CNN.py

# import libraries
import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = np.expand_dims(x_train, axis=-1)
x_test = np.expand_dims(x_test, axis=-1)
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
num_classes = 10


# create the CNN model:
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

# compile the model:

model.compile(loss=tf.keras.losses.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])

# train the model:

model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))


# evaluate the model:

test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=0)
print(f'Test loss: {test_loss}')
print(f'Test accuracy: {test_accuracy}')