cnn.py

#import the libraries
import tensorflow as tf
from keras import layers, models
from keras.datasets import mnist

#load Dataset
(x_train,y_train),(x_test,y_test) = mnist.load_data()
x_train ,x_test = x_train / 255.0, x_test / 255.0 #Normalize
x_train = x_train.reshape(-1,28,28,1)
x_test = x_test.reshape(-1, 28, 28, 1)
model = models.Sequential([
    layers.Conv2D(32,(3,3), activation = 'relu', input_shape=(28 ,28 ,1)),
    layers.MaxPooling2D((2,2)),
    layers.Conv2D(64, (3,3), activation = 'relu'),
    layers.MaxPooling2D((2,2)),
    layers.Flatten(),
    layers.Dense(64, activation = 'relu'),
    layers.Dense(10, activation = 'softmax')
])
#compile the model 
model.compile(optimizer='adam', loss ='sparse_categorical_crossentropy', metrics = ['accuracy'])

#train the model 
model.fit(x_train, y_train, epochs=5, batch_size = 64, validation_split = 0.2)

#evaluate the model 
test_loss, test_acc = model.evaluate(x_test, y_test)
print("Test Accuracy: ", test_acc)