{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7349af0f-a853-451f-8594-2f4da5def776",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "# Identifying hand-written digits (MNIST database) using PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b48d1e9d-113c-4936-8e7f-d325beefdd66",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Implementation Steps \n",
    "1. **Library & Dataset Setup**   \n",
    "The necessary libraries were imported & the MNIST dataset is downloaded using torchvision. The dataset is split into training & testing sets, & dataloaders are created to manage the input pipeline efficiently.\n",
    "\n",
    "2. **Hyperparameter Initialization**  \n",
    "Key training parameters such as input size, number of output classes, number of epochs, batch size, & learning rate are defined. Each image in MNIST is 28×28 pixels, leading to an input size of 784.\n",
    "\n",
    "3. **Model Definition**    \n",
    "A logistic regression model and a multi-layer perceptron (MLP) are defined using PyTorch’s module system. \n",
    "\n",
    "4. **Loss Function & Optimizer**    \n",
    "The cross-entropy loss function is selected for its suitability in multi-class classification. The stochastic gradient descent algorithm is used for optimizing the model weights.\n",
    "\n",
    "5. **Model Training**  \n",
    "The models are trained for five epochs. Each epoch involved iterating through batches of the training data. For each batch, the model performed a forward pass, calculated the loss, backpropagated the gradients, & updated weights using the optimizer.\n",
    "\n",
    "6. **Model Evaluation**    \n",
    "After training, the models are evaluated on the test dataset. The evaluation loop measured the number of correct predictions out of total test samples to calculate the final accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca38130d-a1c2-47f1-b909-be72ee349191",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## 1. Library & Dataset Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34cdced0-51fb-409d-9e0f-27a378f3922e",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision ## Contains some utilities for working with the image data\n",
    "from torchvision.datasets import MNIST\n",
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib inline\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import random_split\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adff58d3-2097-47ab-a9b6-f697e52a0b88",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"HandDigits.png\", width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9615599c-b17c-4ffe-99a7-4a66ecb64447",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### MNIST Dataset\n",
    "#### Training data: 60,000 images, testing data: 10,000 images with 28 $\\times$ 28 pixels. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "793cc6d1-b84d-4f06-8259-0f07da4ef0f7",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### Downloading the MNIST dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3cce6b39-34fe-44ba-8860-926203bf739b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000\n"
     ]
    }
   ],
   "source": [
    "### load the MNIST dataset \n",
    "dataset = MNIST(root = 'data/', download = True)\n",
    "print(len(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "708e3b83-5e51-48ce-8202-577334b696ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torchvision.datasets.mnist.MNIST"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fd2889dd-a116-4625-8052-7f7ce72ece8c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label: 3\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image, label = dataset[10]\n",
    "plt.imshow(image, cmap = 'gray')\n",
    "print('Label:', label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7e8dc65-fcb2-448e-8838-645e32d3dc18",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label: 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaEAAAGdCAYAAAC7EMwUAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAGohJREFUeJzt3XtsU+f9x/GPoeACTSxlkNgZIYoq0KZCYVzKRZSbRES2IijdBHQq4R9Ex0VlaYfGoCObJlKhgrotg23dxkCFgbRSylRWmikksFGmlIuKWIVAhJGKZBkRs0OgRsDz+wPhX01C4Bibb+y8X9Ij4XPOl/PN4SGfPLF97HPOOQEAYKCHdQMAgO6LEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAICZx6wbuNutW7d08eJFZWVlyefzWbcDAPDIOafW1lbl5+erR4/O1zpdLoQuXryogoIC6zYAAA+poaFBAwcO7PSYLvfruKysLOsWAABJ8CDfz1MWQps2bVJRUZEef/xxjRo1SocOHXqgOn4FBwCZ4UG+n6ckhHbt2qUVK1Zo9erVOn78uJ599lmVlJTowoULqTgdACBN+VJxF+2xY8dq5MiR2rx5c2zb17/+dc2ePVsVFRWd1kYiEQUCgWS3BAB4xMLhsLKzszs9JukroevXr+vo0aMqLi6O215cXKzDhw+3Oz4ajSoSicQNAED3kPQQunTpkm7evKm8vLy47Xl5eWpqamp3fEVFhQKBQGzwyjgA6D5S9sKEu5+Qcs51+CTVqlWrFA6HY6OhoSFVLQEAupikv0+of//+6tmzZ7tVT3Nzc7vVkST5/X75/f5ktwEASANJXwn17t1bo0aNUlVVVdz2qqoqTZgwIdmnAwCksZTcMaGsrEwvvfSSRo8erfHjx+u3v/2tLly4oJdffjkVpwMApKmUhNDcuXPV0tKin/70p2psbNTQoUO1b98+FRYWpuJ0AIA0lZL3CT0M3icEAJnB5H1CAAA8KEIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmHnMugEAD2bUqFGea5YtW5bQuRYsWOC5Ztu2bZ5rfvnLX3quOXbsmOcadF2shAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJjxOeecdRNfFolEFAgErNsAUmrEiBGea6qrqz3XZGdne655lMLhsOear3zlKynoBKkQDofvOwdZCQEAzBBCAAAzSQ+h8vJy+Xy+uBEMBpN9GgBABkjJh9o99dRT+tvf/hZ73LNnz1ScBgCQ5lISQo899hirHwDAfaXkOaEzZ84oPz9fRUVFmjdvns6dO3fPY6PRqCKRSNwAAHQPSQ+hsWPHatu2bdq/f7/efvttNTU1acKECWppaenw+IqKCgUCgdgoKChIdksAgC4q5e8Tamtr05NPPqmVK1eqrKys3f5oNKpoNBp7HIlECCJkPN4ndBvvE8psD/I+oZQ8J/Rl/fr107Bhw3TmzJkO9/v9fvn9/lS3AQDoglL+PqFoNKrPPvtMoVAo1acCAKSZpIfQa6+9ptraWtXX1+uf//ynvv3tbysSiai0tDTZpwIApLmk/zru888/1/z583Xp0iUNGDBA48aN05EjR1RYWJjsUwEA0hw3MAUe0jPPPOO55t133/Vck5+f77km0f/era2tnmuuX7/uuSaRFxlMnDjRc82xY8c810iJfU34f9zAFADQpRFCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADCT8g+1Ayz07ds3obqRI0d6rnnnnXc813T1z9e614dQdmb9+vWea3bu3Om55h//+IfnmjVr1niukaSKioqE6vDgWAkBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMxwF21kpN/85jcJ1c2fPz/JnaSnRO4m/sQTT3iuqa2t9VwzZcoUzzVPP/205xo8GqyEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmOEGpujyRo0a5bnmW9/6VkLn8vl8CdV5lciNO//yl794rnnzzTc910jSxYsXPdccP37cc83ly5c910ybNs1zzaP6d4V3rIQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCY8TnnnHUTXxaJRBQIBKzbQIqMGDHCc011dbXnmuzsbM81ifrrX//quWb+/PmeayZPnuy55umnn/ZcI0m/+93vPNf897//TehcXt28edNzzdWrVxM6VyLX/NixYwmdKxOFw+H7/l9kJQQAMEMIAQDMeA6hgwcPaubMmcrPz5fP59OePXvi9jvnVF5ervz8fPXp00dTpkzRqVOnktUvACCDeA6htrY2DR8+XJWVlR3uX79+vTZu3KjKykrV1dUpGAxq+vTpam1tfehmAQCZxfMnq5aUlKikpKTDfc45vfXWW1q9erXmzJkjSdq6davy8vK0Y8cOLV68+OG6BQBklKQ+J1RfX6+mpiYVFxfHtvn9fk2ePFmHDx/usCYajSoSicQNAED3kNQQampqkiTl5eXFbc/Ly4vtu1tFRYUCgUBsFBQUJLMlAEAXlpJXx/l8vrjHzrl22+5YtWqVwuFwbDQ0NKSiJQBAF+T5OaHOBINBSbdXRKFQKLa9ubm53eroDr/fL7/fn8w2AABpIqkroaKiIgWDQVVVVcW2Xb9+XbW1tZowYUIyTwUAyACeV0JXrlzR2bNnY4/r6+t14sQJ5eTkaNCgQVqxYoXWrVunwYMHa/DgwVq3bp369u2rF198MamNAwDSn+cQ+uSTTzR16tTY47KyMklSaWmp/vjHP2rlypW6du2alixZosuXL2vs2LH66KOPlJWVlbyuAQAZgRuYImFDhgzxXLN27VrPNfPmzfNcc+nSJc81ktTY2Oi55mc/+5nnmj//+c+ea3BbIjcwTfTb3K5duzzXfPe7303oXJmIG5gCALo0QggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAICZpH6yKtJTop9s++abb3qu+eY3v+m5prW11XPNggULPNdItz+qxKs+ffokdC50fYMGDbJuIeOxEgIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGG5hC3/jGNxKqS+RmpImYNWuW55ra2toUdAIg2VgJAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMMMNTKGNGzcmVOfz+TzXJHJjUW5Gii/r0cP7z863bt1KQSdIBlZCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzHAD0wzz3HPPea4ZMWJEQudyznmu2bt3b0LnAu5I5GakicxVSTpx4kRCdXhwrIQAAGYIIQCAGc8hdPDgQc2cOVP5+fny+Xzas2dP3P6FCxfK5/PFjXHjxiWrXwBABvEcQm1tbRo+fLgqKyvvecyMGTPU2NgYG/v27XuoJgEAmcnzCxNKSkpUUlLS6TF+v1/BYDDhpgAA3UNKnhOqqalRbm6uhgwZokWLFqm5ufmex0ajUUUikbgBAOgekh5CJSUl2r59u6qrq7VhwwbV1dVp2rRpikajHR5fUVGhQCAQGwUFBcluCQDQRSX9fUJz586N/Xno0KEaPXq0CgsL9cEHH2jOnDntjl+1apXKyspijyORCEEEAN1Eyt+sGgqFVFhYqDNnznS43+/3y+/3p7oNAEAXlPL3CbW0tKihoUGhUCjVpwIApBnPK6ErV67o7Nmzscf19fU6ceKEcnJylJOTo/Lycr3wwgsKhUI6f/68fvSjH6l///56/vnnk9o4ACD9eQ6hTz75RFOnTo09vvN8TmlpqTZv3qyTJ09q27Zt+t///qdQKKSpU6dq165dysrKSl7XAICM4DmEpkyZ0unNAPfv3/9QDeHh9OnTx3NN7969EzpXZy+9v5ddu3YldC50fYk8t1teXp78RjpQXV2dUN2qVauS3Anuxr3jAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmUv7Jqshc0WjUc01jY2MKOkGyJXJH7DVr1niu+cEPfuC55vPPP/dcs2HDBs810u3PT0NqsRICAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhuYImF79+61bgH3MWLEiITqErmx6Ny5cz3XvP/++55rXnjhBc816LpYCQEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADDDDUwzjM/neyQ1kjR79mzPNa+88kpC54L0/e9/33PN66+/ntC5AoGA55rt27d7rlmwYIHnGmQWVkIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMcAPTDOOceyQ1khQMBj3X/OIXv/Bc84c//MFzTUtLi+caSRo3bpznmpdeeslzzfDhwz3XDBw40HPNhQsXPNdI0v79+z3XbNq0KaFzoXtjJQQAMEMIAQDMeAqhiooKjRkzRllZWcrNzdXs2bN1+vTpuGOccyovL1d+fr769OmjKVOm6NSpU0ltGgCQGTyFUG1trZYuXaojR46oqqpKN27cUHFxsdra2mLHrF+/Xhs3blRlZaXq6uoUDAY1ffp0tba2Jr15AEB68/TChA8//DDu8ZYtW5Sbm6ujR49q0qRJcs7prbfe0urVqzVnzhxJ0tatW5WXl6cdO3Zo8eLFyescAJD2Huo5oXA4LEnKycmRJNXX16upqUnFxcWxY/x+vyZPnqzDhw93+HdEo1FFIpG4AQDoHhIOIeecysrKNHHiRA0dOlSS1NTUJEnKy8uLOzYvLy+2724VFRUKBAKxUVBQkGhLAIA0k3AILVu2TJ9++qn+9Kc/tdvn8/niHjvn2m27Y9WqVQqHw7HR0NCQaEsAgDST0JtVly9frr179+rgwYNxb6C78+bFpqYmhUKh2Pbm5uZ2q6M7/H6//H5/Im0AANKcp5WQc07Lli3T7t27VV1draKiorj9RUVFCgaDqqqqim27fv26amtrNWHChOR0DADIGJ5WQkuXLtWOHTv0/vvvKysrK/Y8TyAQUJ8+feTz+bRixQqtW7dOgwcP1uDBg7Vu3Tr17dtXL774Ykq+AABA+vIUQps3b5YkTZkyJW77li1btHDhQknSypUrde3aNS1ZskSXL1/W2LFj9dFHHykrKyspDQMAMofPJXr3yhSJRCIKBALWbaSt73znO55rOnpxSVfyn//8x3NNoi/1Hzx4cEJ1j8LHH3/suebAgQMJnevHP/5xQnXAl4XDYWVnZ3d6DPeOAwCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYSeiTVdF1JXKn5bq6uoTONWbMmITqvLrzib1e3OuTfFOhpaXFc83OnTs917zyyiuea4CujpUQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAMz7nnLNu4ssikYgCgYB1G91KKBRKqG7x4sWea9asWeO5xufzea5JdFr//Oc/91yzefNmzzVnz571XAOkm3A4rOzs7E6PYSUEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADDcwBQCkBDcwBQB0aYQQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMOMphCoqKjRmzBhlZWUpNzdXs2fP1unTp+OOWbhwoXw+X9wYN25cUpsGAGQGTyFUW1urpUuX6siRI6qqqtKNGzdUXFystra2uONmzJihxsbG2Ni3b19SmwYAZIbHvBz84Ycfxj3esmWLcnNzdfToUU2aNCm23e/3KxgMJqdDAEDGeqjnhMLhsCQpJycnbntNTY1yc3M1ZMgQLVq0SM3Nzff8O6LRqCKRSNwAAHQPPuecS6TQOadZs2bp8uXLOnToUGz7rl279MQTT6iwsFD19fV6/fXXdePGDR09elR+v7/d31NeXq6f/OQniX8FAIAuKRwOKzs7u/ODXIKWLFniCgsLXUNDQ6fHXbx40fXq1cu9++67He7/4osvXDgcjo2GhgYnicFgMBhpPsLh8H2zxNNzQncsX75ce/fu1cGDBzVw4MBOjw2FQiosLNSZM2c63O/3+ztcIQEAMp+nEHLOafny5XrvvfdUU1OjoqKi+9a0tLSooaFBoVAo4SYBAJnJ0wsTli5dqnfeeUc7duxQVlaWmpqa1NTUpGvXrkmSrly5otdee00ff/yxzp8/r5qaGs2cOVP9+/fX888/n5IvAACQxrw8D6R7/N5vy5Ytzjnnrl696oqLi92AAQNcr1693KBBg1xpaam7cOHCA58jHA6b/x6TwWAwGA8/HuQ5oYRfHZcqkUhEgUDAug0AwEN6kFfHce84AIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAICZLhdCzjnrFgAASfAg38+7XAi1trZatwAASIIH+X7uc11s6XHr1i1dvHhRWVlZ8vl8cfsikYgKCgrU0NCg7Oxsow7tcR1u4zrcxnW4jetwW1e4Ds45tba2Kj8/Xz16dL7WeewR9fTAevTooYEDB3Z6THZ2dreeZHdwHW7jOtzGdbiN63Cb9XUIBAIPdFyX+3UcAKD7IIQAAGbSKoT8fr/Wrl0rv99v3YoprsNtXIfbuA63cR1uS7fr0OVemAAA6D7SaiUEAMgshBAAwAwhBAAwQwgBAMykVQht2rRJRUVFevzxxzVq1CgdOnTIuqVHqry8XD6fL24Eg0HrtlLu4MGDmjlzpvLz8+Xz+bRnz564/c45lZeXKz8/X3369NGUKVN06tQpm2ZT6H7XYeHChe3mx7hx42yaTZGKigqNGTNGWVlZys3N1ezZs3X69Om4Y7rDfHiQ65Au8yFtQmjXrl1asWKFVq9erePHj+vZZ59VSUmJLly4YN3aI/XUU0+psbExNk6ePGndUsq1tbVp+PDhqqys7HD/+vXrtXHjRlVWVqqurk7BYFDTp0/PuPsQ3u86SNKMGTPi5se+ffseYYepV1tbq6VLl+rIkSOqqqrSjRs3VFxcrLa2ttgx3WE+PMh1kNJkPrg08cwzz7iXX345btvXvvY198Mf/tCoo0dv7dq1bvjw4dZtmJLk3nvvvdjjW7duuWAw6N54443Yti+++MIFAgH361//2qDDR+Pu6+Ccc6WlpW7WrFkm/Vhpbm52klxtba1zrvvOh7uvg3PpMx/SYiV0/fp1HT16VMXFxXHbi4uLdfjwYaOubJw5c0b5+fkqKirSvHnzdO7cOeuWTNXX16upqSlubvj9fk2ePLnbzQ1JqqmpUW5uroYMGaJFixapubnZuqWUCofDkqScnBxJ3Xc+3H0d7kiH+ZAWIXTp0iXdvHlTeXl5cdvz8vLU1NRk1NWjN3bsWG3btk379+/X22+/raamJk2YMEEtLS3WrZm58+/f3eeGJJWUlGj79u2qrq7Whg0bVFdXp2nTpikajVq3lhLOOZWVlWnixIkaOnSopO45Hzq6DlL6zIcudxftztz90Q7OuXbbMllJSUnsz8OGDdP48eP15JNPauvWrSorKzPszF53nxuSNHfu3Nifhw4dqtGjR6uwsFAffPCB5syZY9hZaixbtkyffvqp/v73v7fb153mw72uQ7rMh7RYCfXv3189e/Zs95NMc3Nzu594upN+/fpp2LBhOnPmjHUrZu68OpC50V4oFFJhYWFGzo/ly5dr7969OnDgQNxHv3S3+XCv69CRrjof0iKEevfurVGjRqmqqipue1VVlSZMmGDUlb1oNKrPPvtMoVDIuhUzRUVFCgaDcXPj+vXrqq2t7dZzQ5JaWlrU0NCQUfPDOadly5Zp9+7dqq6uVlFRUdz+7jIf7ncdOtJl54PhiyI82blzp+vVq5f7/e9/7/71r3+5FStWuH79+rnz589bt/bIvPrqq66mpsadO3fOHTlyxD333HMuKysr469Ba2urO378uDt+/LiT5DZu3OiOHz/u/v3vfzvnnHvjjTdcIBBwu3fvdidPnnTz5893oVDIRSIR486Tq7Pr0Nra6l599VV3+PBhV19f7w4cOODGjx/vvvrVr2bUdfje977nAoGAq6mpcY2NjbFx9erV2DHdYT7c7zqk03xImxByzrlf/epXrrCw0PXu3duNHDky7uWI3cHcuXNdKBRyvXr1cvn5+W7OnDnu1KlT1m2l3IEDB5ykdqO0tNQ5d/tluWvXrnXBYND5/X43adIkd/LkSdumU6Cz63D16lVXXFzsBgwY4Hr16uUGDRrkSktL3YULF6zbTqqOvn5JbsuWLbFjusN8uN91SKf5wEc5AADMpMVzQgCAzEQIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMDM/wHQwyWLQYEJCQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image, label = dataset[1]\n",
