def Discriminator(inputs, is_reuse=True, name='disc'):
with tf.variable_scope(name, reuse=is_reuse):
print('is_reuse: {}'.format(is_reuse))
output01 = tf_utils.linear(inputs, DIM, name='fc-1')
output01 = tf_utils.relu(output01, name='relu-1')
output02 = tf_utils.linear(output01, DIM, name='fc-2')
output02 = tf_utils.relu(output02, name='relu-2')
output03 = tf_utils.linear(output02, DIM, name='fc-3')
output03 = tf_utils.relu(output03, name='relu-3')
output04 = tf_utils.linear(output03, DIS_DIM, name='fc-4')
return output04
def discriminator(self, data, name='d_', is_reuse=False):
with tf.variable_scope(name) as scope:
if is_reuse is True:
scope.reuse_variables()
# 64 -> 32 or 32 -> 16
h0_conv = tf_utils.conv2d(data, self.dis_c[0], name='h0_conv2d')
h0_lrelu = tf_utils.lrelu(h0_conv, name='h0_lrelu')
# 32 -> 16 or 16 -> 8
h1_conv = tf_utils.conv2d(h0_lrelu,
self.dis_c[1],
name='h1_conv2d')
h1_batchnorm = tf_utils.batch_norm(h1_conv,
name='h1_batchnorm',
_ops=self._dis_train_ops)
h1_lrelu = tf_utils.lrelu(h1_batchnorm, name='h1_lrelu')
# 16 -> 8 or 8 -> 4
h2_conv = tf_utils.conv2d(h1_lrelu,
self.dis_c[2],
name='h2_conv2d')
h2_batchnorm = tf_utils.batch_norm(h2_conv,
name='h2_batchnorm',
_ops=self._dis_train_ops)
h2_lrelu = tf_utils.lrelu(h2_batchnorm, name='h2_lrelu')
if self.flags.dataset == 'mnist':
h2_flatten = tf.compat.v1.layers.flatten(h2_lrelu)
h3_linear = tf_utils.linear(h2_flatten, 1, name='h3_linear')
return tf.nn.sigmoid(h3_linear), h3_linear
else:
# 8 -> 4
h3_conv = tf_utils.conv2d(h2_lrelu,
self.dis_c[3],
name='h3_conv2d')
h3_batchnorm = tf_utils.batch_norm(h3_conv,
name='h3_batchnorm',
_ops=self._dis_train_ops)
h3_lrelu = tf_utils.lrelu(h3_batchnorm, name='h3_lrelu')
h3_flatten = tf.compat.v1.layers.flatten(h3_lrelu)
h4_linear = tf_utils.linear(h3_flatten, 1, name='h4_linear')
return tf.nn.sigmoid(h4_linear), h4_linear
def generator(self, data, name='g_'):
with tf.compat.v1.variable_scope(name):
# data_flatten = flatten(data)
data_flatten = tf.compat.v1.layers.flatten(data)
# 4 x 4
h0_linear = tf_utils.linear(data_flatten,
4 * 4 * self.gen_c[0],
name='h0_linear')
h0_reshape = tf.reshape(
h0_linear, [tf.shape(h0_linear)[0], 4, 4, self.gen_c[0]])
h0_batchnorm = tf_utils.batch_norm(h0_reshape,
name='h0_batchnorm',
_ops=self._gen_train_ops)
h0_relu = tf.nn.relu(h0_batchnorm, name='h0_relu')
# 8 x 8
h1_deconv = tf_utils.deconv2d(h0_relu,
self.gen_c[1],
name='h1_deconv2d')
h1_batchnorm = tf_utils.batch_norm(h1_deconv,
name='h1_batchnorm',
_ops=self._gen_train_ops)
h1_relu = tf.nn.relu(h1_batchnorm, name='h1_relu')
# 16 x 16
h2_deconv = tf_utils.deconv2d(h1_relu,
self.gen_c[2],
name='h2_deconv2d')
h2_batchnorm = tf_utils.batch_norm(h2_deconv,
name='h2_batchnorm',
_ops=self._gen_train_ops)
h2_relu = tf.nn.relu(h2_batchnorm, name='h2_relu')
# 32 x 32
# if self.flags.dataset == 'mnist' or self.flags.dataset == "nasdaq":
if self.flags.dataset == 'mnist' or self.flags.dataset == "nasdaq":
output = tf_utils.deconv2d(h2_relu,
self.image_size[2],
name='h3_deconv2d')
return tf.nn.tanh(output)
else:
h3_deconv = tf_utils.deconv2d(h2_relu,
self.gen_c[3],
name='h3_deconv2d')
h3_batchnorm = tf_utils.batch_norm(h3_deconv,
name='h3_batchnorm',
_ops=self._gen_train_ops)
h3_relu = tf.nn.relu(h3_batchnorm, name='h3_relu')
# 64 x 64
output = tf_utils.deconv2d(h3_relu,
self.image_size[2],
name='h4_deconv2d')
return tf.nn.tanh(output)