fgY//4Blas/f3MxczCy/GX9x/CdmY2J8f9PVs7/jN/+/f/znYn5/5/vX1lYfnz+zPKPEZS3BVk+c/3//5WVkeX3D6affxk4f/9l/MXDxvL/1x/Ov79//2VgYmf5y8rIwPmPnenD/38/WTlYfv5n+cP07x8v98/vrH9+MP//xc72i/v7n/+/f/3l+87IxMjI9vMzEw/jv+9MnL/ZeH8yfmdmYOD48/X/TyYO1p+gaoOV8Scby48/v/i//v/N+5Pz11+2X6x/f7P9Z//P8ovhJyvLH9b/Aoy8HN/ZGP8zMf1gAPUFf7Oyf2Ni//P/B9M/rv///jGA081vFub///4ys3xkZfnL/vc/C+NXVta//5nZv/1l+8X8j/0bx19Wph/sTD//MnL8Zv39l/knJwsD87//31m5f/zn+v3v3y+e33/+cvz9y/aLiePnn7+MjPx/WH4x/fr/n5H9PxML1282ZoYfjMIMf//84vnIwvyT8TfjHybun/8Z//xnZf3/C1QI/2Xi/MTM85+Fhecn05+/HL/Y/vN+ZfnHzPGH9wvXjz8MoPbSv58MjP8ZGdl+Mf77y/ibkfML21/O7x8Z/7P+Z2Jh+PKP/T8j23cGrv/f//xh/cv2kYcB1P5kZvn5/w/Lr7+MjCysv/78Z+H48ovl7w8m7t9sP/79+cv76yfzP4Y/DN8FGb4y/mBkYf3Gxsz84x8DC+M/Rg6GX79Y/jMw/P3+/xvzLzaG/7/Y/zCwMv379f0fExvLvz/MbL8Zf//9wfr/H8+v7zy/GH+z/OH8/fff3/8s/1jY2H79/ff7Jwv7f6Y/DL+4GZl+/2bm+MfI/ucn8zcWJhYmFs7///8z//3H+Pk/L9Pf33+/MP34xcLB9vv/f5Y/HIx///9gYfzDzPL/FwPzvx+MLL+Y/3xj+M/K9OsjOxczJxPrf2Yepl9Mgpw/f3KwcrOyMbD/+c30588XNn6uX6wcDAy//7Ixcvz8/pv59192LgY2lp/MfxmYGHnYf/78/Pcf23fGn98Y+QWZ/jL+YGf9yczB+Ps3IyMTMzsbG+Ov35z/fzL8/c/EyQgqxBj/fP357/cvtj//2P6w/v3H9IedhYXp978PnxkZ2H98Y/335+d/9r/fvoLaXH/+s7CwMLJ8+fLtCyMPIwMzK/vPv2x//v79/+kvCwMDB8vf7yycP/4z8TEw/OT6zvKPkfU3w3fmfwwMvEy/mf4ycP5n/s3y5zvnX3bmXyz/vrP9+/uf/S8Ty1dmFhbmvz8YfrP8/MH4/x/jPzZQ7feX+T83x2cGZrbvrN9Yfvzj5PnJ9YvrFwPjn1///v5hZGT585XjN9uv/wz//zL+YeP+wfL39zeOT1zsbCy//jL9/MXO+P8XKxMrO8M3hv//mdj+/uD6yfaBnfvL//9sDMw/mVj/MDAwcjEyfmdnYfjyg4v9GwPHjx9MP5iZmVgZ/zCzgsz8yczK+f0H+7//HBwM/xgYfzCxsf38zij86S8X01fGn2z//zMz/vrJxPqBnZOBiYHhG6jvwsrA8u/XXxaWf0zcjAzMv38yMLD8+fXtLxsz6/8vrH+//2f+8puJkZGJ4+e//9/YfrP+//GL4+8flp+/OP9/+Mfx6/8f7r9/mDl+sbIxMv/+xfHjL8sPFobf3H/+/mP68Y8X5DA2tu+M/3n//vnHy8j0iYGJjY2B/d8fTra/3H8ZfjJyfmYR+P+d8RfXfxY2VvYfP5n+MTIw/v3D8o+FheP336//Gf8xfGHj/gMq4n78ZWb78p2ZkYGN5R/rr/9MzH8Z/nIz/vzF+VvgCwsDyx9G9n/srAysv1h+f2X/++k/Aw87w59/HH///OH4ycrwjpXp53+uf6ycDD+Z//7g/MHA8ecPG9N/vk/f2Zj//vnD+Pc7B8tvtj+/Gf+wMn1jZ2EEWfqL/QMzF8MX5v+M/9i+gAapfv1n4vjJxPLjP/Pv/4z/Gf7//8PxnY2JmePvT+Zf/1n/fv/GzsXyiZeZ6S/bfxam/7x/frEw/2f+C8r/zJ//s338z/mb+Qcr479f/xl//mP7/e/Pr3//fjJx/PgFiiueX2wsrKAi6R8z6y9Gpr+M/37/+/2fg/Uv8z9G5r+/2f79YWD6xcjN84aD5R/bb05Gxn///3L+ZmL8+/cHF8svhv9sPxl+cH9n+srAwfyJ+f9f7n/MrJ9B/Zq/DP+Z/nz/+Zf9728OFoY/fzh//uDl+vGVgZv99x825o9s3zl/cX5jZPzP8JvzD9P/P3/+sHH/+sLy6+9vDo7/nIz/GJj+/GFnZ/j99z8jJ+f/v2w/Xvz5/f0/w3+GP/9YmBjZ/7KACoV/n/785/vN+of5DycDEyPT9/8MnH++/mH5wv7rFxM7J/Pfr0yMPxj/sTFzfPvNwfXnz3+Gn6DoYvj84/9vVo5///7//M/0h+nv1//cjLxcv38zc/79w/SP6fcfwX8Mv5m///3Dxvn7N9dPFgbW33//cv1kZvjFxszygY3tx39GHubvv/8ysjD++8HEzgAqB378Zf3JwsDA8o/5J9N/Tpb/fxn////L/vsXx7+fHD+5QOPN3D++/WNm4GH+wAAK2r9MnH/+/Wf7+42F9RczM+tvRpE/LIy/OVh+/uP89+8XEwMr0zfQ0AzLH+Z//37z/vn7/w8DJ8vfn6xs/34z/f3H+o3pN8c/FhbWr39Z//xl5vr7h4HlO9M/Rub/rIwfuP8zsIPqhZ8MoIqT5S8j6wdGlq9cLIyMf3785eD4/ZvlF8fff0xM3zmZf7AwMv9h+A8eXv37h+MX4y8GJobvDKDxwt8sgt9/MDH/ZmJj+vH3F+tvDsb/37j/Mf77/+Mfy+/frGwM/9n//v/KxsTA+I+N6d9fZq5/v/7+Y2Hk+Pedk+E7CwMjwx/QuA0j2w9G9j+/ef79ZvnL8vcrGwvTrz9MDP+Y/v9lZvnNyvaNnfk7C6hRysT0jeW7wO9//wT+fGYGNd3Zvvxl+P7vPws7PxsT079v/75zcHD+ZP3DwMzB+I/x738OUCP3H/sHUCP117+fjJysP77/AQ3lMzD/+c8HqoX/MrMyfmdjZP3H8oflC8P//8ycv5n+MDNyf2Nm+/3vJ6jIZGT9x/Tv77+/jP8Y/rH8A1XHf/+Dhux4fv7i+vuXnZGXkZ+Dj5eLj5ebifHPt2/sTK8evn/7lfEXKxuo58j9l4HxDwsLKyg3srL8YfvP/pmL/T8b48/voBFRNrYPTIwsP/7/Ymdi+MfGwPqXgZnjDxuoLP75k5eF+RcL0zdO1u8M/7//+83BDSrW2f79/MPyn+n3X5Zf//+DymsudgVhHiE2fgYB/j9vfjOysIsICX16++rjgyc/Pv/h+M7C8pnxDzPXFzbGnz+ZfzPw/P3/9R/DD6b/jEwM/36AhkL//GX7+53hH+dPxl+MjIxcf3+x/vvJwsjx589vpn+///5l+c/K+O0XM88XBta/v1jYvjH9Y2H6wyj7/ys3w2dm1r98gvyiAmKc/zjZvv5iYP74i53//R9mVu6fjEz/v3189/zBnz8/uX8xMfzn+czI8vfvv//MAl+Z/vxlZGL7++cvC8vfPwx/2Vl/M/5iYmZg+/Wfif337/9MPN/+sDD++fOP9S87w98//1gZfrD9/c/+7zfXN47fjAwMrIzMv9gYpX//Y+T8y8EvJiDIwsUt9unH31+/fjP9/sfIxP79L8s/djZQk46B5c/Hu08+/2H6w87x/zPLH/Z/oN7eP5afzIw/WRm+M7H+/8f17S/3b85vP9lBo1ssf1l/M/zi4Pn+nfkvC9MvBlDnkuEb/88fnD9/sbOz/vnD+IfxLxPHz//sv1k+M//jFhERFWBhZeH89eXPT6av39j/MDEx/mX585PtJ9O/v6x/Pv9lYWH/pyZx7w4j65/vP/+x/PvGwQgqln8y/+L5wcDK9ovp339GFva/vxn/cf5j/sHKwPbj118OBtbPnL8Y/v5jY/zLyvGT4S87w+8/v1n+M/P8ZP3zm4X125+fDMzMfxiVeDhERSW5uT/9Zvz96/cfVsb/vzm+M4FGYP/+Y/z+n5XpDwMjw38GUKv127PbDL/+/GNmYP7D+pXxDyfTbwZmhp9sf9lZ/v/5/4fh30/ev3+5/zAxMnzj+faPE9SOZWL+xcjE/J2R9ds/VgYGFo4vjMyMrAy/WT4zsoKqNfb/31lZrPj+svMJ/P/86df/3/9+MjNwsv1jYfjL/Of3l7/8HF/+s/MwsP1+zcj5g5GJkUuW59OTT///MzH8Z+H4xfSPkf3nF4F/v3//Y+Vm+MrK/p+NiZn539/fLExM3xg5f//g//eXhYH5J/cftm//uX785vvKwszE+P/PH47/PxiZWX9wMH5l/s3OwqLI8O33j4/fPzOz//zDxM7I+esv+7cfX5m4fn7785fp14/voMzA/Z/t71/m/3/YRUSYHnxg//X9DxMTx/evnP+ZwMOorMzfmbh+/WT6++8/++8vQn9/gvpJDFwsf/8y/WBk/PGPgfMbO/efX8yMn5iYmP7/Z2T5z/b7DwtoCIrl3w8Wlo9ffjOCpjr+/f3HzsjAwPT763d2pl9sTIyM/3/+ZPj5/ecvDqFPfxm4OBlAfWBpxru//rGz/vvBzMX0n+E/608Glp9//jAz/2f6ycz0m4XxH+sfJtb////8ZgINqDNw/PrFxsL4h+U/A/NPVmbQUA7jj79/2FnfM7L+ZGL48Z+ZjeXjjz//WH9/+8fMyM7wj+HfV45fP/4zsTAxsLD+Z2Vn+8rOzfD967f/f0ADQH+Zmb8yyHA9vcfA/pf1+3d25t9sHxmZ/3L95vn+7y/nv/+ggff/jD+YeX79Z//H/IOd+f+PPyysP9k5vv75/ZuJ8+d/Rkam/wygUuonC+N3NlDRzMzE8pnhDwsTC6in+YPpz5c///4ycX3+yMLA/uf1v08sbEz/f/7l/MXEwP3r+2c2FhZGLsY/AkyfnrMzs39jYPz96x8jMwvTb66fv39xsDCwcPxm/P2fgePnFxZG9r//WP6CmvfMf9g/fedkZf356993pt+gcfJvLH9//+P9xsHAzMLym4EJ1BTmYPrxm/M3w9+P///9Zfz/7/13LhYuKcm9rxiZv/7h+vqLiYGPnfPjz/9fOBj+s3ExcjEr/fvI8PM/38+fjIzsDMx/eP9952Bk+faXETQP+ouDg+XrX6Yf/xlY/zL9/c/4h/Mv24+P7P9ZGP9wMDL9+8v0n/EvG+vnH8y/WX//YAM1Mdh+MbP8/f//z/uPf/6zMTL///CfSUGYlU3khyzDX44/P39+/f2T8SMf+39u1m9/vv5h/sXF/l+M9fZLJo7fP5mZGNi/snGCRkpB833/f7IzsDAy//j3j/HfL5Z/DP9YmVgYWf/8YWDiArW6mJnZWf78/cv38///36w/mJj//+NiYmX6xMLE+OfTn09/mP//Y2b9y/73I5OEmDb/n/c8vGKSb4X+vnz26Q/TH8aP/9l+cHEyMjL+/PiD5x+T2G+2Lx9+Mv9jYv7G8IOBlZXhz09mxj/sjKy/2b99Y/rLzPOd499/1t9/GH9wMvz8z8H8g5H979//35n+sX39+4UVNDjzn+HvL9a/DN84mBmj/3///fcPAyM7AyMLy58vLLKasqpPnzBw84n+uPL151/Ob/d+/Pn29f8vJlY2dlGOz9//MzGxsf9i+3Py9b9/7L8Z/zH++8fyn+sHwy8WRq7f/9l+MjB/Y2Fg+cPy/y8T+7d/TBx/fnGxfGX69xfUr2NiAg/1Mv5i/Mf08z/LP9Y///6yfPr39y+oe87MysD45ZewhKo416v7b74JirOzmv56/l/gJw+z4KVLv5j+//3x/ZsAqD339c8vpj+85meesbP+/8PwnwFUGbKw/GX8++sP1/c/rP9YmX+ysvxlYmX9z8jxC1TmcjEy/wJlVBaGn/8YOBj+fGf+xw1q3nD+Y/jPwfKb4R8z6x8mdtDYEIOwpoK81KPbt79/A1V5/Awyv79wmzx/xyP09SvDr5/MP59zcoLmVn7+Y/gvIPsNNEjB8ec7G9O/Xyw/2Fj+gVrRLIx/2RhBfvvOyPKTheE/+w+2v/84f/H8Z/z7g+s/yzfGf39ZmJiYfv1j/c/C8O8P+z8WBjbQuNmff38///4vqCiv/f782ycff3z8yf7pPyfvpz+3pYyFmKQ/vmf8DaoQ/n379YOVhe3f398/3nHJPv3EzP3rLwMr81eW/39ZfzCCOtf/WP78/8vwm+EPC/sXdmZWlj+MzEwM//7///WPieP3bzY25t//mP4wsjH8/83I8I+RnZmRhZf9K6iW//vzHye7vJ3mp+uPPv/+94/v3ysmDgnBn28/Cj37/F3L+eb5X4wMv3//YwF1HLjZGViYfn9jl//649dbFoa/v9n//mL5zfrzDzPbt19/mP//4mBgZPn3j/U7Eysz699/4Bm536xMjIzMzH++sjMxMrP8YWD8z/brH6hWZ/n77dcvZjYmBjY+bnZVLZE7z159Y/jHwP3v/X9uthu/Odne/f/w65eL7NNHTMz/fv/5y/LvN8NHXk6O//9+/eIQYf4v9ufrNwZGpp8szMw/OJgYhf6zMzJ9+8v88/+vH2zfOf6x/v3zg4uJgZkdtKzgD+M/Fpb/rP8YmX7/Zf37+z8H+9+v/1mYGdn/MTAJ8v8XkBCR+X71zo8vn/6yCTJ/+/Pn37+3/9jZOZ6+//bth7rEXSbmn/9Y/v9l+83E8Pn3T+a/oEFLbhYhFob/zAz/fzGysYIarV+52Zj/MzMy/f319df3V/8//Pz+l5Hty1+ebwy////7zfiPlePnr/9MvxlZ2X7//8f05w8zE2iqVpiLm0dc5i37r+vv/r35/uvbn3/fP/369+vbD1ZGTrZPX36yfnkjwcPG9uUny2/W338Y/jL9+fKNgZGB4TcDM9+v72xsjMzcDJ8ZWHne/2b495uB4Q/LDw4+dn6m77K/Pv399ObTL6Z/PxlZmb4zsv9jZeL4w8DM/JvpHxP7P7a/zGxff7Mw8kqqM4twfnz25f3f79//ffn06/e/b+z/f/xi+gsapvv/4ycD759f7+W5vv9hBBWZ/1n/MrIy/vvxn43hD6h5zvT9Jzsvw3+Wf39YOf5x/GT8/4uZien