    "plt.imshow(image, cmap = 'gray')\n",
    "print('Label:', label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "754ce4d8-27b0-4e4b-9b5e-daf35d9a122b",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### Loading the MNIST data with transformation \n",
    "PyTorch doesn't know how to work with images. We need to convert the images into tensors. We can do this by specifying a transform while creating our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a249bec-f518-4f02-802c-53b1a2393145",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset MNIST\n",
      "    Number of datapoints: 60000\n",
      "    Root location: data/\n",
      "    Split: Train\n",
      "    StandardTransform\n",
      "Transform: ToTensor()\n"
     ]
    }
   ],
   "source": [
    "### Convert to tensors\n",
    "## MNIST dataset(images and labels)\n",
    "mnist_dataset = MNIST(root = 'data/', train = True, transform = transforms.ToTensor())\n",
    "print(mnist_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfeac65a-05c8-418c-b8e9-4b445d935590",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "##### The image is now convert to a 28 X 28 tensor. The first dimension is used to keep track of the color channels. Since images in the MNIST dataset are grayscale, there's just one channel. Other datasets have images with color, in that case the color channels would be 3(Red, Green, Blue)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18cf4a88-04b8-4ea6-9bb8-bfcb00270dd4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 28, 28]) 5\n"
     ]
    }
   ],
   "source": [
    "image_tensor, label = mnist_dataset[0]\n",
    "print(image_tensor.shape, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e9f0607-5e1c-46df-baa6-bb1a2fbcddc3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[0.0039, 0.6039, 0.9922, 0.3529, 0.0000],\n",
      "         [0.0000, 0.5451, 0.9922, 0.7451, 0.0078],\n",
      "         [0.0000, 0.0431, 0.7451, 0.9922, 0.2745],\n",
      "         [0.0000, 0.0000, 0.1373, 0.9451, 0.8824],\n",
      "         [0.0000, 0.0000, 0.0000, 0.3176, 0.9412]]])\n",
      "tensor(1.) tensor(0.)\n"
     ]
    }
   ],
   "source": [
    "print(image_tensor[:,10:15,10:15])\n",
    "print(torch.max(image_tensor), torch.min(image_tensor))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5067a5ac-bdc6-4641-ba9c-94f86c1c517d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x143eb7250>"
      ]
     },
     "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": [
    "## Plot the image of the tensor\n",
    "plt.imshow(image_tensor[0,0:27,0:27],cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "06d942f3-e9e2-478c-b4eb-5983c7abf67f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x143f3d310>"
      ]
     },
     "execution_count": 14,
     "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": [
    "## Plot the image of the tensor\n",
    "plt.imshow(image_tensor[0,10:15,10:15],cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815d05b7-c457-4759-bedd-d6448867ca51",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## 2. Hyperparameter Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ad47b705-6a39-463c-98d9-56adbab82b1a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# --- Hyperparameters ---\n",
    "n_trials = 4  ### repeat the experiment 4 times\n",
    "epochs = 40   ### number of training steps \n",
    "batch_size = 128 #### \n",
    "lr_logreg = 0.1\n",
    "lr_mlp = 1e-3\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "hidden1=256\n",
    "hidden2=128\n",
    "p_dropout=0.2\n",
    "#### \n",
    "input_size= 28 * 28\n",
    "num_classes=10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db01674-575b-4078-9c08-a65eaf2255a1",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### Training and Validation Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "511a4659-252e-41bd-bc5d-fbc254c55699",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of Train Datasets:  50000\n",
      "length of Validation Datasets:  10000\n"
     ]
    }
   ],
   "source": [
    "#### split training/validation \n",
    "train_data, validation_data = random_split(mnist_dataset, [50000, 10000])\n",
    "## Print the length of train and validation datasets\n",
    "print(\"length of Train Datasets: \", len(train_data))\n",
    "print(\"length of Validation Datasets: \", len(validation_data))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7b02ea4a-78f5-4efe-9f12-90ce402cc43a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
    "val_loader   = DataLoader(validation_data, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f0d221a-8913-4859-be8c-fa8c7ab19931",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## Define Logistic Regression Model in Pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "df6daa67-8036-4870-8a5e-11a5df6031f0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class LogisticRegression(nn.Module):\n",
    "    def __init__(self, input_size=input_size, num_classes=num_classes):\n",
    "        super().__init__()\n",
    "        self.linear = nn.Linear(input_size, num_classes)\n",
    "        self._init_weights()\n",
    "\n",
    "    def forward(self, xb):\n",
    "        xb = xb.view(xb.size(0), -1)   # flatten, device-safe\n",
    "        return self.linear(xb)\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # small normal init for weights, zero biases\n",
    "        nn.init.normal_(self.linear.weight, mean=0.0, std=0.01)\n",
    "        if self.linear.bias is not None:\n",
    "            nn.init.zeros_(self.linear.bias)\n",
    "\n",
    "    # --- training / validation helpers (device-safe) ---\n",
    "    def training_step(self, batch):\n",
    "        images, labels = batch\n",
    "        device = next(self.parameters()).device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        out = self(images)                       # logits\n",
    "        loss = F.cross_entropy(out, labels)      # scalar tensor\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch):\n",
    "        images, labels = batch\n",
    "        device = next(self.parameters()).device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        out = self(images)\n",
    "        loss = F.cross_entropy(out, labels)\n",
    "        preds = out.argmax(dim=1)\n",
    "        acc = (preds == labels).float().mean()   # tensor\n",
    "        return {'val_loss': loss.detach(), 'val_acc': acc.detach()}\n",
    "\n",
    "    def validation_epoch_end(self, outputs):\n",
    "        # outputs: list of {'val_loss': tensor, 'val_acc': tensor}\n",
    "        batch_losses = [x['val_loss'] for x in outputs]\n",
    "        epoch_loss = torch.stack(batch_losses).mean()\n",
    "        batch_accs = [x['val_acc'] for x in outputs]\n",
    "        epoch_acc = torch.stack(batch_accs).mean()\n",
    "        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n",
    "\n",
    "    def epoch_end(self, epoch, result):\n",
    "        print(f\"Epoch [{epoch}], val_loss: {result['val_loss']:.4f}, val_acc: {result['val_acc']:.4f}\")\n",
    "modelL = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b356235c-962f-46fe-9a0c-db5b0d588064",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## MLP with 2 hidden layer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fdbd8441-05b4-45c3-b313-468e96c12440",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self, input_size=input_size, hidden1=hidden1, hidden2=hidden2, num_classes=num_classes, p_dropout=p_dropout):\n",
    "        super().__init__()\n",
    "        # Layers\n",
    "        self.fc1 = nn.Linear(input_size, hidden1)\n",
    "        self.bn1 = nn.BatchNorm1d(hidden1)\n",
    "        self.fc2 = nn.Linear(hidden1, hidden2)\n",
    "        self.bn2 = nn.BatchNorm1d(hidden2)\n",
    "        self.fc3 = nn.Linear(hidden2, num_classes)\n",
    "        self.dropout = nn.Dropout(p_dropout)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "\n",
    "        # weight init\n",
    "        self._init_weights()\n",
    "\n",
    "    def forward(self, xb):\n",
    "        xb = xb.view(xb.size(0), -1)         # flatten\n",
    "        x = self.fc1(xb)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.dropout(x)\n",
    "\n",
    "        x = self.fc2(x)\n",
    "        x = self.bn2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.dropout(x)\n",
    "\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # He/Kaiming init for linear layers and sensible BN init\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Linear):\n",
    "                nn.init.kaiming_normal_(m.weight, nonlinearity='relu')\n",
    "                if m.bias is not None:\n",
    "                    nn.init.zeros_(m.bias)\n",
    "            elif isinstance(m, nn.BatchNorm1d):\n",
    "                if m.weight is not None:\n",
    "                    nn.init.ones_(m.weight)\n",
    "                if m.bias is not None:\n",
    "                    nn.init.zeros_(m.bias)\n",
    "\n",
    "    # --- Convenience training/validation helpers (device-safe) ---\n",
    "    def training_step(self, batch):\n",
    "        images, labels = batch\n",
    "        device = next(self.parameters()).device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        out = self(images)\n",
    "        loss = F.cross_entropy(out, labels)\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch):\n",
    "        images, labels = batch\n",
    "        device = next(self.parameters()).device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        out = self(images)\n",
    "        loss = F.cross_entropy(out, labels)\n",
    "        preds = out.argmax(dim=1)\n",
    "        acc = (preds == labels).float().mean()\n",
    "        # return tensors (not Python floats) so we can stack them later\n",
    "        return {'val_loss': loss.detach(), 'val_acc': acc.detach()}\n",
    "\n",
    "    def validation_epoch_end(self, outputs):\n",
    "        # outputs: list of {'val_loss': tensor, 'val_acc': tensor}\n",
