diff --git a/MNIST_CNN.ipynb b/MNIST_CNN.ipynb
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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "authorship_tag": "ABX9TyPi1NMWMjcq5xT+iIDJb1ha",
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "9p34bGWawZHo"
+ },
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import datasets, layers, models\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from tensorflow import keras\n",
+ "\n",
+ "(X_train, y_train) , (X_test, y_test) = keras.datasets.mnist.load_data()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Cm6n7mxd24uI",
+ "outputId": "d8ea3c36-2392-4d13-8c75-aec126a8d7c9"
+ },
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
+ "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "X_train = X_train / 255\n",
+ "X_test = X_test / 255"
+ ],
+ "metadata": {
+ "id": "Y32GFU2724wQ"
+ },
+ "execution_count": 10,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "X_train_flattened = X_train.reshape(len(X_train), 28*28)\n",
+ "X_test_flattened = X_test.reshape(len(X_test), 28*28)"
+ ],
+ "metadata": {
+ "id": "JhDxM0Ci24z2"
+ },
+ "execution_count": 11,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model = keras.Sequential([\n",
+ " layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
+ " layers.MaxPooling2D((2, 2)),\n",
+ "\n",
+ " layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
+ " layers.MaxPooling2D((2, 2)),\n",
+ "\n",
+ " keras.layers.Flatten(),\n",
+ " keras.layers.Dense(100, activation='relu'),\n",
+ " keras.layers.Dense(10, activation='softmax')\n",
+ "])\n",
+ "\n",
+ "model.compile(optimizer='adam',\n",
+ " loss='sparse_categorical_crossentropy',\n",
+ " metrics=['accuracy'])\n",
+ "\n",
+ "model.fit(X_train, y_train, epochs=5)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "xiL69z4y8uUN",
+ "outputId": "cf022306-a8f9-4740-f650-fd8b5ec24dc5"
+ },
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.11/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+ " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/5\n",
+ "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 28ms/step - accuracy: 0.9072 - loss: 0.3004\n",
+ "Epoch 2/5\n",
+ "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 28ms/step - accuracy: 0.9866 - loss: 0.0442\n",
+ "Epoch 3/5\n",
+ "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 28ms/step - accuracy: 0.9905 - loss: 0.0289\n",
+ "Epoch 4/5\n",
+ "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 28ms/step - accuracy: 0.9941 - loss: 0.0197\n",
+ "Epoch 5/5\n",
+ "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 28ms/step - accuracy: 0.9955 - loss: 0.0144\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from sklearn.metrics import confusion_matrix , classification_report\n",
+ "import numpy as np\n",
+ "y_pred = model.predict(X_test)\n",
+ "y_pred_classes = [np.argmax(element) for element in y_pred]\n",
+ "\n",
+ "print(\"Classification Report: \\n\", classification_report(y_test, y_pred_classes))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7UA-puJ28uQl",
+ "outputId": "d01495a8-6f7d-4932-84b8-d6f6c4ff9a49"
+ },
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step\n",
+ "Classification Report: \n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.99 1.00 0.99 980\n",
+ " 1 1.00 1.00 1.00 1135\n",
+ " 2 0.99 0.99 0.99 1032\n",
+ " 3 0.99 1.00 0.99 1010\n",
+ " 4 0.99 0.99 0.99 982\n",
+ " 5 0.99 0.99 0.99 892\n",
+ " 6 1.00 0.98 0.99 958\n",
+ " 7 0.98 0.99 0.99 1028\n",
+ " 8 0.99 0.99 0.99 974\n",
+ " 9 0.99 0.99 0.99 1009\n",
+ "\n",
+ " accuracy 0.99 10000\n",
+ " macro avg 0.99 0.99 0.99 10000\n",
+ "weighted avg 0.99 0.99 0.99 10000\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model.evaluate(X_test,y_test)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "M5CcUU1L8uOX",
+ "outputId": "e9f1e805-5974-4da1-e8e4-8ec18aa01f02"
+ },
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - accuracy: 0.9885 - loss: 0.0343\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[0.026914624497294426, 0.9914000034332275]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 17
+ }
+ ]
+ }
+ ]
+}
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