def discriminator_cate(self, zs, reuse=False, name='discriminator_cate'):
with tf.variable_scope(name, reuse=reuse):
layer = Stacker(zs)
layer.linear_block(256, relu)
layer.linear_block(256, relu)
layer.linear(1)
layer.sigmoid()
return layer.last_layer
def Q_function(self, X_gen, reuse=False):
with tf.variable_scope('Q_function', reuse=reuse):
layer = Stacker(X_gen)
layer.linear_block(128, relu)
layer.linear_block(128, relu)
code_logit = layer.linear(10 + 2)
code = layer.softmax()
return code, code_logit
def decoder(self, zs, net_shapes, reuse=False, name='decoder'):
with tf.variable_scope(name, reuse=reuse):
stack = Stacker(zs)
for shape in net_shapes:
stack.linear_block(shape, relu)
stack.linear_block(self.X_flatten_size, sigmoid)
stack.reshape(self.Xs_shape)
return stack.last_layer
def encoder(self, Xs, net_shapes, reuse=False, name='encoder'):
with tf.variable_scope(name, reuse=reuse):
stack = Stacker(Xs)
stack.flatten()
for shape in net_shapes:
stack.linear_block(shape, relu)
stack.linear_block(self.latent_code_size, relu)
return stack.last_layer
def classifier(self, Xs, net_shapes, name='classifier'):
with tf.variable_scope(name):
layer = Stacker(flatten(Xs))
for net_shape in net_shapes:
layer.linear_block(net_shape, relu)
layer.linear(self.Y_size)
logit = layer.last_layer
h = softmax(logit)
return logit, h
def encoder(self, Xs, net_shapes, reuse=False, name='encoder'):
with tf.variable_scope(name, reuse=reuse):
stack = Stacker(Xs)
stack.flatten()
for shape in net_shapes:
stack.linear_block(shape, relu)
stack.linear_block(self.z_size + self.Y_size, relu)
zs = stack.last_layer[:, :self.z_size]
Ys_gen = stack.last_layer[:, self.z_size:]
hs = softmax(Ys_gen)
return zs, Ys_gen, hs
def discriminator_gauss(self,
zs,
net_shapes,
reuse=False,
name='discriminator_gauss'):
with tf.variable_scope(name, reuse=reuse):
layer = Stacker(zs)
for shape in net_shapes:
layer.linear_block(shape, relu)
layer.linear(1)
layer.sigmoid()
return layer.last_layer
def classifier(self, x, dropout_rate):
with tf.variable_scope('classifier'):
layer = Stacker(x)
layer.linear_block(128, lrelu)
layer.dropout(dropout_rate)
layer.linear_block(128, relu)
layer.dropout(dropout_rate)
# layer.linear_block(32, lrelu)
# layer.dropout(dropout_rate)
#
# layer.linear_block(16, lrelu)
# layer.dropout(dropout_rate)
layer.linear(2)
logit = layer.last_layer
h = softmax(logit)
return logit, h
def encoder(self, Xs, name='encoder'):
with tf.variable_scope(name):
stack = Stacker(Xs)
stack.flatten()
stack.linear_block(512, relu)
stack.linear_block(256, relu)
stack.linear_block(128, relu)
stack.linear_block(self.code_size, relu)
return stack.last_layer
def decoder(self, zs, Ys, reuse=False, name='decoder'):
with tf.variable_scope(name, reuse=reuse):
stack = Stacker(concat((zs, Ys), axis=1))
stack.linear_block(128, relu)
stack.linear_block(256, relu)
stack.linear_block(512, relu)
stack.linear_block(self.X_flatten_size, sigmoid)
stack.reshape(self.Xs_shape)
return stack.last_layer
def encoder(self, Xs, Ys, name='encoder'):
with tf.variable_scope(name):
stack = Stacker(concat((flatten(Xs), Ys), axis=1))
stack.linear_block(512, relu)
stack.linear_block(256, relu)
stack.linear_block(128, relu)
stack.linear_block(self.z_size * 2, relu)
h = stack.last_layer
mean = h[:, :self.z_size]
std = tf.nn.softplus(h[:, self.z_size:])
return mean, std
