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 discriminator(self, X, reuse=False):
with tf.variable_scope('discriminator', reuse=reuse):
layer = Stacker(X)
layer.conv_block(128, CONV_FILTER_5522, lrelu)
layer.conv_block(256, CONV_FILTER_5522, lrelu)
layer.reshape([self.batch_size, -1])
layer.linear(1)
layer.sigmoid()
return layer.last_layer
def discriminator(self, X, net_shapes, reuse=False, name='discriminator'):
with tf.variable_scope(name, reuse=reuse):
layer = Stacker(flatten(X))
for shape in net_shapes:
layer.linear(shape)
layer.linear(1)
layer.sigmoid()
return layer.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 generator(self, z, net_shapes, reuse=False, name='generator'):
with tf.variable_scope(name, reuse=reuse):
layer = Stacker(z)
for shape in net_shapes:
layer.linear(shape)
layer.linear(self.X_flatten_size)
layer.sigmoid()
layer.reshape(self.Xs_shape)
return layer.last_layer
def discriminator(self, X, Y, reuse=False):
with tf.variable_scope('discriminator', reuse=reuse):
Y = linear(Y, self.input_h * self.input_w)
Y = reshape(Y, [self.batch_size, self.input_h, self.input_w, 1])
layer = Stacker(tf.concat((X, Y), axis=3))
layer.conv_block(128, CONV_FILTER_5522, lrelu)
layer.conv_block(256, CONV_FILTER_5522, lrelu)
layer.reshape([self.batch_size, -1])
layer.linear(1)
layer.sigmoid()
return layer.last_layer
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 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 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