3zz8/mD8xcYj+FZL88/XtxzcMf/8w/mdj/MvynZGV5dc/5p+/WP+yfuf/94eRRZpbUfbf70ffX39l/PvjLyvj///M//6++wNaG8PA+usbK8O/v7++sr8TVbr+n+0fyy/Gvz9BdSUj299/DMz/f/7k/s/479fX36Au1Xe271/AUyd//v/9/u83wz+WH9/+s3L94mQTl/rz6t03xj9szL////3F8YPnLxvLTwZQXcHAymLG8fbB74/vvv8CtYgYGf/++sv8m+HP31+MvxlYfjMzsjJ/Z/735+cTAfW/51n/s7J+Y/vFyPCXkQnUtfrLyPz/JwcjI8OvP0ysTH8ZGdgZGP+DxnZAYzmcX0EVMvO/X/+ZWTgF/wi9+/buzZ+/rExMf/8x//rPxsz2g5HpFxMLO8uHL7/ef/sJGgX+/vcv05/f//78/cbCzPz/H9MvBsa/rIx/GNn+ffvD849H7tYn0HgOIzMTE/NvZsZfTH8ZQcMmTKyMjL+Zf/5n+svwhYv9/z8m5h+/Gdn+/2FmZGf9B25BMjCACggWBumXHz7+/crI8puN7Q/jD0bGv4wsLN+/sdz8/v0PqNP388/PP////f/z9/fvf8zsDL9BDRfGb2ygodov338xP/j9k5Xxz39Gpr8M/xn+MTMwsjEw/WP//fvvR07u////MbP9/cPw5/MPVlDI/Gdj/vOL9etvln/gEpiZ8QsjLzPnLy6e339ffPrz6uv/LyzsP5gY2P8x/v7Lw/Lxx7c/bL////77+9dfxr+MP3/9Y2JgYWJm+QOqI5gYmZkZ/v79y/L1IwMrA9NvJh7ed59AA4n//zH+//cf1MD4/52JiZXl/3/G/39+sf37ycTKycLy5zcjI2g5E6jEYWD+xMTC/JGFmek/Ew+30N8/L95//PL1Hxsz8zdmRva/f1g+Mf1mZGD+x/Tn109Ghj9/QO0jVoa/DH///Wb+zsjwHTRZ8+fPP4b3bGwi7A8ZWXhZfn5jZPnLwvjjFwsDI2ixwO8vrL+YWJlAAfeP8R/j/5/MTJygfiozEyMr62f2Pwy//rP//cvxnYnhz7//jFyfxCX+f3zz7fN3Po7Pf5mZmVm+M/1m+MP479ffP/8Z/v3/zvb/738WZgZmhr/MoDHAjyzczH//MrL8+/qRQ43947uvT5m5/rCw/GFgYGX+yfaH+TtoVcGfvx/ZQUOYf/+wsPz8y/Tz3y+m30wsf1n+cf1nZf3JwMz0l+HfJ4b/v1hYvjOw/mVnZ+KQ/v3qNw/Lrd9/34PmUUDt2X9//jH8+fud8e9PLmbGf39AjSmG74zM//4xfOf4/ecfI8P3r5/+CYt8/f6NiZ2bkf3/rz+Mf1kY/v/6//83G9e/f79BUc/KDq7mGP79Z/j/CzTRxPiLkf/Hb2am34xMoE410/e/3IxvmFn+/vnH/Z+B49cXRe53P76D1tj9Aa1C+Mf09w/LL8bfrEz/mf7/+vSD9df//2xsPxn//GL+8/c/y68fPx+9Y+X89vf/J5DnGJgY2Jh//Gdk/fEPNAfz/+d/Flam3ywM/1lBvco//7lBbvr/+88PJgZGNlBznOE/aAnVr/8M33//ZWH4wcnCyMLNwyDKyMjyn5mRmek/+88//5nZWRn+sDMy/GVgYuD4/+/PPw6WH9/YWb7/Bw1UM3z/8pWdS4X9xV8Ghr8sLP+Z/7D+Y2Fh+87+/TcTEyNoWJuREdTI/cvMyvL3/z+2fz8YuRhBQ2f/GP8zsrL8Z2BhAS26+8PI+Os3y9+ff1iZfgj+YWTkAVXff36BmsYM//7/+/OH4d+vvz+52RjZ/33/z8L24z/LHyYOULr48//Xp2e/hfk0/7z895+dg/nXz5+s/Jxf//5lZfzO9Oc/aMjtB+Pv//85/jH8+c/AzPOVkYkZvPSGCRS1PxiY2FiYuFh+/2IBZT4mpt9f/oNHGXk+sbMw/GNk+PMftGznLwMT61dQfvjJwPyb8S/bX9A0DjPDb5av4GYWA8Of78Lckl+Z/7D9/Mn3k4PhC+u/n38YGFj+s/z6x8D0l+HHf4a//xn+MbGDJhn/sDAwsv3+w8nA9gs06vj/BwPzb05mZqZ/v//9/8vAyvT7L+MPxh8/GDlYfjMxMzH+Y/rzm/H3n/8/GBj+MPKAwvzX7x+/eRhY/vxh/c/2nfk/w69/jIxMnH9AI5h//v5m/PWXi+3bT0ZQS5D9398f/xgZGP7//s/M9PMvE9t3VvCoBCMo6TH9Yeb6/+v3/39////9DRoD/cf5nQk0BM3Ixsz6j4nx72eWvwy/f4Hq1v/////685mFFTQYzMj8/98PRnamf6Blb+yMHAxM/0ATbH9/f+b4+R+07pL9648/fEx/frPz/P/68z8z958/HH9//QcNVf1l+sPG+Pvn//+Mv9n+gFZxMbP8Z2H4wfGVAdRT/cnM9OUPEwsX43dGFqY/LAx/GVhZmFj//WNg/PWXEUSx/P/75x8TIysjMxMz0zdG0MjG/98s/1iYmf8yMvz++YWJhY3n39t/3378Z/r4nfPfFz6B3/9Z///+x8zK9o/15///bHzf/jGwcrD//wxaCwky8/+/P385mTlASZ3lN2hkhOn/v18/QfO6bGz/GNh/MII6asz/mFkYGBgYGRnYfv5lZWJmYGRhYGL9xvSLkQ00E8XAyMz4hYX1J2iggZMRNC/K840RVEv//fPzBzMjA2jQm0/4HQsD2y8mJlamv/9+sP/lZPjPxvKXi/HXF9D6lR+srKCmCvPv/0ygfP7rD+Of/8ygovP3bzaWLyz//zEygOZT//3+++MvNxsTKGczsjAxsjIz/P3z/x8bM+Pvf4z/f/1hZf7z/QcL+38RZpbXvxi4uH+x/fn5j439H8tfNsZfv77+///jP9O/Pwys//8x8rH+/cnGws7G/IX59y+Z/zd//WZl/svAyMoBWrr799//fwwMTP9/MzB8+s3Kwsjw7xczE2jY6w/LLwa2n0x/mBmY/zKAOoqsDEygZiTzH1DHio2ZkfHfz88sQixs//6x/GXkZ//JzPrlu/Cvr78/MzB9e8zICmqw/vnPws3NwcUm//4jCyffWzHpZ+/Yv/xmZPz7j52B6T8o0tkZf7Mz/mf8w/XnD9P/f0z/WEDtK6a/TP//MTL9YWcETR7/+8XE9P/X378M/0CjVX+ZWJkZmH/9ZWX+z8T27xsLMycbC9u///85vnH8Y5X+y8fy/d2TT6zcX0E9AOY/oHkPNmHmb19YeHj/MIqwsAny/frz799nlm+//7Awg5b+MDKyMjEwsoAmv/6CahSW/3///v3HwMoEWonH+O8XExsTaDEHw/9/f9iY/zD/ZvnNyPiH4z8j2x8mJoa///9+5/gFWo4EGtb8yvD7G8sHLibRf////Odh/sMCmpNm4ODi4v73++fvP0xMb/j/sbKJ/73DrfDgOzMoEfz9y/T737//LD+ZmZm5/v9jZORk+M/y//+/v0wMf0CTEX8YGP7/+//3x392xv//GTkZmf/9ZwYlFtbv/36xsDIz/v7P+O8/ExPLV0Y25r+/OP/9/ffjJyMjP4coy+ff///8YOT+wcLAwMolyPHq+/dPDMxMjN9ZGBm/iwp9YxP6+YyB/S8juDnz/x8T2z8Wln/f/jExsXxnYGEGjYn9Z2D88/cv46/ff0Ezr39/sTL8/M3ym4GB5S8DGzPDN1Y2UI8PNLrwDzQtyvyfmYGRmfsfI9O/f8x/vvN8YeVjY/jz5dd/JlbmH0wizPff/wENGzGx/ub4yfP2Ky/j9w//Odm/MDAy/GZmYmT4y8L0l4EZNI3K+PM/IwszEws7A+O//7/+/P7zl4mB8RP3//+gIemfoFBmZGb885eDkYGZ8fd3NlC9Dlop9YvhPyMT4/8fzAyg9Sh//jD+/8f+n4mL/feP/6xM39hY37//9fsfExPDz5//mP9/4/jDy/ubhYfr/6e/X5iZGH/+/f3v718m1p//Of4wcfz9+xtUmP4DNfKY/jP/Y/zJzPzzz//fjIys/zn+/WH6zcL0+y/D73/M338x//3B8fsPqInGygDu4f7/DRouBI2l/fjMycHMzsrAwybEAloN/fU3E9d30BqdP38Y/v/gZPnPwCrOzcLK9+uFxL/nvxg+/PvPwsrI+vMPE5fgr298LH8Y/v0FVWZMzH9Aw56/WRn/Mf7m+veHmZGJ5c8fRlD9wfiPgesrK8Pf/xyg6QiQ6v9/Gf/+ZWRgYGAGJaHfvz4ysjKxMTOwgnrCP3//5/n35++/H6DFw4x/f/7/8U/o7TdG7j8MAtwvub7+YmT58+/rz39M4C7AHx4WULHAyMDEAJqD/fOX8T8jA8Mf9v/fQeuWfjOA1q78/8/M8AfU2GJh/PeX4R8Tw39GUNXD+O8/A9NfZsa//xhBa/tALfN//xlBTvjPxPj3JwPjf4a/oHFMht+///9iYWXkY2Nk/vJD4v8bhj/cfz/9/fufme3r9z//f4HbM///gQZP/jL9Bc2e/vnFxviH7d+/3+ygnuhv5r/MTD//sTD9Z2X+95cJ1K/4y8r0HzT+9Y+N8R9o2clvJnA7+RfDH1BhyMzIyMz46zsTy99/DIz/QBNGDH8+s3Izg4bQ/jK//cbAysvyn5EZNHnG8e/Lb1aWvwyM/0C9mn//Gf+zMP7+y/CXnYkJtMb5P2i5GCsDIyvnX9CE8v//Pzn+/WEDVZx/////D+ocgJz9F9SVYAbtCvj3l+Hvf2am/6DF7gz/mBhBfQjQsPJ/BrY/rOz/fzAy/WFk+MstyvaX6wMPy7c/XJwsnF/efgcVNP9Aqfwv0+/frH9ZmX/9/8cK2qDByM3w+z8jE9OfX6wMzL+YWP8x/gRNrv//z/z//39Gpv//WZkYmJj+g2Lg/7/ff0Atmv////9j+ANa9/GPAdTHZP77HzRyycj0//fXf39/MbL9Y2Lh/vOHiYGV489vNqYfzN//cnOw/GNiBG1q+MPw+x8L4z8GZtBKVeZ//zmYfzMzM4FKHjaGX6xsv/7+ZWEF2c3IyMD0H5RmGP79Z2YADe6Cyi4GUG0K6pz8Ay2u/ccE2inA8Ac0+8rCBFL068d/5h+MjKDVHAzMLM85ORh/f/sHKpFZ2FlYGX4zMfz9y/jvPyPDf6aff9kgiecvI8M/1n9/OZl+/2Fk/MPE+IuRARz6IEX/GRhAloHWZv//++83IyPD/3+sTKDUy8zwDzS9AFqECKrI//9l+M/CxABO5/8YGZjY/vz8AppnZQEtZPjBBGrNMrP8ZGUE9WdAs4j/fv9jY/jNAGqzMYAWyf5hA60tZvrH/JMZlPCZ/v1i/MvADJpzZPzP/AeEQSUYaG8CKDH+By3CA/WW/jKx/vkP6tgw/gIN4oG6Cf8ZGUGl9N8/v0AGsP/9wcj75ycT85+/jMwsP0D9T5Dz/rP+/fX/NydoEJH5HwPjXwaO33/+g2al/jD8Zfz/999/Vsb/oG7zH1AlDeo1/f/HCOpNgtIWuGn7n+EPEwPT//9/mb+Bgug/A2hW5w8Dy18m0KT77/9Mf1hACZWB6R8Tw++PbKwMoFbjbxY2BlBnDJTRQWXvn/+s/xmYf7OActEPsL//MzD+Zmf4z/iXCZSyQCHxH2T0P9BKIeb/DEz//jH8ZwJ1Chn//f8NTh4s//+DdoH8Y2b8/we0qeDvX0Z2ZgYGcMSB1iX/+/7/PyszaEKX4T8TI8v/P6Dm1U/QuMg/0PqYv0z/WBgY/vxn+cf49yfjf47fIPNBifrfHxbQ6DbbP2ZQcgEtzvrHyPj/P6gl9x9UVYPa9/9AQ4KMoLbov3+MjKDMyPiXhfE3qMPJxMgAag2DOgGMjKBV8qAWIwMoan6BsxXD/98/WTj//WNk+Pf/9z9GdoY/zKBVgsz/GX6y/mVn/ANeiffvN6gJw/yfAVRT/2QDlUyM/0FWMPz7D1qizfTvD6h6/8f6j5Hl/z8GJoY/zIzMfxn/MYC2dzGApg9A/QaW//9AykFLkv4zMTAxsDIy/v39n+HXX1BBy/yT5Q9o1ur3rx+//vxkAi2HZASVbwxMoBBlYGdiYQF1NP+Aaqz//8Ch+p/x399/IIP//f3PDGoR/WcEVadMrKBl/Yygeh5UYfz/BxpWBjXr/oL2gv3/8+f3/7//mRn+sYBWqrCC2uTM4C7v3z+sfxn+M/5mZvzDCVpAwfgPVB39+g0a+mNh+sf0h5XhHwMox/5nASVEhv+gYVlW0LT5XwYGZnBUMTL9//uHkZnh33/Gf6CVyiDVTKD8AEqLDEwM/xgZ/zAw/GEAxRIjqJD/ywJaZMXwi5mZ4R+oZv7P9J8F1LX7/Z8JNMD6j+nvPwYm0AwmEzNooB3Un/3H/I/5PwMDAxNo6p4ZVJ8y/WcEzT+Dlr+ANpcwMoCXcTCAJon+Mf5lAjng138mUHsYVJKCHAYq3/8xgi3/C5rcYGD8ycTC8IcRtGSU+e8/FoY/bP8YWL5zgZo9f1l/Mf9nY/75h5Hh/x8GRkbG/3+YGEBjTP//svwFFYk/OcBLPJiYQKNHoGoAFDsMDEyghVageTomhn/gcvYvqM4F9b3+MzD9Z/4P2vrHwvAHtG3u319WRkaGX0xcP0GZ+xfjv98/QDtvQDM9f//8Z/zN+JeZDdT0+QNaEcjAwgIKFYY/jP//gEKHBTQeDsqSoPY+w19GUPr9y/oXlNb+/GH595uZlZEBVGz+A8UDA6jYYwR54h8Dw79/oGGk/8ygLXR/QKsCGH7++8/EDGq2/f//7xcr43+mv7+Y/jBx/Wb89efv339/mZhBJdq/P0y/QKts//1lBXURGEBWMTL9BIU3aDCFgfEfwx/mvz9ZGL6z/gMFCcNPRtCoJKgo/8PAwMz8D7RiHNQXYmYAzeCDlhkzsLD8Zv4HWt7MyPCbjekv429Qo5aB4SfTP8b/rKDxM0aWb/+ZWBmYQZNfDKAJe9BeFoafjCz//4NG/H5z/gcVVP8YQMUeKKEygzY9/v7/l5WBge33b2b2f+DC6v8fZlD/+z/jX1A0MjIxMPwHVVaMf5n+/2JkZmQGDwODxmhYQPOWzH//sLCzgPruf0Hr+EE76EC7dX5/+wcKWAaQXaAGEmgFxu+//378Z2b5+YeRleHXf9BiMoZ/v36DRgrAfS