    "        batch_losses = [x['val_loss'] for x in outputs]\n",
    "        epoch_loss = torch.stack(batch_losses).mean()\n",
    "        batch_accs = [x['val_acc'] for x in outputs]\n",
    "        epoch_acc = torch.stack(batch_accs).mean()\n",
    "        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n",
    "\n",
    "    def epoch_end(self, epoch, result):\n",
    "        print(f\"Epoch [{epoch}]  val_loss: {result['val_loss']:.4f}, val_acc: {result['val_acc']:.4f}\")\n",
    "\n",
    "modelM = MLP()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9ec849b3-3cfe-49b3-bcb3-c2160f180d51",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import copy\n",
    "import numpy as np\n",
    "\n",
    "def accuracy(outputs, labels):\n",
    "    \"\"\"Return accuracy as a tensor (0..1).\"\"\"\n",
    "    _, preds = torch.max(outputs, dim=1)\n",
    "    return (preds == labels).float().mean()\n",
    "\n",
    "def evaluate(model, val_loader, device=None):\n",
    "    \"\"\"Run validation loop using model.validation_step and validation_epoch_end.\"\"\"\n",
    "    if device is None:\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model.to(device)\n",
    "    model.eval()\n",
    "\n",
    "    outputs = []\n",
    "    with torch.no_grad():\n",
    "        for batch in val_loader:\n",
    "            # assume model.validation_step moves tensors to device or expects CPU — we handle both:\n",
    "            out = model.validation_step(batch)\n",
    "            # ensure tensors (val_loss and val_acc) so validation_epoch_end can stack them\n",
    "            if isinstance(out['val_loss'], float):\n",
    "                out['val_loss'] = torch.tensor(out['val_loss'], device=device)\n",
    "            if isinstance(out['val_acc'], float):\n",
    "                out['val_acc'] = torch.tensor(out['val_acc'], device=device)\n",
    "            outputs.append(out)\n",
    "\n",
    "    return model.validation_epoch_end(outputs)\n",
    "\n",
    "\n",
    "def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD,\n",
    "        device=None, scheduler=None, save_path=None, print_every=1):\n",
    "    \"\"\"\n",
    "    Train model using the model.training_step / validation_step / validation_epoch_end helpers.\n",
    "\n",
    "    - opt_func: optimizer class (e.g. torch.optim.SGD or torch.optim.Adam)\n",
    "    - scheduler: optional lr scheduler (StepLR or ReduceLROnPlateau)\n",
    "    - save_path: optional path to save best model (by val_acc)\n",
    "    \"\"\"\n",
    "    if device is None:\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model.to(device)\n",
    "\n",
    "    optimizer = opt_func(model.parameters(), lr)\n",
    "    best_val_acc = -1.0\n",
    "    best_state = None\n",
    "    history = []\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        # Training\n",
    "        model.train()\n",
    "        train_losses = []\n",
    "        for batch in train_loader:\n",
    "            optimizer.zero_grad()                    # zero BEFORE backward\n",
    "            loss = model.training_step(batch)        # model should handle moving batch to device\n",
    "            # ensure loss is a tensor on correct device\n",
    "            if not torch.is_tensor(loss):\n",
    "                loss = torch.tensor(float(loss), device=device, requires_grad=True)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_losses.append(loss.detach().cpu().item())\n",
    "\n",
    "        # optional scheduler step for per-epoch schedulers (not ReduceLROnPlateau)\n",
    "        if scheduler is not None and not isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):\n",
    "            scheduler.step()\n",
    "\n",
    "        # Validation\n",
    "        result = evaluate(model, val_loader, device=device)\n",
    "        history.append(result)\n",
    "\n",
    "        # save best\n",
    "        if result.get(\"val_acc\", -1) > best_val_acc:\n",
    "            best_val_acc = result[\"val_acc\"]\n",
    "            best_state = copy.deepcopy(model.state_dict())\n",
    "            if save_path is not None:\n",
    "                torch.save(best_state, save_path)\n",
    "\n",
    "        # scheduler that depends on metric\n",
    "        if scheduler is not None and isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):\n",
    "            # commonly step on validation loss; change to val_acc if configured that way\n",
    "            scheduler.step(result.get(\"val_loss\"))\n",
    "\n",
    "        if (epoch + 1) % print_every == 0:\n",
    "            model.epoch_end(epoch, result)\n",
    "\n",
    "    # restore best weights (so returned model is the best one)\n",
    "    if best_state is not None:\n",
    "        model.load_state_dict(best_state)\n",
    "\n",
    "    return history\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "132c40f7-c5d5-49f2-a8c2-9414fba830dd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'val_loss': 2.3027942180633545, 'val_acc': 0.13568037748336792}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result0L = evaluate(modelL, val_loader)\n",
    "result0L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6251b0fc-2c98-409c-95bd-ddf6fe166809",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0], val_loss: 1.9445, val_acc: 0.7042\n",
      "Epoch [1], val_loss: 1.6802, val_acc: 0.7541\n",
      "Epoch [2], val_loss: 1.4828, val_acc: 0.7741\n",
      "Epoch [3], val_loss: 1.3338, val_acc: 0.7858\n",
      "Epoch [4], val_loss: 1.2192, val_acc: 0.7962\n"
     ]
    }
   ],
   "source": [
    "history1L = fit(5, 0.001, modelL, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b9dc652c-7ead-479f-af69-5902044c3a82",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0], val_loss: 1.1292, val_acc: 0.8043\n",
      "Epoch [1], val_loss: 1.0570, val_acc: 0.8106\n",
      "Epoch [2], val_loss: 0.9979, val_acc: 0.8153\n",
      "Epoch [3], val_loss: 0.9488, val_acc: 0.8205\n",
      "Epoch [4], val_loss: 0.9072, val_acc: 0.8245\n"
     ]
    }
   ],
   "source": [
    "history2L = fit(5, 0.001, modelL, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9835970a-af38-4bcc-9d2d-0fd8c5dff22f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0], val_loss: 0.8716, val_acc: 0.8268\n",
      "Epoch [1], val_loss: 0.8408, val_acc: 0.8304\n",
      "Epoch [2], val_loss: 0.8137, val_acc: 0.8336\n",
      "Epoch [3], val_loss: 0.7899, val_acc: 0.8355\n",
      "Epoch [4], val_loss: 0.7687, val_acc: 0.8378\n"
     ]
    }
   ],
   "source": [
    "history3L = fit(5, 0.001, modelL, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4089490b-0e1d-42c1-b879-9d6a148ef747",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0], val_loss: 0.7495, val_acc: 0.8399\n",
      "Epoch [1], val_loss: 0.7323, val_acc: 0.8413\n",
      "Epoch [2], val_loss: 0.7167, val_acc: 0.8437\n",
      "Epoch [3], val_loss: 0.7024, val_acc: 0.8451\n",
      "Epoch [4], val_loss: 0.6893, val_acc: 0.8463\n"
     ]
    }
   ],
   "source": [
    "history4L = fit(5, 0.001, modelL, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eb7bfacd-1428-4c2d-95b5-7e8fd6d79d3f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Vs. No. of epochs')"
      ]
     },
     "execution_count": 26,
     "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": [
    "historyL = [result0L] + history1L + history2L + history3L + history4L\n",
    "accuracies = [result['val_acc'] for result in historyL]\n",
    "plt.plot(accuracies, '-x')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.title('Accuracy Vs. No. of epochs')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0917403a-1a74-4a8a-8394-038d372beb8c",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## Now train the MLP model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "47b068e4-33d1-4be1-8177-1be2ae79b658",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'val_loss': 2.382965326309204, 'val_acc': 0.06398338824510574}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result0M = evaluate(modelM, val_loader)\n",
    "result0M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b6c22ce7-c2ba-4369-991d-04b9e483afa6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0]  val_loss: 1.4342, val_acc: 0.5825\n",
      "Epoch [1]  val_loss: 1.0730, val_acc: 0.7175\n",
      "Epoch [2]  val_loss: 0.8916, val_acc: 0.7675\n",
      "Epoch [3]  val_loss: 0.7782, val_acc: 0.7992\n",
      "Epoch [4]  val_loss: 0.6975, val_acc: 0.8211\n"
     ]
    }
   ],
   "source": [
    "history1M = fit(5, 0.001, modelM, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "45471c00-223a-4e2e-8065-4de079367ae1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0]  val_loss: 0.6370, val_acc: 0.8373\n",
      "Epoch [1]  val_loss: 0.5944, val_acc: 0.8509\n",
      "Epoch [2]  val_loss: 0.5576, val_acc: 0.8575\n",
      "Epoch [3]  val_loss: 0.5257, val_acc: 0.8664\n",
      "Epoch [4]  val_loss: 0.5024, val_acc: 0.8715\n"
     ]
    }
   ],
   "source": [
    "history2M = fit(5, 0.001, modelM, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ed6a97b8-7fd8-4a4e-aca3-07861deba8c6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0]  val_loss: 0.4810, val_acc: 0.8766\n",
      "Epoch [1]  val_loss: 0.4638, val_acc: 0.8797\n",
      "Epoch [2]  val_loss: 0.4487, val_acc: 0.8817\n",
      "Epoch [3]  val_loss: 0.4344, val_acc: 0.8844\n",
      "Epoch [4]  val_loss: 0.4187, val_acc: 0.8888\n"
     ]
    }
   ],
   "source": [
    "history3M = fit(5, 0.001, modelM, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7d42ad63-ee19-4886-8962-b7b78c11f1ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0]  val_loss: 0.4086, val_acc: 0.8900\n",
      "Epoch [1]  val_loss: 0.3995, val_acc: 0.8936\n",
      "Epoch [2]  val_loss: 0.3885, val_acc: 0.8953\n",
      "Epoch [3]  val_loss: 0.3815, val_acc: 0.8973\n",
      "Epoch [4]  val_loss: 0.3710, val_acc: 0.8985\n"
     ]
    }
   ],
   "source": [
    "history4M = fit(5, 0.001, modelM, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a3decf4b-47d6-4f52-9a08-39fb26e3d513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Vs. No. of epochs')"
      ]
     },
     "execution_count": 32,
     "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": [
    "historyM = [result0M] + history1M + history2M + history3M + history4M\n",
    "accuracies = [result['val_acc'] for result in historyM]\n",
    "plt.plot(accuracies, '-x', color='C1')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.title('Accuracy Vs. No. of epochs')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbe319f-f0e8-4beb-b2df-a3b1c2062da0",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### Logistic regression converges faster but has limited capacity, while an MLP converges more slowly but achieves higher final accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7c9c278c-fe02-4968-b51b-a38950ea390b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "historyL = [result0L] + history1L + history2L + history3L + history4L\n",
    "accL = [result['val_acc'] for result in historyL]\n",
    "\n",
    "# MLP history\n",
    "historyM = [result0M] + history1M + history2M + history3M + history4M\n",
    "accM = [result['val_acc'] for result in historyM]\n",
    "\n",
    "# Plot both on the same figure\n",
    "plt.figure(figsize=(7, 5))\n",
