def vanilla(mark, bn=False):
z_dim = 100
model = VAE(z_dim=z_dim,
mark=mark,
classes=0,
sample_shape=[784],
output_shape=[28, 28, 1])
# Define encoder
model.Q.add(Linear(output_dim=128))
model.Q.add(Activation.ReLU())
fork = Fork(name='mu_sigma')
fork.add('mu', Linear(output_dim=z_dim))
fork.add('sigma', Linear(output_dim=z_dim))
model.Q.add(fork)
# Define decoder
model.P.add(Linear(output_dim=128))
model.P.add(Activation.ReLU())
model.P.add(Linear(output_dim=784))
model.P.add(Activation('sigmoid'))
# Build model
model.build()
return model
def ConvLayer(filters, bn=False):
model.add(
Conv2D(filters=filters,
kernel_size=5,
padding='same',
kernel_regularizer=regularizers.L2(strength=strength)))
if bn:
model.add(BatchNorm())
model.add(Activation.ReLU())
def dcgan(mark):
# Initiate model
model = GAN(z_dim=100, sample_shape=[28, 28, 1], mark=mark, classes=10)
# Define generator
model.G.add(Linear(output_dim=7 * 7 * 128))
model.G.add(Reshape(shape=[7, 7, 128]))
model.G.add(BatchNorm())
model.G.add(Activation.ReLU())
model.G.add(Deconv2D(filters=128, kernel_size=5, strides=2,
padding='same'))
model.G.add(BatchNorm())
model.G.add(Activation.ReLU())
model.G.add(Deconv2D(filters=1, kernel_size=5, strides=2, padding='same'))
model.G.add(Activation('sigmoid'))
# model.G.add(Activation('tanh'))
# model.G.add(Rescale(from_scale=[-1., 1.], to_scale=[0., 1.]))
# Define discriminator
# model.D.add(Rescale(from_scale=[0., 1.], to_scale=[-1., 1.]))
model.D.add(Conv2D(filters=128, kernel_size=5, strides=2, padding='same'))
model.D.add(Activation.LeakyReLU())
model.D.add(Conv2D(filters=128, kernel_size=5, strides=2, padding='same'))
model.D.add(BatchNorm())
model.D.add(Activation.LeakyReLU())
model.D.add(Reshape(shape=[7 * 7 * 128]))
model.D.add(Linear(output_dim=1))
model.D.add(Activation('sigmoid'))
# Build model
optimizer = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5)
model.build(loss=pedia.cross_entropy,
G_optimizer=optimizer,
D_optimizer=optimizer)
return model
def mlp00(mark):
# Define model
model = TDPlayer(mark=mark)
model.add(Input(sample_shape=[15, 15]))
model.add(Flatten())
model.add(Linear(225))
model.add(Activation.ReLU())
model.add(Linear(225))
model.add(Activation.ReLU())
model.add(Linear(1))
model.add(Activation('sigmoid'))
# Build model
model.build()
return model
def ka_convnet(mark):
model = Classifier(mark=mark)
model.add(Input(sample_shape=config.sample_shape))
strength = 1e-5
def ConvLayer(filters, bn=False):
model.add(
Conv2D(filters=filters,
kernel_size=5,
padding='same',
kernel_regularizer=regularizers.L2(strength=strength)))
if bn:
model.add(BatchNorm())
model.add(Activation.ReLU())
# Define structure
ConvLayer(32)
model.add(Dropout(0.5))
ConvLayer(32, False)
model.add(Dropout(0.5))
model.add(MaxPool2D(2, 2, 'same'))
ConvLayer(64, True)
model.add(Dropout(0.5))
model.add(MaxPool2D(2, 2, 'same'))
model.add(Flatten())
model.add(Linear(128))
model.add(Activation.ReLU())
# model.add(Dropout(0.5))
model.add(Linear(10))
# Build model
model.build(optimizer=tf.train.AdamOptimizer(learning_rate=1e-4))
return model
