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MNIST_CNN_Image Classification #28

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239 changes: 239 additions & 0 deletions 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": [
"<a href=\"https://colab.research.google.com/github/Ishita-Si/deep-learning-keras-tf-tutorial/blob/master/MNIST_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"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": [
"<keras.src.callbacks.history.History at 0x7925e7557110>"
]
},
"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
}
]
}
]
}