    "plt.plot(accL, '-x', label='Logistic Regression')\n",
    "plt.plot(accM, '-o', label='MLP with 1 hidden layer')\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Validation Accuracy')\n",
    "plt.title('Validation Accuracy vs Number of Epochs')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(\"accuracy_comparison1.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8301589-af9e-4fd2-bcb4-27c390e4e1a8",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "## 6. Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b785652e-b411-4e08-859a-f4cba9656c75",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "### test dataset \n",
    "test_dataset = MNIST(root = 'data/', train = False, transform = transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "50a04316-8d3e-4b3e-bd47-35dc7b1a1b2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape:  torch.Size([1, 28, 28])\n",
      "Label:  7\n"
     ]
    },
    {
     "data": {
      "image/png": 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uLpYktba2SpLy8vIS9s3Ly4s/d7/q6moFg8H4o6CgINmRAAAZJukILVu2TKdOndIf//jHbs/5fL6Er51z3bbds2rVKkUikfijpaUl2ZEAABkmqW9WXb58ufbu3aujR49q5MiR8e2hUEhS1xVROByOb29ra+t2dXSP3++X3+9PZgwAQIbzdCXknNOyZcu0e/duHT58uNt3fRcVFSkUCqmmpia+7fbt26qrq1NJSUlqJgYA9BueroSWLl2qnTt36i9/+YsCgUD8fZ5gMKisrCz5fD6tWLFC69at0+jRozV69GitW7dOTz31lF5++eW0/AcAADKXpwht2bJFkjR79uyE7Vu3btXixYslSStXrtTNmzf12muv6fr165oyZYoOHTqkQCCQkoEBAP2HzznnrIf4qmg0qmAwaD0GBqgxY8Z4XvPvf/87DZN09+yzz3pe89e//jUNkwAPJxKJKDs7u9d9uHccAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADADBECAJghQgAAM0QIAGCGCAEAzCT1k1WBvq6wsDCpdYcOHUrxJD178803Pa/529/+loZJAFtcCQEAzBAhAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZriBKfqln//850mtGzVqVIon6VldXZ3nNc65NEwC2OJKCABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwww1M0edNnz7d85rly5enYRIAqcaVEADADBECAJghQgAAM0QIAGCGCAEAzBAhAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghhuYos+bMWOG5zXf+MY30jBJzy5cuOB5zY0bN9IwCZB5uBICAJghQgAAM54iVF1drcmTJysQCCg3N1cLFy7UuXPnEvZZvHixfD5fwmPq1KkpHRoA0D94ilBdXZ2WLl2qhoYG1dTU6M6dOyotLVVnZ2fCfvPnz9fVq1fjj/3796d0aABA/+DpgwkHDhxI+Hrr1q3Kzc3ViRMnNHPmzPh2v9+vUCiUmgkBAP3WI70nFIlEJEk5OTkJ22tra5Wbm6sxY8ZoyZIlamtr+9rfIxaLKRqNJjwAAAND0hFyzqmyslLTp09XcXFxfHtZWZl27Nihw4cPa8OGDWpsbNTcuXMVi8V6/H2qq6sVDAbjj4KCgmRHAgBkmKS/T2jZsmU6deqUjh07lrB90aJF8V8XFxdr0qRJKiws1L59+1ReXt7t91m1apUqKyvjX0ejUUIEAANEUhFavny59u7dq6NHj2rkyJG97hsOh1VYWKimpqYen/f7/fL7/cmMAQDIcJ4i5JzT8uXL9f7776u2tlZFRUUPXNPe3q6WlhaFw+GkhwQA9E+e3hNaunSp/vCHP2jnzp0KBAJqbW1Va2urbt68KanrViRvvPGGPvroI126dEm1tbVasGCBhg8frueeey4t/wEAgMzl6Upoy5YtkqTZs2cnbN+6dasWL16swYMH6/Tp09q+fbs+//xzhcNhzZkzR7t27VIgEEjZ0ACA/sHzP8f1JisrSwcPHnykgQAAAwd30Qa+4p///KfnNf/3f//nec1nn33meQ3QH3EDUwCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADAjM896NbYj1k0GlUwGLQeAwDwiCKRiLKzs3vdhyshAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZvpchPrYrewAAEl6mD/P+1yEOjo6rEcAAKTAw/x53ufuon337l1duXJFgUBAPp8v4bloNKqCggK1tLQ88M6s/RnHoQvHoQvHoQvHoUtfOA7OOXV0dCg/P1+DBvV+rfPEY5rpoQ0aNEgjR47sdZ/s7OwBfZLdw3HownHownHownHoYn0cHvZH8vS5f44DAAwcRAgAYCajIuT3+7VmzRr5/X7rUUxxHLpwHLpwHLpwHLpk2nHocx9MAAAMHBl1JQQA6F+IEADADBECAJghQgAAMxkVoc2bN6uoqEhPPvmkJk6cqA8//NB6pMeqqqpKPp8v4REKhazHSrujR49qwYIFys/Pl8/n0549exKed86pqqpK+fn5ysrK0uzZs3XmzBmbYdPoQcdh8eLF3c6PqVOn2gybJtXV1Zo8ebICgYByc3O1cOFCnTt3LmGfgXA+PMxxyJTzIWMitGvXLq1YsUKrV6/WyZMnNWPGDJWVleny5cvWoz1WY8eO1dWrV+OP06dPW4+Udp2dnRo/frw2bdrU4/Pr16/Xxo0btWnTJjU2NioUCmnevHn97j6EDzoOkjR//vyE82P//v2PccL0q6ur09KlS9XQ0KCamhrduXNHpaWl6uzsjO8zEM6HhzkOUoacDy5D/PCHP3Svvvpqwrbvfve77q233jKa6PFbs2aNGz9+vPUYpiS5999/P/713bt3XSgUcu+88058261bt1wwGHS/+c1vDCZ8PO4/Ds45V1FR4Z599lmTeay0tbU5Sa6urs45N3DPh/uPg3OZcz5kxJXQ7du3deLECZWWliZsLy0tVX19vdFUNpqampSfn6+ioiK9+OKLunjxovVIppqbm9Xa2ppwbvj9fs2aNWvAnRuSVFtbq9zcXI0ZM0ZLlixRW1ub9UhpFYlEJEk5OTmSBu75cP9xuCcTzoeMiNC1a9f05ZdfKi8vL2F7Xl6eWltbjaZ6/KZMmaLt27fr4MGDeu+999Ta2qqSkhK1t7dbj2bm3v//gX5uSFJZWZl27Nihw4cPa8OGDWpsbNTcuXMVi8WsR0sL55wqKys1ffp0FRcXSxqY50NPx0HKnPOhz91Fuzf3/2gH51y3bf1ZWVlZ/Nfjxo3TtGnT9PTTT2vbtm2qrKw0nMzeQD83JGnRokXxXxcXF2vSpEkqLCzUvn37VF5ebjhZeixbtkynTp3SsWPHuj03kM6HrzsOmXI+ZMSV0PDhwzV48OBuf5Npa2vr9jeegWTYsGEaN26cmpqarEcxc+/TgZwb3YXDYRUWFvbL82P58uXau3evjhw5kvCjXwba+fB1x6EnffV8yIgIDR06VBMnTlRNTU3C9pqaGpWUlBhNZS8Wi+ns2bMKh8PWo5gpKipSKBRKODdu376turq6AX1uSFJ7e7taWlr61fnhnNOyZcu0e/duHT58WEVFRQnPD5Tz4UHHoSd99nww/FCEJ3/605/ckCFD3O9+9zv3r3/9y61YscINGzbMXbp0yXq0x+b11193tbW17uLFi66hocH99Kc/dYFAoN8fg46ODnfy5El38uRJJ8lt3LjRnTx50v33v/91zjn3zjvvuGAw6Hbv3u1Onz7tXnrpJRcOh100GjWePLV6Ow4dHR3u9ddfd/X19a65udkdOXLETZs2zX3729/uV8fhF7/4hQsGg662ttZdvXo1/vjiiy/i+wyE8+FBxyGTzoeMiZBzzv361792hYWFbujQoW7ChAkJH0ccCBYtWuTC4bAbMmSIy8/Pd+Xl5e7MmTPWY6XdkSNHnKRuj4qKCudc18dy16xZ40KhkPP7/W7mzJnu9OnTtkOnQW/H4YsvvnClpaVuxIgRbsiQIW7UqFGuoqLCXb582XrslOrpv1+S27p1a3yfgXA+POg4ZNL5wI9yAACYyYj3hAAA/RMRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYOb/AdNhpwQWTuUmAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img, label = test_dataset[0]\n",
    "plt.imshow(img[0], cmap = 'gray')\n",
    "print(\"shape: \", img.shape)\n",
    "print('Label: ', label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a7e8cb1d-4363-4393-a6a2-3e9d6fac2ef8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1, 28, 28])\n",
      "torch.Size([1, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "print(img.unsqueeze(0).shape)\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ffbf5bb0-9a10-4bec-9f68-4b9d443c3214",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def predict_image(img, model):\n",
    "    xb = img.unsqueeze(0)\n",
    "    yb = model(xb)\n",
    "    _, preds = torch.max(yb, dim = 1)\n",
    "    return(preds[0].item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "27d7f991-7d10-479b-8f35-6bf05e141fe2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label: 7 , Predicted : 7\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img, label = test_dataset[0]\n",
    "plt.imshow(img[0], cmap = 'gray')\n",
    "print('Label:', label, ', Predicted :', predict_image(img, modelL))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5f1fe258-13a6-43c4-8adb-ff68c668c99c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'val_loss': 0.6390308141708374, 'val_acc': 0.8609374761581421}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_loader = DataLoader(test_dataset, batch_size = 256)\n",
    "resultL = evaluate(modelL, test_loader)\n",
    "resultL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "70433e6c-f211-4436-8822-0fbcb92a1570",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'val_loss': 0.3206319808959961, 'val_acc': 0.9159179925918579}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultM = evaluate(modelM, test_loader)\n",
    "resultM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f6c089c1-a28d-4e1d-a305-c34aaa6a263d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(modelL.state_dict(), 'mnist-logistic.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "404daf38-e948-4117-9e79-ef54100ccf7c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(modelM.state_dict(), 'mnist-MLP.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0458e994-3bc0-44c7-b474-fa1b962d8341",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('linear.weight',\n",
       "              tensor([[ 0.0194,  0.0009, -0.0122,  ...,  0.0026,  0.0062, -0.0072],\n",
       "                      [-0.0002, -0.0110,  0.0028,  ...,  0.0160, -0.0014, -0.0027],\n",
       "                      [-0.0003, -0.0111, -0.0031,  ...,  0.0075, -0.0115,  0.0059],\n",
       "                      ...,\n",
       "                      [-0.0242, -0.0007, -0.0163,  ..., -0.0071,  0.0064,  0.0015],\n",
       "                      [-0.0057,  0.0084, -0.0080,  ..., -0.0046, -0.0181, -0.0037],\n",
       "                      [ 0.0053,  0.0077,  0.0021,  ..., -0.0068, -0.0114, -0.0157]])),\n",
       "             ('linear.bias',\n",
       "              tensor([-0.0448,  0.0907, -0.0222, -0.0230,  0.0257,  0.0416, -0.0090,  0.0434,\n",
       "                      -0.0916, -0.0107]))])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelL.state_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4601609e-51c1-49b7-87cb-5d8348fda5f0",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### The weights "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9326bdda-8826-428d-9697-544704571f25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### weights from Logistic Regression \n",
    "import matplotlib.pyplot as plt\n",
    "w = modelL.state_dict()['linear.weight'].cpu().numpy()  # shape (10, 784)\n",
    "fig, axes = plt.subplots(2,5, figsize=(10,5))\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(w[i].reshape(28,28), cmap='seismic')\n",
    "    ax.set_title(f\"class {i}\")\n",
    "    ax.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4622edb5-2e4f-43af-8cfd-6bd35fb6678e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### weights from MLP\n",
    "# approximate effective input → output weights\n",
    "W_eff = modelM.fc3.weight @ modelM.fc2.weight @ modelM.fc1.weight\n",
    "# shape: (10, 784)\n",
    "\n",
    "def plot_class_templates(W_eff):\n",
    "    plt.figure(figsize=(10,4))\n",
    "    for i in range(10):\n",
    "        plt.subplot(2,5,i+1)\n",
    "        plt.imshow(W_eff[i].detach().cpu().reshape(28,28), cmap=\"seismic\")\n",
    "        plt.title(f\"Class {i}\")\n",
    "        plt.axis(\"off\")\n",
    "    plt.suptitle(\"MLP Approx. Class Templates\", fontsize=14)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "plot_class_templates(W_eff)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4582f430-92e4-4de0-a8cb-5e94e33f6a11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(\n",
       "  (linear): Linear(in_features=784, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Check wrong prediciton \n",
    "modelL.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "70ec95b7-e8b3-4c2a-9649-d63b821b4538",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "wrong_images = []\n",
    "wrong_preds = []\n",
    "wrong_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for images, labels in test_loader:\n",
    "        outputs = modelL(images)\n",
    "        _, preds = torch.max(outputs, dim=1)\n",
    "\n",
    "        # find misclassified indices\n",
    "        wrong_idx = preds != labels\n",
    "\n",
    "        wrong_images.extend(images[wrong_idx])\n",
    "        wrong_preds.extend(preds[wrong_idx])\n",
    "        wrong_labels.extend(labels[wrong_idx])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "772ff225-905a-4fd6-a63d-34d74ee1778a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_wrong_predictions(images, preds, labels, n=10):\n",
    "    plt.figure(figsize=(12, 4))\n",
    "    for i in range(n):\n",
    "        plt.subplot(1, n, i + 1)\n",
    "        plt.imshow(images[i].squeeze(), cmap=\"gray\")\n",
    "        plt.title(f\"P:{preds[i].item()} / T:{labels[i].item()}\")\n",
    "        plt.axis(\"off\")\n",
    "    plt.show()\n",
    "\n",
    "show_wrong_predictions(wrong_images, wrong_preds, wrong_labels, n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "efa65de6-c9d5-4f8a-825a-ced059653ee0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def to_cpu(t):\n",
    "    \"\"\"Detach and move a tensor to CPU (returns cpu tensor).\"\"\"\n",
    "    return t.detach().cpu()\n",
    "\n",