42ln8Mf//9BW3UAGXRf/+YmJhA4fUPlDT+/uVg+/GbgYH5F6jAYvnPCinRfrOCanemH6z/mEFrKhmZ/oBimhE0SgBaUQ4aUwOVVQz/QDN0oF0TTP+Y/v0HtUwYQc2l/wwsf//8Bu0ZZfrFyApaZAnqM4LqJQYWZtC6m38M/0A9dFZmFo7voFKEBVRW//3PyvKdFbSqk4XlL6hWZv/N8I/pD8sv0KzgPwbWX0yM/5n//mf49YuRifHfnz88DKz///xhYfrBwvznz9//jH9Bo2MsoOUVP/6CWgmM/36D9h39B21f+sfwi43xP6hx+Rs0cPfv7////0D7UhhZfjKC1m7/BfV4WH8z/WNm+c3K/uc3M+M/UJ+IieX/X1BPBrR26icr4/+/oJbMP5Y/oMYIaJEf8+9/bKz/2P6DtvD9/fqH/T/rv9+Mf/4zsP4Bzcox/WcArSUEOfwPqAsD2p777+e//yz//v9h/PePieEX27/vLAxMTKAdhMx/mP6BeuGMf0DtZFC5wfqf+e9vdpZ/zP///GD6y8T27zcrAyPTvz9M7D8YWP/9Z2L9B6p7mVhA5d3v/4yMjH9BE8EMfxmYGf5//8XA9RvU/2YElUhsTL//MjH9/83y/xcT4x8m9v8/GUBDAL9ZmL8zMzL9B037szL/A0XEvz+M/0CLoZmZwCOVDKz//v0CpSg2Fqb/PxlYmUFrTVn+fGEEBQpoXPzPf3aG/3/+Mv35D6o8mZiZQEP+//78/fOLGTRk9+/X/7//2djY/zCwsP1iYPj34+8v0A6rX19+/APlVFC36z8zy19m0Pqx3yygHA1aZvH3/39Whr+/WEBLjVjYGBiY/oGq5n+gRUeg9g8TaJCa5SfjP8Y/oEbdP9DwMqhg/vGflZHxF2i/x18mhr8gT4IWW4JKpv///4AH7EGda7bff0GbnkBTnazM/379YgI1sEDLa0CjiaBFjX//s/79D+o4gpYqgUo65v8MvxlBS1QYmZnB06S/Qc2+/yyggAdVEqwg3zOz/v/L/Ps/26+/jP+Z/7ODZqf+gMoqJubfoGY2A9vfP8x/GUFNGuZf/0FDp6Cx9t///v9nYvr3neUfE8M/5v8sDIx/QFUn+z/Gf6yg1Z2//v1nASUWFkbQGB6oDQqaDGIA72BgBI2oMTAzMIFKXabfDCC/MTL+Zv3HyPyHifH/bwbGn4ygTWyMf/4z/f/+n/UnqBPHAEpi/5j//AbNGf/6w/LvDxtoGP0fE2gN+D/G379BhSa4rQXa9PnvD2iGjpmBkYHx919mBgZQ2wZU0TEy/gNXZP9+gka0QNJ/GZgZ//4GpYR/P/4x/GP69ffXv79/wW1CUDuS6c8/0CwpExMDaPkqM+Mf0Bj87z+/QONSDP9///4HWsDA8ZvhF2jdEAMjA/MfFtD2O9Boxp+/zH//MbD8ZQTNJvxhYPgJ3iXwnwk8sPOf9S+ovGdj+Mv4m4nlH2gRHWh4//9/ZhZQlc32i5HpH9Nf9j9/QS0s0CJ/0BAqIyPz3/+gCgnUkv//7+/Pf+yAMTAysYDHVxlANROY9YPpPxuknmBgBC3xZGACN2/+/2FmAnUM/jP9Z/7L9Jvx71/QPgMGUOMclH4YGRiY/4KG2f8z/WEEzTuAzAO1lEBdNZb/jH8YQeUj6/+/oFYIA+Pf/8ygkR+mv3+ZGFn+Mv0GJXXQDC8Dwz9Qu/4faAKKEWwEqNHwn/EPA8tv0HA7aHfhX9AwMgPIUtBCCQaGf6DuMROodQaeTPsL6rWBupug9MAIaoEwMICrZ0ZwQ+wvEzPId/9BeYyB5f/ff/9+AwDVZaOj940vigAAAABJRU5ErkJggg==",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=128x128>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from medmnist import BreastMNIST\n",
    "from medmnist import OCTMNIST\n",
    "# from medmnist import PneumoniaMNIST\n",
    "\n",
    "image_pil = OCTMNIST(split=\"train\", size=128, download=True)[0][0]\n",
    "image_pil"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383cc122-24db-4577-9871-07c96436d0e7",
   "metadata": {},
   "source": [
    "We will convert the image to a PyTorch tensor and show shape and data type details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74b8096a-7b70-4b16-9c9a-299fc432b78d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image.shape=torch.Size([1, 128, 128])\n",
      "image.dtype=torch.uint8\n"
     ]
    }
   ],
   "source": [
    "image = pil_to_tensor(image_pil)\n",
    "print(f\"{image.shape=}\")\n",
    "print(f\"{image.dtype=}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7cc67d7-b6ff-42c2-98ae-f6d486062f4d",
   "metadata": {},
   "source": [
    "PyTorch stores images as tensors with shape **C × H × W**\n",
    "(channels, height, width).\n",
    "\n",
    "The loaded image is `uint8` in the range `[0, 255]`.\n",
    "Before feeding images to convolution layers, we convert to `float32` and rescale to `[0, 1]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dba001cc-9245-49a0-b078-6f0f5657857f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image.dtype=torch.float32\n",
      "tuple(image.aminmax())=(tensor(0.0118), tensor(0.9529))\n"
     ]
    }
   ],
   "source": [
    "image = image.to(dtype=th.float32) / 255.0\n",
    "print(f\"{image.dtype=}\")\n",
    "print(f\"{tuple(image.aminmax())=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a50fe3d5-975b-4500-a10a-af4d3d75aaa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11f3ab4d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d05d809-5828-4ce7-adfb-095363fd0abd",
   "metadata": {},
   "source": [
    "## 2. Convolution layer\n",
    "\n",
    "Before defining a full CNN, we first explore `nn.Conv2d`,\n",
    "which is the basic convolution layer in PyTorch.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6df2042-8ea9-48d9-bde0-7579e6b1be35",
   "metadata": {},
   "source": [
    "### 2.1. One channel in, one channel out\n",
    "\n",
    "We start with the simplest case: one input channel and one output channel.\n",
    "This means the layer applies one convolution filter to the image.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "206def35-3a7c-4fe8-a8bc-1646f57bfe9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a convolutional layer\n",
    "conv1 = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b16792f7-7b74-4cd4-90dd-93e12e5c7ae8",
   "metadata": {},
   "source": [
    "We can inspect the learnable parameters of the layer:\n",
    "\n",
    "- `weight`: convolution kernel values\n",
    "- `bias`: one scalar bias per output channel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "008b50ba-02cc-4a4b-8b38-7a4f15b99ee1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv1.weight=Parameter containing:\n",
      "tensor([[[[ 0.1270,  0.0629,  0.0502, -0.0234, -0.1140],\n",
      "          [-0.0420,  0.0066, -0.1631, -0.1500,  0.1874],\n",
      "          [ 0.1807,  0.1172, -0.0568, -0.1186,  0.1591],\n",
      "          [-0.1956, -0.0710, -0.1472, -0.0561,  0.0052],\n",
      "          [-0.1958, -0.1872,  0.1574, -0.0707,  0.0556]]]], requires_grad=True)\n",
      "conv1.bias=Parameter containing:\n",
      "tensor([0.0553], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "print(f\"{conv1.weight=}\")\n",
    "print(f\"{conv1.bias=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "938766d1-fe2c-4294-89c3-4e912c59f621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(-0.5), np.float64(4.5), np.float64(4.5), np.float64(-0.5))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize conv1.weight (one filter of shape 1x5x5)\n",
    "plt.figure(figsize=(3, 3))\n",
    "plt.imshow(conv1.weight[0, 0].detach(), cmap='gray')\n",
    "plt.title(\"conv1 filter (5x5)\")\n",
    "plt.colorbar(shrink=0.8)\n",
    "plt.axis(\"off\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c5ecbdf-a914-459f-82c2-aedaaef925c3",
   "metadata": {},
   "source": [
    "Note that `weight` and `bias` are initialized **randomly**. Initializing parameters in neural networks is not completely trivial, but PyTorch's initialization usually works well in practice without further tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5688494-5b40-4068-96f3-3741e64d0021",
   "metadata": {},
   "source": [
    "We can now apply the convolution and visualize the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a122a1b0-4092-4e28-862a-b1cfe5598688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Output of convolution layer')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply the convolutional layer to the test image\n",
    "out = conv1(image)\n",
    "\n",
    "plt.imshow(out[0].detach())\n",
    "plt.title(\"Output of convolution layer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fa26f6fa-bf6c-4519-aa8d-0ae29fc3ac2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image.shape=torch.Size([1, 128, 128])\n",
