def stack_discriminator_layers(init):
model = Sequential(init_method=init)
model.add(
Conv2D(64, kernel_size=(5, 5), padding='same', input_shape=img_dims))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(5, 5), padding='same'))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(2))
model.add(Activation('sigmoid'))
return model
model = Sequential(init_method='he_uniform')
model.add(
Conv2D(filters=32,
kernel_size=(3, 3),
activation='relu',
input_shape=(1, 8, 8),
padding='same'))
model.add(Dropout(0.25))
model.add(BatchNormalization())
model.add(
Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(10, activation='softmax')) # 10 digits classes
model.compile(loss='categorical_crossentropy', optimizer=opt)
model_epochs = 12
fit_stats = model.fit(train_data.reshape(-1, 1, 8, 8),
one_hot(train_label),
batch_size=128,
epochs=model_epochs,
validation_data=(test_data.reshape(-1, 1, 8, 8),
one_hot(test_label)),
shuffle_data=True)