    "def im_to_plot(img_tensor, mean=None, std=None):\n",
    "    \"\"\"\n",
    "    Convert a CHW tensor to HWC numpy for plt.imshow.\n",
    "    Handles grayscale (1,H,W) and RGB (3,H,W).\n",
    "    If mean/std provided (iterable of length C) it will unnormalize.\n",
    "    \"\"\"\n",
    "    img = to_cpu(img_tensor)\n",
    "    if img.dim() == 3 and img.size(0) == 1:\n",
    "        img = img.squeeze(0)   # (H, W)\n",
    "        if mean is not None and std is not None:\n",
    "            img = img * std[0] + mean[0]\n",
    "        return img.numpy()\n",
    "    elif img.dim() == 3 and img.size(0) == 3:\n",
    "        if mean is not None and std is not None:\n",
    "            mean_t = torch.tensor(mean).view(-1,1,1)\n",
    "            std_t  = torch.tensor(std).view(-1,1,1)\n",
    "            img = img * std_t + mean_t\n",
    "        img = img.permute(1,2,0)  # HWC\n",
    "        return img.numpy()\n",
    "    else:\n",
    "        return img.squeeze().numpy()\n",
    "\n",
    "def collect_disagreements(ModelL, ModelM, dataloader, device=None,\n",
    "                          max_examples_per_case=30, return_probs=True,\n",
    "                          sort_by_confidence_gap=False):\n",
    "    \"\"\"\n",
    "    Collect examples where:\n",
    "      - ModelL wrong & ModelM correct\n",
    "      - ModelM wrong & ModelL correct\n",
    "\n",
    "    Returns dict with keys:\n",
    "      'L_wrong_M_correct' and 'M_wrong_L_correct' containing lists of tuples:\n",
    "      (img_tensor_cpu, predL, probL, predM, probM, label, conf_gap)\n",
    "    conf_gap = ModelM_conf - ModelL_conf (useful for sorting)\n",
    "    \"\"\"\n",
    "    # device auto-detect\n",
    "    if device is None:\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "    ModelL.to(device)\n",
    "    ModelM.to(device)\n",
    "    ModelL.eval()\n",
    "    ModelM.eval()\n",
    "\n",
    "    l_wrong_m_correct = []\n",
    "    m_wrong_l_correct = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for images, labels in dataloader:\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outL = ModelL(images)\n",
    "            outM = ModelM(images)\n",
    "\n",
    "            # handle binary vs multi-class logits\n",
    "            if outL.dim() == 1 or (outL.dim() == 2 and outL.size(1) == 1):\n",
    "                probsL = torch.sigmoid(outL.view(-1))\n",
    "                predsL = (probsL > 0.5).long()\n",
    "                confL = probsL.clone()  # confidence for predicted class\n",
    "            else:\n",
    "                probsL = F.softmax(outL, dim=1)\n",
    "                predsL = torch.argmax(probsL, dim=1)\n",
    "                confL = probsL.max(dim=1).values\n",
    "\n",
    "            if outM.dim() == 1 or (outM.dim() == 2 and outM.size(1) == 1):\n",
    "                probsM = torch.sigmoid(outM.view(-1))\n",
    "                predsM = (probsM > 0.5).long()\n",
    "                confM = probsM.clone()\n",
    "            else:\n",
    "                probsM = F.softmax(outM, dim=1)\n",
    "                predsM = torch.argmax(probsM, dim=1)\n",
    "                confM = probsM.max(dim=1).values\n",
    "\n",
    "            # boolean masks\n",
    "            L_wrong = predsL != labels\n",
    "            M_correct = predsM == labels\n",
    "            mask_Lwrong_Mcorrect = L_wrong & M_correct\n",
    "\n",
    "            M_wrong = predsM != labels\n",
    "            L_correct = predsL == labels\n",
    "            mask_Mwrong_Lcorrect = M_wrong & L_correct\n",
    "\n",
    "            # collect examples\n",
    "            for idx in torch.where(mask_Lwrong_Mcorrect)[0]:\n",
    "                if len(l_wrong_m_correct) >= max_examples_per_case:\n",
    "                    break\n",
    "                i = int(idx)\n",
    "                conf_gap = float(confM[i].cpu().item()) - float(confL[i].cpu().item())\n",
    "                l_wrong_m_correct.append((\n",
    "                    to_cpu(images[i]),\n",
    "                    int(predsL[i].cpu().item()),\n",
    "                    float(confL[i].cpu().item()),\n",
    "                    int(predsM[i].cpu().item()),\n",
    "                    float(confM[i].cpu().item()),\n",
    "                    int(labels[i].cpu().item()),\n",
    "                    conf_gap\n",
    "                ))\n",
    "\n",
    "            for idx in torch.where(mask_Mwrong_Lcorrect)[0]:\n",
    "                if len(m_wrong_l_correct) >= max_examples_per_case:\n",
    "                    break\n",
    "                i = int(idx)\n",
    "                conf_gap = float(confM[i].cpu().item()) - float(confL[i].cpu().item())\n",
    "                m_wrong_l_correct.append((\n",
    "                    to_cpu(images[i]),\n",
    "                    int(predsL[i].cpu().item()),\n",
    "                    float(confL[i].cpu().item()),\n",
    "                    int(predsM[i].cpu().item()),\n",
    "                    float(confM[i].cpu().item()),\n",
    "                    int(labels[i].cpu().item()),\n",
    "                    conf_gap\n",
    "                ))\n",
    "\n",
    "            if len(l_wrong_m_correct) >= max_examples_per_case and len(m_wrong_l_correct) >= max_examples_per_case:\n",
    "                break\n",
    "\n",
    "    # optional sorting by confidence gap (largest positive gap first)\n",
    "    if sort_by_confidence_gap:\n",
    "        l_wrong_m_correct.sort(key=lambda t: t[-1], reverse=True)  # M much more confident than L\n",
    "        m_wrong_l_correct.sort(key=lambda t: t[-1], reverse=True)\n",
    "\n",
    "    return {\n",
    "        \"L_wrong_M_correct\": l_wrong_m_correct,\n",
    "        \"M_wrong_L_correct\": m_wrong_l_correct\n",
    "    }\n",
    "\n",
    "def plot_disagreements(case_list, title, n=8, mean=None, std=None, cmap=\"gray\"):\n",
    "    \"\"\"\n",
    "    case_list: list of tuples (img_tensor_cpu, predL, confL, predM, confM, label, conf_gap)\n",
    "    \"\"\"\n",
    "    n = min(n, len(case_list))\n",
    "    if n == 0:\n",
    "        print(f\"No examples for: {title}\")\n",
    "        return\n",
    "\n",
    "    plt.figure(figsize=(3.5 * n, 3))\n",
    "    plt.suptitle(title, fontsize=14)\n",
    "    for i in range(n):\n",
    "        img_tensor, predL, confL, predM, confM, label, conf_gap = case_list[i]\n",
    "        ax = plt.subplot(1, n, i+1)\n",
    "        npimg = im_to_plot(img_tensor, mean=mean, std=std)\n",
    "\n",
    "        if npimg.ndim == 2:\n",
    "            ax.imshow(npimg, cmap=cmap)\n",
    "        else:\n",
    "            ax.imshow(np.clip(npimg, 0, 1))\n",
    "\n",
    "        ax.set_title(f\"T:{label}\\nL:{predL} ({confL:.2f})\\nM:{predM} ({confM:.2f})\", fontsize=9)\n",
    "        ax.axis(\"off\")\n",
    "    plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
    "    plt.show()\n",
    "\n",
    "# -------------------------\n",
    "# Example usage (replace with your objects / loader):\n",
    "# -------------------------\n",
    "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# ModelL.to(device); ModelM.to(device)\n",
    "# cases = collect_disagreements(ModelL, ModelM, test_loader, device=device,\n",
    "#                              max_examples_per_case=40, sort_by_confidence_gap=True)\n",
    "# plot_disagreements(cases[\"L_wrong_M_correct\"], \"ModelL WRONG, ModelM CORRECT\", n=8, mean=None, std=None)\n",
    "# plot_disagreements(cases[\"M_wrong_L_correct\"], \"ModelM WRONG, ModelL CORRECT\", n=8, mean=None, std=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "605ccb36-a0dc-4c6e-9c7f-b356ffef3ea0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2800x300 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2800x300 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "modelL.to(device); modelM.to(device)\n",
    "cases = collect_disagreements(modelL, modelM, test_loader, device=device, max_examples_per_case=40, sort_by_confidence_gap=True)\n",
    "plot_disagreements(cases[\"L_wrong_M_correct\"], \"ModelL WRONG, ModelM CORRECT\", n=8, mean=None, std=None)\n",
    "plot_disagreements(cases[\"M_wrong_L_correct\"], \"ModelM WRONG, ModelL CORRECT\", n=8, mean=None, std=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c54364-b2a2-4147-8cfa-fa47026fcff6",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### Confusion Matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "89c5b4df-9e43-457f-97a8-c85f618076c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "def collect_preds(model, dataloader, device):\n",
    "    model.eval()\n",
    "    all_preds = []\n",
    "    all_labels = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for images, labels in dataloader:\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(images)\n",
    "            preds = torch.argmax(outputs, dim=1)\n",
    "\n",
    "            all_preds.append(preds.cpu())\n",
    "            all_labels.append(labels.cpu())\n",
    "\n",
    "    return torch.cat(all_labels), torch.cat(all_preds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fc9f099e-d8e2-4b59-b87b-d4a41f7646a2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def plot_confusion_with_boxes(y_true, y_pred, title, top_k=6):\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    num_classes = cm.shape[0]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(7, 6))\n",
    "    im = ax.imshow(cm, interpolation=\"nearest\", cmap=\"Blues\")\n",
    "    plt.colorbar(im, ax=ax)\n",
    "\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel(\"Predicted label\")\n",
    "    ax.set_ylabel(\"True label\")\n",
    "    ax.set_xticks(range(num_classes))\n",
    "    ax.set_yticks(range(num_classes))\n",
    "\n",
    "    # annotate counts\n",
    "    for i in range(num_classes):\n",
    "        for j in range(num_classes):\n",
    "            ax.text(j, i, cm[i, j], ha=\"center\", va=\"center\", fontsize=9)\n",
    "\n",
    "    # find top off-diagonal entries\n",
    "    off_diag = []\n",
    "    for i in range(num_classes):\n",
    "        for j in range(num_classes):\n",
    "            if i != j and cm[i, j] > 0:\n",
    "                off_diag.append((cm[i, j], i, j))\n",
    "\n",
    "    off_diag.sort(reverse=True)\n",
    "    top_errors = off_diag[:top_k]\n",
    "\n",
    "    # draw boxes\n",
    "    for _, i, j in top_errors:\n",
    "        rect = plt.Rectangle(\n",
    "            (j - 0.5, i - 0.5),\n",
    "            1,\n",
    "            1,\n",
    "            fill=False,\n",
    "            edgecolor=\"red\",\n",
    "            linewidth=2\n",
    "        )\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ef929b16-f641-4df6-80fb-a78af860d12f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "modelL.to(device)\n",
    "modelM.to(device)\n",
    "\n",
    "y_true_L, y_pred_L = collect_preds(modelL, test_loader, device)\n",
    "y_true_M, y_pred_M = collect_preds(modelM, test_loader, device)\n",
    "\n",
    "plot_confusion_with_boxes(\n",
    "    y_true_L,\n",
    "    y_pred_L,\n",
    "    title=\"Confusion Matrix – ModelL (Logistic Regression)\",\n",
    "    top_k=6\n",
    ")\n",
    "\n",
    "plot_confusion_with_boxes(\n",
    "    y_true_M,\n",
