def discriminator(self,
x_tensor,
z_tensor,
is_training):
""" Build discriminator to compute probabilities that given samples (x, z) are from the encoder
Args:
x_tensor: Tensor of shape [batch, height, width, channel], a batch of data points
z_tensor: Tensor of shape [batch, latent_dim], a batch of latent points
is_training: Boolean, whether the output of this function will be used for training or inference
Returns:
probs: Tensor of shape [batch, 1], probability that given data points are from a true data distribution
disc_features: Tensor of shape [batch, feature_dim], discriminative feature vectors
"""
with arg_scope(default_arg_scope(is_training, activation_fn=leaky_relu)):
with tf.variable_scope('discriminator', reuse=tf.AUTO_REUSE):
with tf.variable_scope('network_x'):
h = conv2d(x_tensor, num_outputs=64, kernel_size=3, stride=2, scope='conv1')
h = conv2d(h, num_outputs=128, kernel_size=3, stride=2, scope='conv2')
h = conv2d(h, num_outputs=256, kernel_size=3, stride=2, scope='conv3')
h = conv2d(h, num_outputs=64, kernel_size=3, stride=1, scope='conv4')
x_features = tf.layers.flatten(h, name='flatten')
with tf.variable_scope('network_z'):
z_features = fully_connected(z_tensor, num_outputs=512, normalizer_fn=None, scope='fc')
with tf.variable_scope('network_relation'):
total_features = tf.concat([x_features, z_features], axis=-1)
h = dropout(total_features, keep_prob=0.5, is_training=is_training, scope='dropout1')
h = fully_connected(h, num_outputs=1024, normalizer_fn=None, scope='fc')
disc_features = dropout(h, keep_prob=0.5, is_training=is_training, scope='dropout2')
logits = fully_connected(disc_features, num_outputs=1, activation_fn=None, normalizer_fn=None, scope='logit')
probs = tf.math.sigmoid(logits, name='sigmoid')
return probs, disc_features
def decoder(self, z_tensor, is_training):
""" Generator which maps a latent point to a data point
Args:
z_tensor: Tensor of shape [batch, latent_dim], a batch of latent points
is_training: Boolean, whether the output of this function will be used for training or inference
Returns:
output_tensor: Tensor of shape [batch, height, width, channel]
"""
with arg_scope(default_arg_scope(is_training)):
with tf.variable_scope('generator', reuse=tf.AUTO_REUSE):
h = fully_connected(z_tensor,
num_outputs=7 * 7 * 128,
scope='transform')
h = tf.reshape(h, shape=[-1, 7, 7, 128], name='reshape')
h = conv2d_transpose(h,
num_outputs=64,
kernel_size=4,
stride=2,
scope='conv1_transpose')
h = conv2d_transpose(h,
num_outputs=1,
kernel_size=4,
stride=2,
activation_fn=None,
normalizer_fn=None,
scope='conv2_transpose')
output_tensor = tf.math.tanh(h, name='tanh')
return output_tensor
def encoder(self,
x_tensor,
is_training):
""" Encoder which maps a data point to a latent point
Args:
x_tensor: Tensor of shape [batch, height, width, channel], a batch of data points
is_training: Boolean, whether the output of this function will be used for training or inference
Returns:
z_enc: Tensor of shape [batch, latent_dim]
"""
with arg_scope(default_arg_scope(is_training)):
with tf.variable_scope('encoder', reuse=tf.AUTO_REUSE):
h = conv2d(x_tensor, num_outputs=64, kernel_size=3, stride=2, scope='conv1')
h = conv2d(h, num_outputs=128, kernel_size=3, stride=2, scope='conv2')
h = conv2d(h, num_outputs=256, kernel_size=3, stride=2, scope='conv3')
h = conv2d(h, num_outputs=64, kernel_size=3, stride=1, scope='conv4')
conv_features = tf.layers.flatten(h, name='flatten')
z_enc = fully_connected(conv_features, num_outputs=self.latent_dim,
activation_fn=None, scope='encoding')
return z_enc