      "out.shape=torch.Size([1, 124, 124])\n"
     ]
    }
   ],
   "source": [
    "print(f\"{image.shape=}\")\n",
    "print(f\"{out.shape=}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ae79b4-f443-4959-928d-08987e2a5ff0",
   "metadata": {},
   "source": [
    "### 2.2. Padding\n",
    "\n",
    "The output here is smaller than the input because PyTorch does **not** add padding by default.\n",
    "In CNNs, we often use padding to preserve spatial size across layers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0551b2e5-5279-43b2-b059-c6263f872687",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapes with padding='same':\n",
      "image.shape=torch.Size([1, 128, 128])\n",
      "out.shape=torch.Size([1, 128, 128])\n"
     ]
    }
   ],
   "source": [
    "# Create a convolution layer with padding=same to keep the image size\n",
    "conv2 = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=5, padding=\"same\")\n",
    "out = conv2(image)\n",
    "\n",
    "print(\"Shapes with padding='same':\")\n",
    "print(f\"{image.shape=}\")\n",
    "print(f\"{out.shape=}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80aa5fb4-76fa-4385-b295-339b22e18f3b",
   "metadata": {},
   "source": [
    "### 2.3. Multiple convolutions in one convolution layer\n",
    "\n",
    "With one output channel, the layer detects one type of pattern.\n",
    "Usually we want multiple pattern detectors, so we use multiple output channels.\n",
    "Each output channel corresponds to one convolution filter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2dae2f01-014f-456c-bf14-0a4cc84794ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 1, 5, 5])\n",
      "torch.Size([8])\n"
     ]
    }
   ],
   "source": [
    "# Convolution layer with 8 filters (and therefore 8 output channels)\n",
    "conv3 = nn.Conv2d(in_channels=1, out_channels=8, kernel_size=5, padding=\"same\")\n",
    "\n",
    "print(f\"{conv3.weight.shape}\")\n",
    "print(f\"{conv3.bias.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f30a313-a042-491e-bf8e-0448e9a89bc0",
   "metadata": {},
   "source": [
    "In PyTorch, convolution weights have shape:\n",
    "\n",
    "$$\n",
    "(\\text{out\\_channels},\\; \\text{in\\_channels},\\; h,\\; w)\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "37593731-239b-4616-b01f-1abc779eeb36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the 8 filters\n",
    "\n",
    "plt.imshow(make_grid(conv3.weight, nrow=4)[0])\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Convolution filters (8 output channels)\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b2e12db3-158c-42e1-b9a0-7340e7d730f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image.shape=torch.Size([1, 128, 128])\n",
      "out3.shape=torch.Size([8, 128, 128])\n"
     ]
    }
   ],
   "source": [
    "# Compute the output and visualize the 8 outputs\n",
    "\n",
    "out3 = conv3(image)\n",
    "print(f\"{image.shape=}\")\n",
    "print(f\"{out3.shape=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c02edf41-d504-488a-a769-3d3ed9441add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Output feature maps (8 channels)')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(make_grid(out3[:, None], nrow=4)[0])\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Output feature maps (8 channels)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4595b289-bb96-44d9-b342-47a9d788800e",
   "metadata": {},
   "source": [
    "### 2.4. Strided convolution\n",
    "\n",
    "We can downsample feature maps directly in convolution layers by setting `stride > 1`.\n",
    "This reduces spatial resolution while increasing receptive field.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2042db2b-37ae-4a08-a19d-c302cf89d77a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image.shape=torch.Size([1, 128, 128])\n",
      "out4.shape=torch.Size([8, 64, 64])\n"
     ]
    }
   ],
   "source": [
    "conv4 = nn.Conv2d(\n",
    "    in_channels=1,\n",
    "    out_channels=8,\n",
    "    kernel_size=5,\n",
    "    padding=2,  # We cannot use padding=\"same\" with strided convolutions since the output size will never be the same\n",
    "    stride=2\n",
    ")\n",
    "\n",
    "out4 = conv4(image)\n",
    "print(f\"{image.shape=}\")\n",
    "print(f\"{out4.shape=}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d0cc65-630d-4f98-99bb-9c5f2c090d1d",
   "metadata": {},
   "source": [
    "## 3. Define the CNN\n",
    "\n",
    "Now we put the building blocks together into one complete CNN.\n",
    "\n",
    "In PyTorch, models are defined as subclasses of `nn.Module`.\n",
    "\n",
    "- In `__init__`, we define layers and hyperparameters.\n",
    "- In `forward`, we define how data flows through those layers.\n",
    "\n",
    "We now use this structure to define a small CNN.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f28ad4b-0b44-4a56-abdc-2d1caa1cd5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TestCNN(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        # Call parent constructor of nn.Module\n",
    "        super().__init__()\n",
    "\n",
    "        # Define our conv layers\n",
    "        # Note: we could use nn.Sequential for convenience\n",
    "        self.conv1 = nn.Conv2d(in_channels=1, out_channels=8, kernel_size=3, padding=\"same\")\n",
    "        self.conv2 = nn.Conv2d(in_channels=8, out_channels=8, kernel_size=3, padding=\"same\")\n",
    "\n",
    "        self.conv3 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3, padding=1, stride=2)\n",
    "        self.conv4 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=\"same\")\n",
    "\n",
    "        self.conv5 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1, stride=2)\n",
    "        self.conv6 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding=\"same\")\n",
    "\n",
    "        # Define our activation layer\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "        # Global avg pooling\n",
    "        self.gap = nn.AdaptiveAvgPool2d((1, 1))\n",
    "\n",
    "        # FC layer\n",
    "        self.fc = nn.Linear(in_features=32, out_features=2)\n",
    "\n",
    "        # Softmax\n",
    "        self.softmax = nn.Softmax(dim=-1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Defines the interaction between layers\n",
    "        \"\"\"\n",
    "        # x is the input image\n",
    "\n",
    "        # 1st block\n",
    "        # x.shape == (1, 128, 128)\n",
    "        fmap = self.conv1(x)\n",
    "        fmap = self.relu(fmap)\n",
    "        # fmap.shape == (8, 128, 128)\n",
    "        fmap = self.conv2(fmap)\n",
    "        fmap = self.relu(fmap)\n",
    "        # fmap.shape == (8, 128, 128)\n",
    "\n",
    "        # 2nd block\n",
    "        fmap = self.conv3(fmap)\n",
    "        fmap = self.relu(fmap)\n",
    "        # fmap.shape == (16, 64, 64)\n",
    "        fmap = self.conv4(fmap)\n",
    "        fmap = self.relu(fmap)\n",
    "        # fmap.shape == (16, 64, 64)\n",
    "\n",
    "        # 3rd block\n",
    "        fmap = self.conv5(fmap)\n",
