def build(self, z, train):
with tf.variable_scope(self.scope_name, reuse=tf.AUTO_REUSE):
batch_size = tf.shape(z)[0]
layers = [z]
with tf.variable_scope("layer0"):
layers.append(linear(layers[-1], 128))
layers.append(tf.nn.relu(layers[-1]))
layers.append(batch_norm()(layers[-1], train=train))
with tf.variable_scope("layer1"):
layers.append(linear(layers[-1], 4 * 4 * 64))
layers.append(tf.nn.relu(layers[-1]))
layers.append(batch_norm()(layers[-1], train=train))
layers.append(tf.reshape(layers[-1], [-1, 4, 4, 64]))
with tf.variable_scope("layer2"):
layers.append(
deconv2d(layers[-1], [batch_size, 8, 8, 64],
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel))
layers.append(lrelu(layers[-1]))
layers.append(batch_norm()(layers[-1], train=train))
with tf.variable_scope("layer3"):
layers.append(
deconv2d(layers[-1], [batch_size, 16, 16, 32],
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel))
layers.append(lrelu(layers[-1]))
layers.append(batch_norm()(layers[-1], train=train))
with tf.variable_scope("layer4"):
layers.append(
deconv2d(layers[-1], [batch_size, 32, 32, 32],
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel))
layers.append(lrelu(layers[-1]))
layers.append(batch_norm()(layers[-1], train=train))
with tf.variable_scope("layer5"):
layers.append(
deconv2d(layers[-1], [
batch_size, self.output_height, self.output_width,
self.output_depth
],
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel))
layers.append(tf.nn.sigmoid(layers[-1]))
return layers[-1], layers
def build(self, images, train):
with tf.variable_scope(self.scope_name, reuse=tf.AUTO_REUSE):
layers = [images]
with tf.variable_scope("layer0"):
layers.append(conv2d(
layers[-1],
32,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel,
sn_op=None))
layers.append(lrelu(layers[-1]))
with tf.variable_scope("layer1"):
layers.append(conv2d(
layers[-1],
32,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel,
sn_op=None))
layers.append(lrelu(layers[-1]))
layers.append(batch_norm()(layers[-1], train=train))
with tf.variable_scope("layer2"):
layers.append(conv2d(
layers[-1],
64,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel,
sn_op=None))
layers.append(lrelu(layers[-1]))
layers.append(batch_norm()(layers[-1], train=train))
with tf.variable_scope("layer3"):
layers.append(conv2d(
layers[-1],
64,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel,
sn_op=None))
layers.append(lrelu(layers[-1]))
layers.append(batch_norm()(layers[-1], train=train))
with tf.variable_scope("layer4"):
layers.append(linear(layers[-1], 128, sn_op=None))
layers.append(lrelu(layers[-1]))
layers.append(batch_norm()(layers[-1], train=train))
with tf.variable_scope("layer5"):
layers.append(linear(layers[-1], self.output_length))
return layers[-1], layers
def build(self, image, train):
with tf.variable_scope(self.scope_name, reuse=tf.AUTO_REUSE):
layers = [image]
with tf.variable_scope("layer0"):
layers.append(
conv2d(layers[-1],
self.start_depth,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel))
layers.append(lrelu(layers[-1]))
with tf.variable_scope("layer1"):
layers.append(
conv2d(layers[-1],
self.start_depth * 2,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel))
layers.append(batch_norm()(layers[-1], train=train))
layers.append(lrelu(layers[-1]))
with tf.variable_scope("layer2"):
layers.append(
conv2d(layers[-1],
self.start_depth * 4,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel))
layers.append(batch_norm()(layers[-1], train=train))
layers.append(lrelu(layers[-1]))
with tf.variable_scope("layer3"):
layers.append(
conv2d(layers[-1],
self.start_depth * 8,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel))
layers.append(batch_norm()(layers[-1], train=train))
layers.append(lrelu(layers[-1]))
with tf.variable_scope("layer4"):
layers.append(linear(layers[-1], self.output_length))
return layers[-1], layers
def build(self, z, train):
with tf.variable_scope(self.scope_name, reuse=tf.AUTO_REUSE):
layers = [z]
for i in range(self.num_layers - 1):
with tf.variable_scope("layer{}".format(i)):
layers.append(linear(layers[-1], self.l_dim))
layers.append(lrelu(layers[-1]))
with tf.variable_scope("layer{}".format(self.num_layers - 1)):
layers.append(linear(layers[-1], 2))
logit = layers[-1]
layers.append(tf.nn.softmax(layers[-1]))
prob = layers[-1]
return logit, prob, layers
def build(self, image, train, sn_op):
with tf.variable_scope(self.scope_name, reuse=tf.AUTO_REUSE):
with tf.variable_scope("shared"):
layers = [image]
with tf.variable_scope("layer0"):
layers.append(
conv2d(layers[-1],
self.start_depth,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel,
sn_op=sn_op))
layers.append(lrelu(layers[-1]))
with tf.variable_scope("layer1"):
layers.append(
conv2d(layers[-1],
self.start_depth * 2,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel,
sn_op=sn_op))
layers.append(lrelu(layers[-1]))
with tf.variable_scope("layer2"):
layers.append(
conv2d(layers[-1],
self.start_depth * 4,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel,
sn_op=sn_op))
layers.append(lrelu(layers[-1]))
with tf.variable_scope("layer3"):
layers.append(
conv2d(layers[-1],
self.start_depth * 8,
d_h=self.stride,
d_w=self.stride,
k_h=self.kernel,
k_w=self.kernel,
sn_op=sn_op))
layers.append(lrelu(layers[-1]))
with tf.variable_scope("x"):
image_layers = [layers[-1]]
with tf.variable_scope("layer0"):
image_layers.append(
linear(image_layers[-1], 1, sn_op=sn_op))
image_layers.append(tf.nn.sigmoid(image_layers[-1]))
with tf.variable_scope("q"):
q_layers = [layers[-1]]
with tf.variable_scope("layer0"):
q_layers.append(
linear(q_layers[-1], self.output_length, sn_op=sn_op))
layers.extend(image_layers)
layers.extend(q_layers)
return image_layers[-1], q_layers[-1], layers