3. build a network

mnist03.py

model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=(28,28,1)))
model.add(Activation('relu'))
BatchNormalization(axis=-1)
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

BatchNormalization(axis=-1)
model.add(Conv2D(64,(3, 3)))
model.add(Activation('relu'))
BatchNormalization(axis=-1)
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
# Fully connected layer

BatchNormalization()
model.add(Dense(512))
model.add(Activation('relu'))
BatchNormalization()
model.add(Dropout(0.2))
model.add(Dense(10))

model.add(Activation('softmax'))