Docs change examples to notebooks

examples/classification-regression/mnist_loading_saved_model.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MNIST Loading Saved Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-21T12:52:21.706906Z",
+     "start_time": "2024-04-21T12:52:18.726598200Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.datasets import mnist  # Dataset for testing\n",
+    "\n",
+    "from neuralnetlib.model import Model\n",
+    "from neuralnetlib.preprocessing import one_hot_encode\n",
+    "from neuralnetlib.utils import train_test_split\n",
+    "from neuralnetlib.metrics import accuracy_score, confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Loading the MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-21T12:52:21.915810200Z",
+     "start_time": "2024-04-21T12:52:21.706906Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-21T12:52:22.072282500Z",
+     "start_time": "2024-04-21T12:52:21.916810900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_train = x_train.reshape(-1, 28 * 28) / 255.0\n",
+    "x_test = x_test.reshape(-1, 28 * 28) / 255.0\n",
+    "y_train = one_hot_encode(y_train, num_classes=10)\n",
+    "y_test = one_hot_encode(y_test, num_classes=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Split the training data into training and validation sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-21T12:52:22.233389700Z",
+     "start_time": "2024-04-21T12:52:22.073284800Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "_, x_val, _, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Load the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-21T12:52:22.258467800Z",
+     "start_time": "2024-04-21T12:52:22.234388100Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model = Model.load('my_mnist_model.npz')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Predict and evaluate on the validation set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-21T12:52:22.323518700Z",
+     "start_time": "2024-04-21T12:52:22.257467100Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Accuracy: 0.899\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred_val = model.predict(x_val)\n",
+    "accuracy_val = accuracy_score(y_pred_val, y_val)\n",
+    "print(f'Validation Accuracy: {accuracy_val}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Optionally, you can still evaluate on the test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-21T12:52:22.393768500Z",
+     "start_time": "2024-04-21T12:52:22.318518600Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy: 0.8863\n",
+      "Confusion Matrix:\n",
+      "[[ 937    0    0    1   11    7    2   18    1    3]\n",
+      " [   0 1097    3    4    0    3    2    4   19    3]\n",
+      " [  13    9  858   36   26    1   23   38   16   12]\n",
+      " [   8    6   18  899    2   33    2   16   12   14]\n",
+      " [   1    0    1    0  944    0    7    2    1   26]\n",
+      " [  19    0    0   82   30  701   12    5   23   20]\n",
+      " [  18    2    0    0   70   15  849    1    2    1]\n",
+      " [   0    9   10    5   15    0    0  945    4   40]\n",
+      " [   6   22    3    3   37   26    9    2  803   63]\n",
+      " [   3    2    1   11  137    2    0   15    8  830]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred_test = model.predict(x_test)\n",
+    "accuracy_test = accuracy_score(y_pred_test, y_test)\n",
+    "print(f'Test Accuracy: {accuracy_test}')\n",
+    "print(f'Confusion Matrix:\\n{confusion_matrix(y_pred_test, y_test)}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}