    "    y_pred_M,\n",
    "    title=\"Confusion Matrix – ModelM (MLP)\",\n",
    "    top_k=6\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "102a7af1-c723-4f32-be88-ccbe00b0dbc5",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## Access Confidence in ML models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c5e5b03e-9830-4556-ac44-dda1c904cb63",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.calibration import calibration_curve\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def collect_confidence_and_correctness(model, dataloader, device):\n",
    "    model.eval()\n",
    "    probs_all = []\n",
    "    correct_all = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for xb, yb in dataloader:\n",
    "            xb = xb.to(device)\n",
    "            yb = yb.to(device)\n",
    "\n",
    "            logits = model(xb)\n",
    "            probs = torch.softmax(logits, dim=1)\n",
    "\n",
    "            confs, preds = probs.max(dim=1)\n",
    "            probs_all.append(confs.cpu())\n",
    "            correct_all.append((preds == yb).float().cpu())\n",
    "\n",
    "    probs_all = torch.cat(probs_all).numpy()\n",
    "    correct_all = torch.cat(correct_all).numpy()\n",
    "\n",
    "    return probs_all, correct_all\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "148d65ae-c2f8-4ab4-a7d3-f7b2f7447641",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Logistic Regression\n",
    "probs_L, correct_L = collect_confidence_and_correctness(modelL, test_loader, device)\n",
    "frac_pos_L, mean_pred_L = calibration_curve(\n",
    "    correct_L, probs_L, n_bins=10, strategy=\"uniform\"\n",
    ")\n",
    "\n",
    "# MLP\n",
    "probs_M, correct_M = collect_confidence_and_correctness(modelM, test_loader, device)\n",
    "frac_pos_M, mean_pred_M = calibration_curve(\n",
    "    correct_M, probs_M, n_bins=10, strategy=\"uniform\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1ed6745e-bb29-429d-b104-f13f15bd709d",
   "metadata": {
    "tags": []
   },
   "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",
    "\n",
    "# Perfect calibration line\n",
    "plt.plot([0, 1], [0, 1], \"k--\", label=\"Perfect calibration\")\n",
    "\n",
    "# Logistic Regression\n",
    "plt.plot(mean_pred_L, frac_pos_L, \"-o\", label=\"Logistic Regression\")\n",
    "\n",
    "# MLP\n",
    "plt.plot(mean_pred_M, frac_pos_M, \"-s\", label=\"MLP\")\n",
    "\n",
    "plt.xlabel(\"Mean predicted confidence\")\n",
    "plt.ylabel(\"Fraction of correct predictions\")\n",
    "plt.title(\"Reliability Diagram (Calibration Curve)\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\n",
    "    \"calibration_curve_logistic_vs_mlp.png\",\n",
    "    dpi=300,\n",
    "    bbox_inches=\"tight\"\n",
    ")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "37221412-90af-44d7-826f-21e57ef2b8ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ECE Logistic: 0.23214034938812259\n",
      "ECE MLP:      0.07543363214433194\n"
     ]
    }
   ],
   "source": [
    "def expected_calibration_error(probs, correct, n_bins=10):\n",
    "    bins = np.linspace(0, 1, n_bins + 1)\n",
    "    ece = 0.0\n",
    "    for i in range(n_bins):\n",
    "        mask = (probs >= bins[i]) & (probs < bins[i+1])\n",
    "        if mask.sum() > 0:\n",
    "            acc = correct[mask].mean()\n",
    "            conf = probs[mask].mean()\n",
    "            ece += (mask.sum() / len(probs)) * abs(acc - conf)\n",
    "    return ece\n",
    "\n",
    "print(\"ECE Logistic:\", expected_calibration_error(probs_L, correct_L))\n",
    "print(\"ECE MLP:     \", expected_calibration_error(probs_M, correct_M))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "22381402-d362-4873-b944-52466e4d9bd4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Logistic Regression ===\n",
      " correct: n=8636, mean_conf=0.672, std=0.193\n",
      " wrong:   n=1364, mean_conf=0.374, std=0.123\n",
      "\n",
      "=== MLP ===\n",
      " correct: n=9139, mean_conf=0.869, std=0.163\n",
      " wrong:   n=861, mean_conf=0.516, std=0.171\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "\n",
    "# If you already have probs_* and correct_* arrays from before, skip collection and set:\n",
    "# probs_L, correct_L = probs_L, correct_L\n",
    "# probs_M, correct_M = probs_M, correct_M\n",
    "# Otherwise collect them:\n",
    "def collect_confidence_and_correctness(model, dataloader, device):\n",
    "    model.eval()\n",
    "    probs_all = []\n",
    "    correct_all = []\n",
    "    with torch.no_grad():\n",
    "        for xb, yb in dataloader:\n",
    "            xb = xb.to(device); yb = yb.to(device)\n",
    "            logits = model(xb)\n",
    "            if logits.dim() == 1 or (logits.dim()==2 and logits.size(1) == 1):\n",
    "                probs = torch.sigmoid(logits.view(-1))\n",
    "                confs = probs.cpu().numpy()\n",
    "                preds = (probs > 0.5).long().cpu().numpy()\n",
    "                correct = (preds == yb.cpu().numpy()).astype(float)\n",
    "            else:\n",
    "                probs = torch.softmax(logits, dim=1)\n",
    "                confs = probs.max(dim=1).values.cpu().numpy()\n",
    "                preds = probs.argmax(dim=1).cpu().numpy()\n",
    "                correct = (preds == yb.cpu().numpy()).astype(float)\n",
    "            probs_all.append(confs)\n",
    "            correct_all.append(correct)\n",
    "    probs_all = np.concatenate(probs_all, axis=0)\n",
    "    correct_all = np.concatenate(correct_all, axis=0)\n",
    "    return probs_all, correct_all\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# ensure models are on same device\n",
    "modelL.to(device); modelM.to(device)\n",
    "\n",
    "probs_L, correct_L = collect_confidence_and_correctness(modelL, test_loader, device)\n",
    "probs_M, correct_M = collect_confidence_and_correctness(modelM, test_loader, device)\n",
    "\n",
    "# split into correct / wrong\n",
    "probs_L_corr = probs_L[correct_L == 1]\n",
    "probs_L_wrong = probs_L[correct_L == 0]\n",
    "probs_M_corr = probs_M[correct_M == 1]\n",
    "probs_M_wrong = probs_M[correct_M == 0]\n",
    "\n",
    "# summary stats\n",
    "def print_summary(name, probs_corr, probs_wrong):\n",
    "    print(f\"=== {name} ===\")\n",
    "    print(f\" correct: n={len(probs_corr)}, mean_conf={np.mean(probs_corr):.3f}, std={np.std(probs_corr):.3f}\")\n",
    "    print(f\" wrong:   n={len(probs_wrong)}, mean_conf={np.mean(probs_wrong):.3f}, std={np.std(probs_wrong):.3f}\")\n",
    "    print()\n",
    "\n",
    "print_summary(\"Logistic Regression\", probs_L_corr, probs_L_wrong)\n",
    "print_summary(\"MLP\", probs_M_corr, probs_M_wrong)\n",
    "\n",
    "# Plotting\n",
    "bins = np.linspace(0.0, 1.0, 21)  # 20 bins (0.0-0.05-...-1.0)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4), sharey=True)\n",
    "plt.suptitle(\"Confidence histograms (correct vs wrong)\")\n",
    "\n",
    "# Logistic column\n",
    "ax = axes[0]\n",
    "ax.hist(probs_L_corr, bins=bins, density=True, alpha=0.6, label=f\"correct (n={len(probs_L_corr)})\", color=\"#1f77b4\")\n",
    "ax.hist(probs_L_wrong, bins=bins, density=True, alpha=0.6, label=f\"wrong (n={len(probs_L_wrong)})\", color=\"#ff7f0e\")\n",
    "ax.set_title(\"Logistic Regression\")\n",
    "ax.set_xlabel(\"Max softmax confidence\")\n",
    "ax.set_ylabel(\"Density\")\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# MLP column\n",
    "ax = axes[1]\n",
    "ax.hist(probs_M_corr, bins=bins, density=True, alpha=0.6, label=f\"correct (n={len(probs_M_corr)})\", color=\"#1f77b4\")\n",
    "ax.hist(probs_M_wrong, bins=bins, density=True, alpha=0.6, label=f\"wrong (n={len(probs_M_wrong)})\", color=\"#ff7f0e\")\n",
    "ax.set_title(\"MLP\")\n",
    "ax.set_xlabel(\"Max softmax confidence\")\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.savefig(\"confidence_histograms_logistic_vs_mlp.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad0f1f8e-c7c9-402b-9f0b-21e6dcb563f1",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### Saliency maps \n",
    "For an image model, a saliency map highlights the pixels that most influenced the model’s decision for one image and one class. Saliency measures:\n",
    "LIf I slightly change each pixel, how much would the prediction change?\n",
    "$$\n",
    "\\text{Saliency}(x) = \\left| \\frac{\\partial S_c(x)}{\\partial x} \\right|\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "94f873dd-7fff-481a-ba73-a0095edea448",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def _to_device(tensor, device):\n",
    "    return tensor.to(device)\n",
    "\n",
    "def compute_saliency(model, images, labels=None, device=None,\n",
    "                     target='pred', abs_val=True,\n",
    "                     smooth=False, n_samples=25, stdev=0.1):\n",
    "    \"\"\"\n",
    "    Compute saliency maps for a batch of images.\n",
    "\n",
    "    model: PyTorch model (in eval mode)\n",
    "    images: torch.Tensor (B, C, H, W) on CPU or device\n",
    "    labels: optional true labels (B,)\n",
    "    device: torch.device or None (auto)\n",
    "    target: 'pred' (use predicted class) or 'true' (use provided labels)\n",
    "    abs_val: if True, take absolute value of gradients (common practice)\n",
    "    smooth: if True, use SmoothGrad (averaging gradients over noisy samples)\n",
    "    n_samples: number of noisy samples for SmoothGrad\n",
    "    stdev: noise std (fraction of data range) for SmoothGrad\n",
    "\n",
    "    Returns:\n",
    "      saliency_maps: numpy array (B, H, W) normalized to [0,1]\n",
    "      preds: predicted classes (B,)\n",
    "      targets_used: classes used for gradient (B,)\n",
    "    \"\"\"\n",
    "    model.eval()\n",
    "    if device is None:\n",
    "        device = next(model.parameters()).device\n",
    "\n",
    "    images = images.to(device)\n",
    "    images_orig = images.detach()\n",
    "\n",
    "    B = images.shape[0]\n",
    "\n",
    "    # helper to compute gradients for one input batch\n",
    "    def grads_for_inputs(x, target_idxs):\n",
    "        x.requires_grad_(True)\n",
    "        outputs = model(x)  # logits\n",
    "        if outputs.dim() == 1 or (outputs.dim()==2 and outputs.size(1)==1):\n",
    "            # binary logits -> treat as single logit; target must be 0/1\n",
    "            # create scalar outputs for the target class: use logit*(2*t-1)\n",
    "            # but simpler: use probability after sigmoid\n",
    "            probs = torch.sigmoid(outputs.view(-1))\n",
    "            scores = probs if target_idxs is None else probs.gather(0, target_idxs)\n",
    "        else:\n",
    "            # multiclass\n",
    "            if target_idxs is None:\n",
    "                # fallback: use predicted class\n",
    "                preds = outputs.argmax(dim=1)\n",
    "                scores = outputs.gather(1, preds.unsqueeze(1)).squeeze(1)\n",
    "            else:\n",
    "                scores = outputs.gather(1, target_idxs.unsqueeze(1)).squeeze(1)\n",
    "\n",
    "        # backward on each example in batch: sum of scores enables grad calculation\n",
    "        grads = []\n",
    "        # zero grads\n",