    "        fmap = self.relu(fmap)\n",
    "        # fmap.shape == (32, 32, 32)\n",
    "        fmap = self.conv6(fmap)\n",
    "        fmap = self.relu(fmap)\n",
    "        # fmap.shape == (32, 32, 32)\n",
    "\n",
    "        # GAP\n",
    "        fvec = self.gap(fmap)\n",
    "        fvec = th.squeeze(fvec, dim=(-1, -2))\n",
    "\n",
    "        # FC\n",
    "        out = self.fc(fvec)\n",
    "\n",
    "        # out = self.softmax(out)\n",
    "\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "93085d3e-54e8-44f7-ba66-4be808f2dc47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out.shape=torch.Size([2])\n"
     ]
    }
   ],
   "source": [
    "cnn = TestCNN()\n",
    "out = cnn(image)\n",
    "print(f\"{out.shape=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fdc5ac2b-4568-4fff-a829-9b165259a955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.5043, 0.4957], grad_fn=<SoftmaxBackward0>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "th.softmax(out, dim=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5337b0d5-7436-442c-bc42-fc6722f17a15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of parameters: 18106\n"
     ]
    }
   ],
   "source": [
    "# Number of parameters\n",
    "print(\"Number of parameters:\", sum(p.numel() for p in cnn.parameters()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ef43669-6f51-4ce4-9a73-ffc165384d72",
   "metadata": {},
   "source": [
    "## 4. Data loading\n",
    "\n",
    "In this section, we prepare the **training** dataset and define how data is fed to the network.\n",
    "\n",
    "First, we create a training dataset object with basic preprocessing.\n",
    "Then, we wrap it with a `DataLoader` so PyTorch can iterate in mini-batches efficiently.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "69278e37-c1f6-4c21-bedb-f7d264fbf80c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4708 samples in training dataset\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 8\n",
    "IMAGE_SIZE = 28  # (or 128)\n",
    "\n",
    "# from medmnist import BreastMNIST\n",
    "# from medmnist import OCTMNIST\n",
    "from medmnist import PneumoniaMNIST\n",
    "\n",
    "# Training data\n",
    "data_train = PneumoniaMNIST(\n",
    "    split=\"train\",\n",
    "    size=IMAGE_SIZE,\n",
    "    download=True,\n",
    "    # transforms.ToTensor() converts images from bytes to float and map the range to [0.0, 1.0]\n",
    "    # This is very important for CNNs, input data should be roughly in the range (-1.0, 1.0), never (0, 255)\n",
    "    transform=transforms.ToTensor(),\n",
    "    target_transform=lambda label: label.item()\n",
    ")\n",
    "\n",
    "print(f\"{len(data_train)} samples in training dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8e2d4d-7318-44fa-b73e-3b1ed231a301",
   "metadata": {},
   "source": [
    "Each dataset item is a pair `(image, label)`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d15aee36-1eb3-4d20-b881-690f247c46bd",
   "metadata": {},
   "source": [
    "We now instantiate a `DataLoader` for the training dataset.\n",
    "\n",
    "A `DataLoader` iterates over a dataset and returns mini-batches `(images, labels)`.\n",
    "It also handles shuffling, batching, and efficient data fetching.\n",
    "\n",
    "For training, we enable shuffling so each epoch sees the same samples in a different order.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f5fdcb49-0a52-4dd1-b434-732fbed57349",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader_train = DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1d9bf8b8-39f7-4793-81ac-1d3e07efa5ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_images.shape=torch.Size([8, 1, 28, 28])\n",
      "batch_labels.shape=torch.Size([8])\n"
     ]
    }
   ],
   "source": [
    "# Example, let's get a first batch\n",
    "batch_images, batch_labels = next(iter(loader_train))\n",
    "\n",
    "# The shape of a batch must be (batch_size, num_channels, height, width)\n",
    "print(f\"{batch_images.shape=}\")\n",
    "# The shape of labels must be (batch_size,)\n",
    "print(f\"{batch_labels.shape=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7939f64a-b19d-4162-bd4d-39002a6c721d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "grid = make_grid(batch_images, nrow=4, pad_value=1)[0]\n",
    "plt.imshow(grid)\n",
    "plt.title(\"A minibatch of training images\")\n",
    "plt.axis(\"off\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52b59874-365a-4463-9a9c-544061603b89",
   "metadata": {},
   "source": [
    "## 5. Basic training algorithm\n",
    "\n",
    "Now that we have the model and the training loader, we can train the CNN.\n",
    "We use cross-entropy loss and optimize model parameters with mini-batch gradient descent.\n",
    "\n",
    "At each mini-batch, training follows the same four steps:\n",
    "1. forward pass (compute logits)\n",
    "2. compute loss\n",
    "3. backward pass (compute gradients)\n",
    "4. optimizer step (update parameters)\n",
    "\n",
    "This first version focuses only on parameter updates from the training set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4246aea0-5072-4c3a-bde2-436fc443eb4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the network to train\n",
    "cnn = TestCNN()\n",
    "# Move the network to the device (GPU if available, CPU otherwise)\n",
    "cnn.to(device)\n",
    "\n",
    "# We create an Adam optimizer instead of a Stochastic Gradient Descent (SGD) optimizer\n",
    "# Adam is an extension of SGD with improved training speed and stability.\n",
    "# The optimizer will automatically update the network's parameters\n",
    "optimizer = th.optim.Adam(cnn.parameters(), lr=1e-4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8339f11b-a54b-4a0f-ad61-88efb527fd78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create the loss function\n",
    "# Since TestCNN outputs logits, we will use nn.CrossEntropyLoss\n",
    "loss_function = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7f9f33f1-64c4-45cd-adc2-3c3e8f6829ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0...\n",
      "Epoch 1...\n",
      "Epoch 2...\n",
      "Epoch 3...\n",
      "Epoch 4...\n",
      "Epoch 5...\n",
      "Epoch 6...\n",
      "Epoch 7...\n",
      "Epoch 8...\n",
      "Epoch 9...\n"
     ]
    }
   ],
   "source": [
    "MAX_EPOCHS = 10   # Maximum number of training epochs\n",
    "\n",
    "# History of training losses, for plotting\n",
    "losses_train = []\n",
    "\n",
    "for epoch in range(MAX_EPOCHS):\n",
    "    print(f\"Epoch {epoch}...\")\n",
    "\n",
    "    # --- Training phase ---\n",
    "    cnn.train()  # Set the CNN in training mode\n",
    "\n",
    "    # We run an entire epoch\n",
    "    for images, labels in loader_train:\n",
    "        # Move batch to GPU (if available)\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Compute predictions\n",
    "        pred_logits = cnn(images)\n",
    "        # Compute loss averaged over the minibatch\n",
    "        loss = loss_function(pred_logits, labels)\n",
