def inference(self, z, reuse=False, trainable=True):
with tf.variable_scope("generator") as scope:
if reuse:
scope.reuse_variables()
print("===G")
# linear projection.
z_, h0_w, self.h0_b = mp.linear_project('g_lin_project_h0',
z,
self.first_conv_dim * 8 *
4 * 4,
reuse=reuse,
with_w=True)
# reshape for cnn inputs.
h0 = tf.reshape(z_, [-1, 4, 4, self.first_conv_dim * 8])
# batch norm
h0 = tf.nn.relu(self.g_bn0(h0, trainable=trainable))
# h0 = tf.nn.relu(mp.batch_norm(h0, scope_name='g_bn_h0', reuse=reuse, trainable=trainable))
# deconv1 conv2d_transpose arguments = (scope_name, inputs, shape, output_shape, bias_shape, stride, padding='VALID', reuse=False, trainable=True)
deconv_h1, deconv_h1_w, deconv_h1_b = mp.conv2d_transpose(
'g_deconv_h1',
h0, [5, 5, self.first_conv_dim * 8,
h0.get_shape()[-1]],
[self.batch_size, 8, 8, self.first_conv_dim * 8],
[self.first_conv_dim * 8], [1, 2, 2, 1],
padding='SAME',
reuse=reuse,
with_w=True,
trainable=trainable)
h1 = tf.nn.relu(self.g_bn1(deconv_h1, trainable=trainable))
# h1 = tf.nn.relu(mp.batch_norm(deconv_h1, scope_name='g_bn_h1', reuse=reuse, trainable=trainable))
# deconv2 conv2d_transpose arguments = (scope_name, inputs, shape, output_shape, bias_shape, stride, padding='VALID', reuse=False, trainable=True)
deconv_h2, deconv_h2_w, deconv_h2_b = mp.conv2d_transpose(
'g_deconv_h2',
h1, [5, 5, self.first_conv_dim * 4,
h1.get_shape()[-1]],
[self.batch_size, 16, 16, self.first_conv_dim * 4],
[self.first_conv_dim * 4], [1, 2, 2, 1],
padding='SAME',
reuse=reuse,
with_w=True,
trainable=trainable)
print(deconv_h2.get_shape())
h2 = tf.nn.relu(self.g_bn2(deconv_h2, trainable=trainable))
# h2 = tf.nn.relu(mp.batch_norm(deconv_h2, scope_name='g_bn_h2', reuse=reuse, trainable=trainable))
# 3rd
deconv_h3, deconv_h3_w, deconv_h3_b = mp.conv2d_transpose(
'g_deconv_h3',
h2, [5, 5, 3, h2.get_shape()[-1]],
[self.batch_size, 32, 32, 3], [3], [1, 2, 2, 1],
padding='SAME',
reuse=reuse,
with_w=True,
trainable=trainable)
print(deconv_h3.get_shape())
return tf.nn.tanh(deconv_h3)
def inference(self, z, reuse=False):
# linear projection.
z_, h0_w, self.h0_b = mp.linear_project('g_lin_project_h0',
z,
self.first_conv_dim * 8 * 7 *
7,
reuse=reuse,
with_w=True)
# reshape for cnn inputs.
h0 = tf.reshape(z_, [-1, 7, 7, self.first_conv_dim * 8])
# batch norm
h0 = tf.nn.relu(mp.batch_norm(h0, scope_name='g_bn_h0', reuse=reuse))
# deconv1 conv2d_transpose arguments = (scope_name, inputs, shape, output_shape, bias_shape, stride, padding='VALID', reuse=False, trainable=True)
deconv_h1, deconv_h1_w, deconv_h1_b = mp.conv2d_transpose(
'g_deconv_h1',
h0, [5, 5, self.first_conv_dim * 4,
h0.get_shape()[-1]],
[self.batch_size, 14, 14, self.first_conv_dim * 4],
[self.first_conv_dim * 4], [1, 2, 2, 1],
padding='SAME',
reuse=reuse,
with_w=True)
h1 = tf.nn.relu(
mp.batch_norm(deconv_h1, scope_name='g_bn_h1', reuse=reuse))
# deconv2 conv2d_transpose arguments = (scope_name, inputs, shape, output_shape, bias_shape, stride, padding='VALID', reuse=False, trainable=True)
deconv_h2, deconv_h2_w, deconv_h2_b = mp.conv2d_transpose(
'g_deconv_h2',
h1, [5, 5, 1, h1.get_shape()[-1]], [self.batch_size, 28, 28, 1],
[1], [1, 2, 2, 1],
padding='SAME',
reuse=reuse,
with_w=True)
print(deconv_h2.get_shape())
return tf.nn.tanh(deconv_h2)