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
def stack_generator_layers(init):
model = Sequential(init_method=init)
model.add(Dense(128 * 7 * 7, input_shape=(latent_dim, )))
model.add(Activation('leaky_relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Reshape((128, 7, 7)))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=(5, 5), padding='same'))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation('leaky_relu'))
model.add(UpSampling2D())
model.add(Conv2D(img_channels, kernel_size=(5, 5), padding='same'))
model.add(Activation('tanh'))
return model
from ztlearn.dl.layers import BatchNormalization, Conv2D
from ztlearn.dl.layers import Dropout, Dense, Flatten, MaxPooling2D
data = datasets.load_digits()
plot_digits_img_samples(data)
train_data, test_data, train_label, test_label = train_test_split(
data.data, data.target, test_size=0.33, random_seed=5)
opt = register_opt(optimizer_name='adam', momentum=0.01, learning_rate=0.001)
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)