    "        model.zero_grad()\n",
    "        scores_sum = scores.sum()\n",
    "        scores_sum.backward(retain_graph=False)\n",
    "        grad = x.grad.detach().clone()  # (B, C, H, W)\n",
    "        x.requires_grad_(False)\n",
    "        x.grad = None\n",
    "        return grad\n",
    "\n",
    "    # Determine targets to use\n",
    "    with torch.no_grad():\n",
    "        logits = model(images)\n",
    "        if logits.dim() == 1 or (logits.dim()==2 and logits.size(1)==1):\n",
    "            probs = torch.sigmoid(logits.view(-1))\n",
    "            preds = (probs>0.5).long().cpu()\n",
    "        else:\n",
    "            preds = torch.argmax(logits, dim=1).cpu()\n",
    "    preds = preds.to('cpu')\n",
    "\n",
    "    if target == 'pred':\n",
    "        targets_used = preds.to(device)\n",
    "    elif target == 'true':\n",
    "        if labels is None:\n",
    "            raise ValueError(\"labels must be provided when target='true'\")\n",
    "        targets_used = labels.to(device)\n",
    "    else:\n",
    "        raise ValueError(\"target must be 'pred' or 'true'\")\n",
    "\n",
    "    # compute vanilla or smoothgrad\n",
    "    if not smooth:\n",
    "        grad = grads_for_inputs(images, targets_used).cpu()  # (B, C, H, W)\n",
    "    else:\n",
    "        # SmoothGrad: average gradients from noisy samples\n",
    "        grads_accum = torch.zeros_like(images)\n",
    "        data_range = images.max() - images.min()\n",
    "        noise_std = stdev * (data_range if data_range > 0 else 1.0)\n",
    "        for _ in range(n_samples):\n",
    "            noise = torch.randn_like(images) * noise_std\n",
    "            noisy = (images_orig + noise).to(device)\n",
    "            g = grads_for_inputs(noisy, targets_used).cpu()\n",
    "            grads_accum += g\n",
    "        grad = grads_accum / float(n_samples)\n",
    "\n",
    "    # convert grad → saliency map (collapse channel)\n",
    "    # common options: absolute max across channels, L2 across channels\n",
    "    grad_np = grad.numpy()  # (B, C, H, W)\n",
    "    # use absolute max across channels:\n",
    "    saliency = np.max(np.abs(grad_np), axis=1) if abs_val else np.max(grad_np, axis=1)\n",
    "    # normalize each image to [0,1]\n",
    "    saliency_norm = np.zeros_like(saliency)\n",
    "    for i in range(B):\n",
    "        s = saliency[i]\n",
    "        s = s - s.min()\n",
    "        if s.max() > 0:\n",
    "            s = s / s.max()\n",
    "        saliency_norm[i] = s\n",
    "\n",
    "    return saliency_norm, preds.cpu().numpy(), targets_used.cpu().numpy()\n",
    "\n",
    "def plot_saliency_grid(images, saliency_maps, preds=None, labels=None, cmap='hot', alpha=0.6, ncols=5, title=None, unnormalize_fn=None):\n",
    "    \"\"\"\n",
    "    images: torch.Tensor (B, C, H, W) on CPU\n",
    "    saliency_maps: np.array (B, H, W) in [0,1]\n",
    "    preds, labels: optional lists or arrays (B,)\n",
    "    unnormalize_fn: function to convert image tensor -> numpy HxW or HxWx3 (optional)\n",
    "    \"\"\"\n",
    "    B = images.shape[0]\n",
    "    ncols = min(ncols, B)\n",
    "    nrows = int(np.ceil(B / ncols))\n",
    "    plt.figure(figsize=(3.2 * ncols, 3 * nrows))\n",
    "    if title:\n",
    "        plt.suptitle(title, fontsize=14)\n",
    "    for i in range(B):\n",
    "        img = images[i]\n",
    "        sal = saliency_maps[i]\n",
    "\n",
    "        ax = plt.subplot(nrows, ncols, i + 1)\n",
    "\n",
    "        # get image numpy for plotting\n",
    "        if unnormalize_fn:\n",
    "            npimg = unnormalize_fn(img)\n",
    "        else:\n",
    "            npimg = img.detach().cpu().numpy()\n",
    "            # convert CHW -> HWC if needed\n",
    "            if npimg.ndim == 3 and npimg.shape[0] in (1,3):\n",
    "                npimg = np.transpose(npimg, (1,2,0))\n",
    "            # if single-channel, reduce to HxW\n",
    "            if npimg.ndim == 3 and npimg.shape[2] == 1:\n",
    "                npimg = npimg[:,:,0]\n",
    "\n",
    "        # show base image\n",
    "        if npimg.ndim == 2:\n",
    "            ax.imshow(npimg, cmap='gray')\n",
    "            ax.imshow(sal, cmap=cmap, alpha=alpha, vmin=0, vmax=1)\n",
    "        else:\n",
    "            # color image: show image and overlay saliency as heatmap (use clipped)\n",
    "            ax.imshow(np.clip(npimg, 0, 1))\n",
    "            ax.imshow(sal, cmap=cmap, alpha=alpha, vmin=0, vmax=1)\n",
    "\n",
    "        title_lines = []\n",
    "        if preds is not None:\n",
    "            title_lines.append(f\"P:{int(preds[i])}\")\n",
    "        if labels is not None:\n",
    "            title_lines.append(f\"T:{int(labels[i])}\")\n",
    "        if title_lines:\n",
    "            ax.set_title(\" / \".join(title_lines), fontsize=9)\n",
    "        ax.axis('off')\n",
    "    plt.tight_layout(rect=[0,0,1,0.95])\n",
    "    plt.show()\n",
    "\n",
    "# -----------------------------\n",
    "# Usage example: compare two models side-by-side\n",
    "# -----------------------------\n",
    "def compare_models_saliency(modelA, modelB, images_batch, labels_batch=None,\n",
    "                            device=None, target='pred', smooth=False, n_samples=25, stdev=0.15,\n",
    "                            unnormalize_fn=None, n_show=6):\n",
    "    \"\"\"\n",
    "    Compute and plot saliency maps for two models for the same inputs side-by-side.\n",
    "\n",
    "    modelA, modelB: PyTorch models (in eval mode)\n",
    "    images_batch: torch.Tensor (B, C, H, W) -- pick a small batch (e.g. B=6)\n",
    "    labels_batch: optional true labels (B,)\n",
    "    unnormalize_fn: optional function(img_tensor) -> numpy for displaying original image properly\n",
    "    \"\"\"\n",
    "    if device is None:\n",
    "        device = next(modelA.parameters()).device\n",
    "\n",
    "    images_cpu = images_batch.detach().cpu()\n",
    "    # compute for model A\n",
    "    salA, predsA, targetsA = compute_saliency(modelA, images_batch, labels=labels_batch, device=device,\n",
    "                                              target=target, abs_val=True, smooth=smooth, n_samples=n_samples, stdev=stdev)\n",
    "    # compute for model B\n",
    "    salB, predsB, targetsB = compute_saliency(modelB, images_batch, labels=labels_batch, device=device,\n",
    "                                              target=target, abs_val=True, smooth=smooth, n_samples=n_samples, stdev=stdev)\n",
    "\n",
    "    # choose how many to show\n",
    "    n_show = min(n_show, images_batch.shape[0])\n",
    "    # plot grid: for simplicity, show modelA then modelB in adjacent rows\n",
    "    print(\"Top: ModelL  |  Bottom: ModelM\")\n",
    "    plt.figure(figsize=(6 * n_show, 6))\n",
    "    for i in range(n_show):\n",
    "        # original image (top row)\n",
    "        ax = plt.subplot(2, n_show, i + 1)\n",
    "        npimg = (unnormalize_fn(images_cpu[i]) if unnormalize_fn else images_cpu[i].numpy())\n",
    "        if npimg.ndim == 3 and npimg.shape[0] in (1,3):  # CHW\n",
    "            npimg = np.transpose(npimg, (1,2,0))\n",
    "            if npimg.shape[2] == 1:\n",
    "                npimg = npimg[:,:,0]\n",
    "        ax.imshow(npimg, cmap='gray' if npimg.ndim==2 else None)\n",
    "        ax.set_title(f\"Input #{i}\")\n",
    "        ax.axis('off')\n",
    "\n",
    "        # modelA saliency (bottom-left)\n",
    "        ax = plt.subplot(2, n_show, n_show + i + 1)\n",
    "        if npimg.ndim == 2:\n",
    "            ax.imshow(npimg, cmap='gray')\n",
    "            ax.imshow(salA[i], cmap='hot', alpha=0.6, vmin=0, vmax=1)\n",
    "        else:\n",
    "            ax.imshow(npimg)\n",
    "            ax.imshow(salA[i], cmap='hot', alpha=0.6, vmin=0, vmax=1)\n",
    "        ax.set_title(f\"A P:{predsA[i]} T:{(labels_batch[i].item() if labels_batch is not None else '-')}\")\n",
    "        ax.axis('off')\n",
    "\n",
    "        # overlay ModelB saliency next to it (bonus: show as separate figure)\n",
    "        # show on the same subplot? better: open a new figure for ModelB side-by-side\n",
    "    plt.suptitle(\"Top row: inputs — Bottom row: ModelA saliency\", fontsize=14)\n",
    "    plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
    "    plt.show()\n",
    "\n",
    "    # Now show ModelB saliency in a separate figure (side-by-side comparison easier to inspect)\n",
    "    plt.figure(figsize=(6 * n_show, 3))\n",
    "    for i in range(n_show):\n",
    "        ax = plt.subplot(1, n_show, i + 1)\n",
    "        npimg = (unnormalize_fn(images_cpu[i]) if unnormalize_fn else images_cpu[i].numpy())\n",
    "        if npimg.ndim == 3 and npimg.shape[0] in (1,3):\n",
    "            npimg = np.transpose(npimg, (1,2,0))\n",
    "            if npimg.shape[2] == 1:\n",
    "                npimg = npimg[:,:,0]\n",
    "        if npimg.ndim == 2:\n",
    "            ax.imshow(npimg, cmap='gray')\n",
    "            ax.imshow(salB[i], cmap='hot', alpha=0.6, vmin=0, vmax=1)\n",
    "        else:\n",
    "            ax.imshow(npimg)\n",
    "            ax.imshow(salB[i], cmap='hot', alpha=0.6, vmin=0, vmax=1)\n",
    "        ax.set_title(f\"B P:{predsB[i]} T:{(labels_batch[i].item() if labels_batch is not None else '-')}\")\n",
    "        ax.axis('off')\n",
    "    plt.suptitle(\"ModelB saliency maps\", fontsize=14)\n",
    "    plt.tight_layout(rect=[0,0,1,0.95])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b0e13d31-fe1f-4a16-bc87-1ae2078aa6c8",
   "metadata": {},
   "source": [
    "## Color Scheme: Lblack → dark red → red → orange → yellow → white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7cca607f-5843-45e7-a7e8-5f7eb0bb53ad",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top: ModelL  |  Bottom: ModelM\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images, labels = next(iter(test_loader))\n",
    "k = 6\n",
    "images = images[:k]\n",
    "labels = labels[:k]\n",
    "compare_models_saliency(modelL, modelM, images, labels, device=device,\n",
    "                         target='pred', smooth=True, n_samples=30, stdev=0.12,\n",
    "                         unnormalize_fn=None, n_show=6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f52a301a-3555-4792-a9f7-3e3bf60a99b9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "### saving Saliency Maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "831c5a42-2df0-429a-92bd-5cd03a1af53b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "def _img_to_numpy_for_display(img_tensor, unnormalize_fn=None):\n",
    "    \"\"\"Return HxW or HxWx3 numpy array in [0,1] for plotting.\"\"\"\n",
    "    if unnormalize_fn:\n",
    "        return unnormalize_fn(img_tensor)\n",
    "    arr = img_tensor.detach().cpu().numpy()\n",
    "    # CHW -> HWC if needed\n",
    "    if arr.ndim == 3 and arr.shape[0] in (1,3):\n",
    "        arr = np.transpose(arr, (1,2,0))\n",
    "        if arr.shape[2] == 1:\n",
    "            arr = arr[:,:,0]\n",
    "    return arr\n",
    "\n",
    "def compare_models_saliency_return_figs(\n",
    "    modelA, modelB, images_batch, labels_batch=None,\n",
    "    device=None, target='pred', smooth=False, n_samples=25, stdev=0.15,\n",