    "\n",
    "        # Remove existing gradients\n",
    "        optimizer.zero_grad()\n",
    "        # Compute the gradient (backpropagation)\n",
    "        loss.backward()\n",
    "        # Update parameters\n",
    "        optimizer.step()\n",
    "\n",
    "        # Store current loss\n",
    "        losses_train.append(loss.item())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "756e9352-ce04-41da-9543-0db70dbd62a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training loss vs iteration\n",
    "train_iters = list(range(len(losses_train)))\n",
    "\n",
    "plt.figure(figsize=(8, 4.5))\n",
    "plt.plot(train_iters, losses_train, label=\"Train loss\", linewidth=0.2)\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.title(\"Training loss vs iteration\")\n",
    "plt.grid()\n",
    "plt.legend();\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bb5669a-89a5-4d62-9240-0064b0c45b3a",
   "metadata": {},
   "source": [
    "## 6. Data augmentation pipeline\n",
    "\n",
    "The basic training loop above is intentionally minimal to show the core algorithm.\n",
    "In practice, training usually includes data augmentation to improve generalization.\n",
    "\n",
    "We define a data augmentation pipeline for training images.\n",
    "At each mini-batch, random label-preserving transformations (for example, small rotations or flips)\n",
    "create different valid variants of the same cases.\n",
    "This increases effective data diversity and helps the network generalize better without collecting new data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "df656983-2c6f-4926-81e6-ffd282894b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "augm_transforms = transforms.Compose([\n",
    "    # Random flip with prob 0.5\n",
    "    # transforms.RandomHorizontalFlip(),\n",
    "    # Random rotation with -10 + 10 degrees\n",
    "    transforms.RandomRotation(10),\n",
    "    # Convert PIL to a float PyTorch tensor in range [0.0, 1.0]\n",
    "    # This is very important for CNNs, input data should be roughly in the range (-1.0, 1.0), never (0, 255)\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "# We can create much more extreme augmentations\n",
    "# augm_strong_transforms = transforms.Compose([\n",
    "#     transforms.RandomHorizontalFlip(p=0.5),\n",
    "#     transforms.RandomAffine(\n",
    "#         degrees=(-10, 10),\n",
    "#         translate=(0.05, 0.05),\n",
    "#         scale=(0.9, 1.1),\n",
    "#         # shear=10,\n",
    "#     ),\n",
    "#     transforms.ColorJitter(\n",
    "#         brightness=0.25,\n",
    "#         contrast=0.25,\n",
    "#     ),\n",
    "#     transforms.RandomAdjustSharpness(sharpness_factor=2, p=0.3),\n",
    "#     transforms.RandomAutocontrast(p=0.3),\n",
    "#     transforms.ToTensor(),\n",
    "# ])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4648c553-a546-4bcb-81cb-9e4555934d9b",
   "metadata": {},
   "source": [
    "It is very important that CNN inputs are numerically normalized, typically to `[0,1]` or around `[-1,1]`.\n",
    "\n",
    "Feeding raw `[0,255]` values is usually a bad idea because optimization becomes less stable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4684b0c1-a2c2-43c9-af01-6438c23998c9",
   "metadata": {},
   "source": [
    "We now reload the datasets providing the data augmentation pipelines in the `transform` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "64074e35-ed58-4c96-8232-ce6183028316",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4708 samples in training dataset\n"
     ]
    }
   ],
   "source": [
    "# Training data\n",
    "data_train = PneumoniaMNIST(\n",
    "    split=\"train\",\n",
    "    size=IMAGE_SIZE,\n",
    "    download=True,\n",
    "    transform=augm_transforms,\n",
    "    target_transform=lambda label: label.item()\n",
    ")\n",
    "\n",
    "print(f\"{len(data_train)} samples in training dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc5944ef-22c7-4671-b4d2-98fe7d7d6c5c",
   "metadata": {},
   "source": [
    "We recreate the data loader for the new dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "df7afda7-9b12-4ddb-9a26-01286711bcc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader_train = DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ac1237fe-e969-4736-82c4-01b24bab2966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_images, batch_labels = next(iter(loader_train))\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "grid = make_grid(batch_images, nrow=4, pad_value=1)[0]\n",
    "plt.imshow(grid)\n",
    "plt.title(\"A minibatch of training images with data augmentation\")\n",
    "plt.axis(\"off\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d8822c-ab7d-48ff-9c29-12f2aa5c6679",
   "metadata": {},
   "source": [
    "## 7. Full training procedure (data augmentation + early stopping)\n",
    "\n",
    "In this section, we move from the minimal training loop to a practical training workflow that is closer to real projects. We train with data augmentation to improve generalization, evaluate performance on a separate validation set after each epoch, save model checkpoints, and apply early stopping to prevent unnecessary training once validation performance stops improving.\n",
    "\n",
    "To that end, we first load a validation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f5de98f5-e03b-46ea-8ca5-04596ce777c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "524 samples in validation dataset\n"
     ]
    }
   ],
   "source": [
    "# Validation data\n",
    "data_val = PneumoniaMNIST(\n",
    "    split=\"val\",\n",
    "    size=IMAGE_SIZE,\n",
    "    download=True,\n",
    "    # Validation dataset does NOT use data augmentation\n",
    "    transform=transforms.ToTensor(),\n",
    "    target_transform=lambda label: label.item()\n",
    ")\n",
    "\n",
    "# Create DataLoader for validation dataset.\n",
    "# We keep a fixed order (`shuffle=False`) because no parameter updates are performed with validation data.\n",
    "loader_val = DataLoader(data_val, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "print(f\"{len(data_val)} samples in validation dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "560bdd26-dd8f-4c36-b9a4-42a116befc6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(cnn: nn.Module, loader: DataLoader):\n",
    "    \"\"\"Run inference on all batches of a loader and return model outputs and true labels.\"\"\"\n",
    "    \n",
    "    # Set CNN in evaluation mode (important for BatchNorm/Dropout behavior)\n",
    "    cnn.eval()\n",
    "\n",
    "    preds = []\n",
    "    labels = []\n",
    "\n",
    "    with th.no_grad():\n",
    "        for batch_images, batch_labels in loader:\n",
    "            batch_images = batch_images.to(device)\n",
    "            batch_labels = batch_labels.to(device)\n",
    "\n",
    "            batch_logits = cnn(batch_images)\n",
    "            preds.append(batch_logits)\n",
    "            labels.append(batch_labels)\n",
    "\n",
    "    preds = th.cat(preds, dim=0)\n",
