def encoder(self, x, is_training=True, reuse=False):
with tf.variable_scope("encoder", reuse=reuse):
if self.verbose: print(x.shape)
# Layer 1
net = layers.conv2d(x, self.conv_dim >> 1, name='en_conv1')
net = tf.nn.leaky_relu(net)
if self.verbose: print(net.shape)
# Layer 2
net = layers.conv2d(net, self.conv_dim, name='en_conv2')
net = tf.nn.leaky_relu(net)
if self.verbose: print(net.shape)
# Layer 3
net = layers.flatten(net)
if self.verbose: print(net.shape)
# Layer 4
net = layers.linear(net, self.linear_dim, scope='en_fc1')
net = tf.nn.leaky_relu(net)
if self.verbose: print(net.shape)
# Layer 5
net = layers.linear(net, self.latent_dim, scope='en_fc2')
net = tf.identity(net)
if self.verbose: print(net.shape)
return net
def encoder(self, x, is_training=True, reuse=False):
with tf.variable_scope("encoder", reuse=reuse):
if self.verbose: print(x.shape)
# Layer 0: Flattening
net = layers.flatten(x)
if self.verbose: print(net.shape, ": Flatten")
# Layer 1
net = layers.linear(net,
self.linear_dim,
scope='en_fc1',
w_initializer=self.w_initializer,
b_initializer=self.b_initializer)
net = tf.nn.tanh(net)
if self.verbose: print(net.shape)
# Layer 2
net = layers.linear(net,
self.linear_dim >> 1,
scope='en_fc2',
w_initializer=self.w_initializer,
b_initializer=self.b_initializer)
net = tf.identity(net, name="main_out")
if self.verbose: print(net.shape)
return net
def encoder_v2(self, x, is_training=True, reuse=False):
with tf.variable_scope("encoder_v2", reuse=reuse):
if self.verbose: print(x.shape)
# Layer 1
net = layers.conv2d(x, self.conv_dim >> 1, name='en_conv1')
net = tf.nn.leaky_relu(net)
if self.verbose: print(net.shape)
# Layer 2
net = layers.conv2d(net, self.conv_dim, name='en_conv2')
net = layers.batch_norm(net,
is_training=is_training,
scope='en_bn2')
net = tf.nn.leaky_relu(net)
if self.verbose: print(net.shape)
# Layer 3
net = layers.flatten(net)
if self.verbose: print(net.shape)
# Layer 4
net = layers.linear(net, self.linear_dim, scope='en_fc3')
net = layers.batch_norm(net,
is_training=is_training,
scope='en_bn3')
net = tf.nn.leaky_relu(net)
if self.verbose: print(net.shape)
# Layer 5
out_logit = layers.linear(net, self.latent_dim, scope='en_fc4')
out = tf.nn.sigmoid(out_logit, name="main_out")
if self.verbose: print(out.shape)
return out
def encoder(self,x, is_training=True, reuse=False):
with tf.variable_scope("encoder", reuse=reuse):
if self.verbose: print(x.shape)
# Encoder Space
net = layers.flatten(x)
if self.verbose: print(net.shape,": Flatten")
# Latent Space
net = layers.linear(net, self.latent_dim, scope = 'en_fc1',
w_initializer = self.w_initializer,
b_initializer = self.b_initializer)
net = tf.nn.relu(net, name = "main_out")
if self.verbose: print(net.shape)
return net
def encoder(self, x, is_training=True, reuse=False):
with tf.variable_scope("encoder", reuse=reuse):
if self.verbose: print(x.shape)
net = tf.keras.layers.Conv2D(
filters=32,
kernel_size=[5, 5],
strides=2,
padding='same',
kernel_initializer='glorot_uniform')(x)
if self.verbose: print(net.shape)
# net = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(net)
# if self.verbose: print(net.shape)
net = tf.keras.layers.Conv2D(
filters=16,
kernel_size=[5, 5],
strides=2,
padding='same',
kernel_initializer='glorot_uniform')(net)
if self.verbose: print(net.shape)
# net = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(net)
# if self.verbose: print(net.shape)
# net = tf.keras.layers.Conv2D(filters = 8,
# kernel_size = [5,5],
# strides = 4,
# padding = 'same',
# kernel_initializer='glorot_uniform')(net)
# if self.verbose: print(net.shape)
self.main_shape = [
-1,
int(net.shape[1]),
int(net.shape[2]),
int(net.shape[3])
] # will be used by the decoder
# net = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(net)
# if self.verbose: print(net.shape)
net = layers.flatten(net)
if self.verbose: print(net.shape)
net = layers.linear(net,
self.latent_dim << 2,
scope='en_fc1',
w_initializer=self.w_initializer,
b_initializer=self.b_initializer)
net = tf.nn.relu(net)
if self.verbose: print(net.shape)
net = layers.linear(net,
self.latent_dim << 1,
scope='en_fc2',
w_initializer=self.w_initializer,
b_initializer=self.b_initializer)
net = tf.nn.relu(net)
if self.verbose: print(net.shape)
net = layers.linear(net,
self.latent_dim,
scope='en_fc3',
w_initializer=self.w_initializer,
b_initializer=self.b_initializer)
net = tf.nn.relu(net, name='main_out')
if self.verbose: print(net.shape)
return net