    "    unnormalize_fn=None, n_show=6, show=True\n",
    "):\n",
    "    \"\"\"\n",
    "    Compute saliency maps for modelA and modelB and return matplotlib Figure objects.\n",
    "    Returns: (figA, figB)\n",
    "    If show=True the figures are displayed; otherwise they are not shown but still returned.\n",
    "    \"\"\"\n",
    "    if device is None:\n",
    "        device = next(modelA.parameters()).device\n",
    "\n",
    "    images_batch = images_batch.to(device)\n",
    "    B = images_batch.shape[0]\n",
    "    n_show = min(n_show, B)\n",
    "\n",
    "    # compute saliency for both models (uses your compute_saliency function)\n",
    "    salA, predsA, targetsA = compute_saliency(\n",
    "        modelA, images_batch, labels=labels_batch, device=device,\n",
    "        target=target, abs_val=True, smooth=smooth, n_samples=n_samples, stdev=stdev\n",
    "    )\n",
    "    salB, predsB, targetsB = compute_saliency(\n",
    "        modelB, images_batch, labels=labels_batch, device=device,\n",
    "        target=target, abs_val=True, smooth=smooth, n_samples=n_samples, stdev=stdev\n",
    "    )\n",
    "\n",
    "    images_cpu = images_batch.detach().cpu()\n",
    "\n",
    "    # ---------- Figure A: Inputs (top row) + ModelA saliency (bottom row) ----------\n",
    "    figA, axesA = plt.subplots(2, n_show, figsize=(3.5 * n_show, 6))\n",
    "    figA.suptitle(\"Top: input images — Bottom: ModelA saliency\", fontsize=14)\n",
    "    for i in range(n_show):\n",
    "        # input image (top)\n",
    "        ax = axesA[0, i] if n_show > 1 else axesA[0]\n",
    "        npimg = _img_to_numpy_for_display(images_cpu[i], unnormalize_fn)\n",
    "        if npimg.ndim == 2:\n",
    "            ax.imshow(npimg, cmap='gray')\n",
    "        else:\n",
    "            ax.imshow(np.clip(npimg, 0, 1))\n",
    "        ax.set_title(f\"Input #{i}\")\n",
    "        ax.axis('off')\n",
    "\n",
    "        # ModelA saliency (bottom)\n",
    "        ax = axesA[1, i] if n_show > 1 else axesA[1]\n",
    "        if npimg.ndim == 2:\n",
    "            ax.imshow(npimg, cmap='gray')\n",
    "            ax.imshow(salA[i], cmap='hot', alpha=0.6, vmin=0, vmax=1)\n",
    "        else:\n",
    "            ax.imshow(np.clip(npimg, 0, 1))\n",
    "            ax.imshow(salA[i], cmap='hot', alpha=0.6, vmin=0, vmax=1)\n",
    "\n",
    "        title_text = f\"A P:{predsA[i]}\"\n",
    "        if labels_batch is not None:\n",
    "            title_text += f\" T:{int(labels_batch[i].item())}\"\n",
    "        ax.set_title(title_text)\n",
    "        ax.axis('off')\n",
    "\n",
    "    plt.tight_layout(rect=[0,0,1,0.95])\n",
    "\n",
    "    # ---------- Figure B: ModelB saliency in one row ----------\n",
    "    figB, axesB = plt.subplots(1, n_show, figsize=(3.5 * n_show, 3.5))\n",
    "    figB.suptitle(\"ModelB saliency maps\", fontsize=14)\n",
    "    for i in range(n_show):\n",
    "        ax = axesB[i] if n_show > 1 else axesB\n",
    "        npimg = _img_to_numpy_for_display(images_cpu[i], unnormalize_fn)\n",
    "        if npimg.ndim == 2:\n",
    "            ax.imshow(npimg, cmap='gray')\n",
    "            ax.imshow(salB[i], cmap='hot', alpha=0.6, vmin=0, vmax=1)\n",
    "        else:\n",
    "            ax.imshow(np.clip(npimg, 0, 1))\n",
    "            ax.imshow(salB[i], cmap='hot', alpha=0.6, vmin=0, vmax=1)\n",
    "\n",
    "        title_text = f\"B P:{predsB[i]}\"\n",
    "        if labels_batch is not None:\n",
    "            title_text += f\" T:{int(labels_batch[i].item())}\"\n",
    "        ax.set_title(title_text)\n",
    "        ax.axis('off')\n",
    "\n",
    "    plt.tight_layout(rect=[0,0,1,0.95])\n",
    "\n",
    "    if show:\n",
    "        plt.show()  # display both figures\n",
    "    else:\n",
    "        # do not call plt.show() so figures remain available to save\n",
    "        pass\n",
    "\n",
    "    return figA, figB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "82e1885f-033b-44d3-bdac-1efc294f43d9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: figures/saliency_modelL.png and figures/saliency_modelM.png\n"
     ]
    }
   ],
   "source": [
    "images, labels = next(iter(test_loader))\n",
    "images = images[:6]\n",
    "labels = labels[:6]\n",
    "\n",
    "# compute saliency and get figure objects (do not auto-show so we can save)\n",
    "figA, figB = compare_models_saliency_return_figs(\n",
    "    modelL, modelM, images, labels,\n",
    "    device=device, target='pred', smooth=True, n_samples=30, stdev=0.12,\n",
    "    unnormalize_fn=None, n_show=6, show=False\n",
    ")\n",
    "\n",
    "# ensure output directory exists\n",
    "\n",
    "# save them\n",
    "figA.savefig(\"saliency_modelL.png\", dpi=300, bbox_inches=\"tight\")\n",
    "figB.savefig(\"saliency_modelM.png\", dpi=300, bbox_inches=\"tight\")\n",
    "\n",
    "# close to free memory\n",
    "plt.close(figA)\n",
    "plt.close(figB)\n",
    "\n",
    "print(\"Saved: figures/saliency_modelL.png and figures/saliency_modelM.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c0a76fe1-4eb1-4f9e-9104-087effc1b35a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saliency batches: 100%|██████████| 40/40 [00:00<00:00, 99.97it/s]\n",
      "Saliency batches: 100%|██████████| 40/40 [00:00<00:00, 82.57it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x400 with 21 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved saliency arrays and confidences to disk.\n"
     ]
    }
   ],
   "source": [
    "# Requires your compute_saliency() from the message and the per_class_average_saliency/plot_per_class_comparison functions.\n",
    "import numpy as np\n",
    "import torch\n",
    "from tqdm import tqdm  # optional for progress bar\n",
    "\n",
    "def collect_saliency_for_loader(model, dataloader, device=None,\n",
    "                                target='pred', smooth=False, n_samples=25, stdev=0.12,\n",
    "                                max_batches=None):\n",
    "    \"\"\"\n",
    "    Run compute_saliency() over dataloader and collect:\n",
    "      sal_all: (N, H, W) numpy, normalized [0,1]\n",
    "      preds_all: (N,) ints\n",
    "      confs_all: (N,) floats (max softmax prob)\n",
    "      labels_all: (N,) ints (true labels if provided by loader)\n",
    "    max_batches: optional limit for quick debugging\n",
    "    \"\"\"\n",
    "    model.eval()\n",
    "    if device is None:\n",
    "        device = next(model.parameters()).device\n",
    "\n",
    "    sal_list = []\n",
    "    preds_list = []\n",
    "    conf_list = []\n",
    "    labels_list = []\n",
    "\n",
    "    loss = torch.nn.CrossEntropyLoss()  # only to infer nothing; not used\n",
    "\n",
    "    for b_idx, (images, labels) in enumerate(tqdm(dataloader, desc=\"Saliency batches\")):\n",
    "        if (max_batches is not None) and (b_idx >= max_batches):\n",
    "            break\n",
    "\n",
    "        # compute saliency & preds using your function\n",
    "        sal_maps, preds_np, targets_used = compute_saliency(\n",
    "            model, images, labels=labels, device=device,\n",
    "            target=target, abs_val=True, smooth=smooth, n_samples=n_samples, stdev=stdev\n",
    "        )\n",
    "        # sal_maps: (B, H, W) numpy, preds_np: (B,), targets_used: (B,)\n",
    "\n",
    "        # compute confidences (max softmax) on the batch\n",
    "        with torch.no_grad():\n",
    "            images_dev = images.to(device)\n",
    "            logits = model(images_dev)\n",
    "            if logits.dim() == 1 or (logits.dim()==2 and logits.size(1) == 1):\n",
    "                probs = torch.sigmoid(logits.view(-1))\n",
    "                # For binary, treat confidence as prob for predicted class\n",
    "                confs = (probs > 0.5).float()  # boolean -> 0/1, but better to use prob itself:\n",
    "                confs = probs.cpu().numpy()\n",
    "            else:\n",
    "                probs = torch.softmax(logits, dim=1)\n",
    "                confs = probs.max(dim=1).values.cpu().numpy()  # (B,)\n",
    "\n",
    "        # append\n",
    "        sal_list.append(sal_maps)               # numpy (B,H,W)\n",
    "        preds_list.append(preds_np)             # numpy (B,)\n",
    "        conf_list.append(confs)                 # numpy (B,)\n",
    "        labels_list.append(labels.cpu().numpy())# numpy (B,)\n",
    "\n",
    "    # concat\n",
    "    sal_all   = np.concatenate(sal_list, axis=0)\n",
    "    preds_all = np.concatenate(preds_list, axis=0)\n",
    "    conf_all  = np.concatenate(conf_list, axis=0)\n",
    "    labels_all= np.concatenate(labels_list, axis=0)\n",
    "\n",
    "    return sal_all, preds_all, conf_all, labels_all\n",
    "\n",
    "# Example usage:\n",
    "# Assuming train/test loaders: test_loader (or val_loader)\n",
    "# and models: modelL, modelM already on device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "modelL.to(device)\n",
    "modelM.to(device)\n",
    "\n",
    "# Collect saliency for both models (set smooth=True for SmoothGrad if you want)\n",
    "salL, predsL, confL, labels = collect_saliency_for_loader(modelL, test_loader, device=device,\n",
    "                                                          target='pred', smooth=False, n_samples=25, stdev=0.12)\n",
    "salM, predsM, confM, labelsM = collect_saliency_for_loader(modelM, test_loader, device=device,\n",
    "                                                          target='pred', smooth=False, n_samples=25, stdev=0.12)\n",
    "\n",
    "# Sanity: labels should match\n",
    "assert np.array_equal(labels, labelsM), \"Labels mismatch between runs (shouldn't happen).\"\n",
    "\n",
    "# Now you have:\n",
    "#   salL: (N, H, W)\n",
    "#   salM: (N, H, W)\n",
    "#   labels: (N,)\n",
    "\n",
    "# -------------------------\n",
    "# Compute per-class averages and plot (re-using functions from earlier)\n",
    "# -------------------------\n",
    "# If you don't have per_class_average_saliency/plot_per_class_comparison defined in the cell,\n",
    "# paste the definitions from the earlier message before running these lines.\n",
    "\n",
    "avgL, counts = per_class_average_saliency(salL, labels, num_classes=10, normalize=\"max\")\n",
    "avgM, _      = per_class_average_saliency(salM, labels, num_classes=10, normalize=\"max\")\n",
    "\n",
    "# Plot side-by-side comparison (top row ModelL, bottom row ModelM)\n",
    "plot_per_class_comparison(avgL, avgM, counts, cmap=\"hot\", show_counts=True)\n",
    "\n",
    "# -------------------------\n",
    "# Bonus: save arrays for later\n",
    "# -------------------------\n",
    "np.save(\"saliency_modelL.npy\", salL)\n",
    "np.save(\"saliency_modelM.npy\", salM)\n",
    "np.save(\"labels.npy\", labels)\n",
    "np.save(\"predsL.npy\", predsL)\n",
    "np.save(\"predsM.npy\", predsM)\n",
    "np.save(\"confsL.npy\", confL)\n",
    "np.save(\"confsM.npy\", confM)\n",
    "\n",
    "print(\"Saved saliency arrays and confidences to disk.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bae4f030-c936-4b95-a7c2-721894758862",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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