    "    labels = th.cat(labels, dim=0)\n",
    "\n",
    "    return preds, labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2dd1c6dc-57e1-4bdc-9aac-d4cdccafd98e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the network to train\n",
    "cnn = TestCNN()\n",
    "# Move the network to the device (GPU if available, CPU otherwise)\n",
    "cnn.to(device)\n",
    "\n",
    "# Optimizer\n",
    "optimizer = th.optim.Adam(cnn.parameters(), lr=1e-4)\n",
    "\n",
    "# Loss function\n",
    "loss_function = nn.CrossEntropyLoss()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c7b1b595-67ea-4659-b1aa-2854285d7141",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0...\n",
      "  val loss: 0.5594\n",
      "Epoch 1...\n",
      "  val loss: 0.5398\n",
      "Epoch 2...\n",
      "  val loss: 0.4593\n",
      "Epoch 3...\n",
      "  val loss: 0.3729\n",
      "Epoch 4...\n",
      "  val loss: 0.3283\n",
      "Epoch 5...\n",
      "  val loss: 0.2653\n",
      "Epoch 6...\n",
      "  val loss: 0.2551\n",
      "Epoch 7...\n",
      "  val loss: 0.2493\n",
      "Epoch 8...\n",
      "  val loss: 0.2648\n",
      "Epoch 9...\n",
      "  val loss: 0.2623\n",
      "Best validation loss: 0.2493 at epoch 7\n"
     ]
    }
   ],
   "source": [
    "MAX_EPOCHS = 10   # Maximum number of training epochs\n",
    "PATIENCE = 5      # Stop if validation loss does not improve for 5 epochs\n",
    "\n",
    "# History of training and validation losses, for plotting\n",
    "losses_train = []\n",
    "losses_val = []\n",
    "\n",
    "# Parameters for early stopping\n",
    "best_val_loss = float(\"inf\")  # Best validation loss found so far\n",
    "bad_epochs = 0                # Number of consecutive epochs where validation loss did not improve\n",
    "best_epoch = -1               # Epoch with best validation loss so far\n",
    "\n",
    "# Keep track of the total number of training iterations (for plotting)\n",
    "n_iterations = 0\n",
    "\n",
    "for epoch in range(MAX_EPOCHS):\n",
    "    print(f\"Epoch {epoch}...\")\n",
    "\n",
    "    # --- Training phase ---\n",
    "    cnn.train()  # Set the CNN in training mode\n",
    "    epoch_train_loss = 0.0\n",
    "    n_train_batches = 0\n",
    "\n",
    "    # We run an entire epoch\n",
    "    for images, labels in loader_train:\n",
    "        # Move batch to GPU (if available)\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        # Compute predictions\n",
    "        pred_logits = cnn(images)\n",
    "        # Compute loss averaged over the minibatch\n",
    "        loss = loss_function(pred_logits, labels)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        # Compute the gradient\n",
    "        loss.backward()\n",
    "        # Update parameters\n",
    "        optimizer.step()\n",
    "\n",
    "        n_iterations += 1\n",
    "\n",
    "        # Store current loss\n",
    "        losses_train.append(loss.item())\n",
    "\n",
    "    # --- Validation phase ---\n",
    "    val_logits, val_labels = evaluate_model(cnn, loader_val)\n",
    "    val_loss = loss_function(val_logits, val_labels)\n",
    "    val_loss_item = val_loss.item()\n",
    "\n",
    "    print(f\"  val loss: {val_loss_item:.4f}\")\n",
    "    # Store validation loss for plotting\n",
    "    losses_val.append((n_iterations - 1, val_loss_item))\n",
    "\n",
    "    # Checkpoint for this epoch\n",
    "    th.save(cnn.state_dict(), f\"checkpoint_{epoch:03d}.pth\")\n",
    "\n",
    "    # Early stopping logic\n",
    "    if val_loss_item < best_val_loss:\n",
    "        # If we improved the best validation loss so far, save model and reset bad epochs\n",
    "        best_val_loss = val_loss_item\n",
    "        best_epoch = epoch\n",
    "        bad_epochs = 0\n",
    "        th.save(cnn.state_dict(), \"checkpoint_best.pth\")\n",
    "    else:\n",
    "        # Otherwise, increase bad epochs\n",
    "        bad_epochs += 1\n",
    "\n",
    "    # If the number of bad epochs passed the patience, early stop\n",
    "    if bad_epochs >= PATIENCE:\n",
    "        print(f\"Early stopping at epoch {epoch} (best epoch: {best_epoch})\")\n",
    "        break\n",
    "\n",
    "print(f\"Best validation loss: {best_val_loss:.4f} at epoch {best_epoch}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3ba4ea61-62fa-439a-8141-248d19fb9cd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training and validation loss vs iteration\n",
    "train_iters = list(range(len(losses_train)))\n",
    "val_iters = [it for it, _ in losses_val]\n",
    "val_loss_values = [lv for _, lv in losses_val]\n",
    "\n",
    "plt.figure(figsize=(8, 4.5))\n",
    "plt.plot(train_iters, losses_train, label=\"Train loss\", linewidth=0.2)\n",
    "plt.plot(val_iters, val_loss_values, 'o-', label=\"Validation loss\", linewidth=2, markersize=4)\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.title(\"Training and validation loss vs iteration\")\n",
    "plt.grid()\n",
    "plt.legend();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f1d278-0dc1-4bb3-be17-cfadf12f00ac",
   "metadata": {},
   "source": [
    "## 8. Evaluation\n",
    "\n",
    "After training is finished, we evaluate the model on the test set.\n",
    "In this section, we compute test accuracy, confusion-matrix-derived metrics, and ROC-AUC.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b20327dd-1890-406b-a368-9047aba372da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "624 samples in test dataset\n"
     ]
    }
   ],
   "source": [
    "# Create test dataset and loader (no data augmentation)\n",
    "data_test = PneumoniaMNIST(\n",
    "    split=\"test\",\n",
    "    size=IMAGE_SIZE,\n",
    "    download=True,\n",
    "    transform=transforms.ToTensor(),\n",
    "    target_transform=lambda label: label.item()\n",
    ")\n",
    "loader_test = DataLoader(data_test, batch_size=32, shuffle=False)\n",
    "\n",
    "print(f\"{len(data_test)} samples in test dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d177cd0",
   "metadata": {},
   "source": [
    "We now evaluate the trained model on the test set.\n",
    "\n",
    "Important: for evaluation we switch the model to `eval()` mode and disable gradient computation with `torch.no_grad()`.\n",
    "This makes inference correct and more efficient.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "99f3d82e-c176-4833-bb1c-7c902cf7bf9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive samples: 390\n",
      "Negative samples: 234\n",
      "Total samples:    624\n"
     ]
    }
   ],
   "source": [
    "# Class distribution in the test set\n",
    "labels_test = data_test.labels.ravel()\n",
    "n_pos = int((labels_test == 1).sum())\n",
    "n_neg = int((labels_test == 0).sum())\n",
    "\n",
    "print(f\"Positive samples: {n_pos}\")\n",
    "print(f\"Negative samples: {n_neg}\")\n",
    "print(f\"Total samples:    {len(labels_test)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "632c3180",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.8814\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Ensure model is in evaluation mode\n",
