2.1 Hyperparameters

# Hyperparameters
training_epochs = 5 # Total number of training epochs
learning_rate = 0.003 # The learning rate

# create a model
def create_model():
    model = Sequential()

    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
    model.add(MaxPooling2D((2, 2)))
    model.add(Conv2D(32, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))
    model.add(BatchNormalization())

    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(10, activation='softmax'))

    model.compile(optimizer=Adam(learning_rate), loss='categorical_crossentropy', metrics=['accuracy'])
    return model

model = create_model()
model.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 26, 26, 32)        320       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 11, 11, 32)        9248      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 32)          0         
_________________________________________________________________
batch_normalization (BatchNo (None, 5, 5, 32)          128       
_________________________________________________________________
flatten (Flatten)            (None, 800)               0         
_________________________________________________________________
dense (Dense)                (None, 128)               102528    
_________________________________________________________________
dense_1 (Dense)              (None, 10)                1290      
=================================================================
Total params: 113,514
Trainable params: 113,450
Non-trainable params: 64
_________________________________________________________________