    "cnn.eval()\n",
    "\n",
    "# Load best checkpoint before final test evaluation (if available)\n",
    "if Path(\"checkpoint_best.pth\").exists():\n",
    "    cnn.load_state_dict(th.load(\"checkpoint_best.pth\", map_location=\"cpu\"))\n",
    "\n",
    "# Evaluate the model with test data\n",
    "logits_pred, y_true = evaluate_model(cnn, loader_test)\n",
    "y_pred = th.argmax(logits_pred, dim=1)\n",
    "\n",
    "# Compute accuracy\n",
    "accuracy = (y_pred == y_true).float().mean().item()\n",
    "print(f\"Test accuracy: {accuracy:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "04f4720e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP=357, TN=193, FP=41, FN=33\n",
      "Sensitivity (Recall): 0.9154\n",
      "Specificity:          0.8248\n",
      "Precision:            0.8970\n",
      "F1-score:             0.9061\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix entries for binary classification\n",
    "TP = ((y_pred == 1) & (y_true == 1)).sum().item()\n",
    "TN = ((y_pred == 0) & (y_true == 0)).sum().item()\n",
    "FP = ((y_pred == 1) & (y_true == 0)).sum().item()\n",
    "FN = ((y_pred == 0) & (y_true == 1)).sum().item()\n",
    "\n",
    "print(f\"TP={TP}, TN={TN}, FP={FP}, FN={FN}\")\n",
    "\n",
    "sensitivity = TP / (TP + FN)\n",
    "specificity = TN / (TN + FP)\n",
    "precision = TP / (TP + FP)\n",
    "f1 = (2 * precision * sensitivity) / (precision + sensitivity)\n",
    "\n",
    "print(f\"Sensitivity (Recall): {sensitivity:.4f}\")\n",
    "print(f\"Specificity:          {specificity:.4f}\")\n",
    "print(f\"Precision:            {precision:.4f}\")\n",
    "print(f\"F1-score:             {f1:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1453bcf0",
   "metadata": {},
   "source": [
    "### ROC curve\n",
    "\n",
    "For binary classification, we can inspect performance across all decision thresholds using the ROC curve.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6c7da73b-53be-42cc-a726-868e692c548d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC-AUC: 0.9373\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# Convert PyTorch tensors to NumPy\n",
    "y_true_np = y_true.cpu().numpy()\n",
    "y_score_np = (logits_pred[:, 1] - logits_pred[:, 0]).cpu().numpy()\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_true_np, y_score_np)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(5.0, 5.0))\n",
    "plt.plot(fpr, tpr, linewidth=2, label=f\"ROC (AUC={roc_auc:.3f})\")\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=1, label='Random')\n",
    "plt.xlim(0.0, 1.0)\n",
    "plt.ylim(0.0, 1.0)\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "plt.title('ROC curve')\n",
    "plt.grid()\n",
    "plt.legend(loc='lower right');\n",
    "\n",
    "print(f\"ROC-AUC: {roc_auc:.4f}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f266e3b-7ef9-4743-b256-62fcfd4f95b4",
   "metadata": {},
   "source": [
    "## 9. Advanced components\n",
    "\n",
    "So far we used a plain CNN baseline.\n",
    "Next, we introduce two common design components used in modern architectures:\n",
    "\n",
    "- **Batch normalization**\n",
    "- **Skip connections** (residual learning)\n",
    "\n",
    "We use both components in a `ResidualBlock` that can be used as a layer of your CNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3344039e-ef45-47d2-9e19-9d3219a2a5cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResidualBlock(nn.Module):\n",
    "    \"\"\"Basic residual block: Conv-BN-ReLU -> Conv-BN -> add skip -> ReLU.\"\"\"\n",
    "\n",
    "    def __init__(self, in_channels: int, out_channels: int, stride: int = 1):\n",
    "        super().__init__()\n",
    "\n",
    "        self.conv1 = nn.Conv2d(\n",
    "            in_channels=in_channels,\n",
    "            out_channels=out_channels,\n",
    "            kernel_size=3,\n",
    "            stride=stride,\n",
    "            padding=1,\n",
    "            # No bias before BatchNorm\n",
    "            bias=False,\n",
    "        )\n",
    "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
    "\n",
    "        self.conv2 = nn.Conv2d(\n",
    "            in_channels=out_channels,\n",
    "            out_channels=out_channels,\n",
    "            kernel_size=3,\n",
    "            stride=1,\n",
    "            padding=1,\n",
    "            # No bias before BatchNorm\n",
    "            bias=False,\n",
    "        )\n",
    "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
    "\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "        # Projection shortcut when spatial size or channels change\n",
    "        if stride != 1 or in_channels != out_channels:\n",
    "            self.skip = nn.Sequential(\n",
    "                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(out_channels),\n",
    "            )\n",
    "        else:\n",
    "            self.skip = nn.Identity()\n",
    "\n",
    "    def forward(self, x: th.Tensor) -> th.Tensor:\n",
    "        # Skip connection\n",
    "        identity = self.skip(x)\n",
    "\n",
    "        # Residual branch\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "\n",
    "        # Join two branches\n",
    "        out = out + identity\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "55fc3bfd-2f8a-4edc-8496-cd32e26c47aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out.shape=torch.Size([4, 2])\n",
      "Number of parameters: 77490\n"
     ]
    }
   ],
   "source": [
    "class SimpleResNet(nn.Module):\n",
    "    \"\"\"Small ResNet-style CNN for binary classification.\"\"\"\n",
    "\n",
    "    def __init__(self, num_classes: int = 2):\n",
    "        super().__init__()\n",
    "\n",
    "        # First conv layer (before residual blocks)\n",
    "        self.stem = nn.Sequential(\n",
    "            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2, bias=False),\n",
    "            nn.BatchNorm2d(16),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "\n",
    "        self.residual_blocks = nn.Sequential(\n",
    "            ResidualBlock(16, 16, stride=1),\n",
    "            ResidualBlock(16, 32, stride=2),\n",
    "            ResidualBlock(32, 64, stride=2),\n",
    "        )\n",
    "\n",
    "        self.gap = nn.AdaptiveAvgPool2d((1, 1))\n",
    "        self.fc = nn.Linear(64, num_classes)\n",
    "\n",
    "    def forward(self, x: th.Tensor) -> th.Tensor:\n",
    "        x = self.stem(x)\n",
    "        x = self.residual_blocks(x)\n",
    "        x = self.gap(x)\n",
    "        x = th.flatten(x, 1)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "# Quick shape check\n",
    "resnet = SimpleResNet(num_classes=2)\n",
    "out = resnet(th.randn(4, 1, IMAGE_SIZE, IMAGE_SIZE))\n",
    "print(f\"{out.shape=}\")\n",
    "print(\"Number of parameters:\", sum(p.numel() for p in resnet.parameters()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79ed70de-cb60-40d3-adb2-d9cd50643611",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
