def conv_decoder_32_large(inputs,
output_features=False,
nonlinearity=None,
bn=True,
kernel_initializer=None,
kernel_regularizer=None,
is_training=False,
counters={}):
name = get_name("conv_decoder_32_large", counters)
print("construct", name, "...")
with tf.variable_scope(name):
with arg_scope([deconv2d, dense],
nonlinearity=nonlinearity,
bn=bn,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
is_training=is_training,
counters=counters):
outputs = dense(inputs, 512)
outputs = tf.reshape(outputs, [-1, 1, 1, 512])
outputs = deconv2d(outputs, 256, 4, 1, "VALID")
outputs = deconv2d(outputs, 128, 4, 2, "SAME")
outputs = deconv2d(outputs, 64, 4, 2, "SAME")
outputs = deconv2d(outputs, 32, 4, 2, "SAME")
if output_features:
return outputs
outputs = deconv2d(outputs,
3,
1,
1,
"SAME",
nonlinearity=tf.sigmoid,
bn=False)
outputs = 2. * outputs - 1.
return outputs
def conv_decoder_28_binary(inputs,
nonlinearity=None,
bn=True,
kernel_initializer=None,
kernel_regularizer=None,
is_training=False,
counters={}):
name = get_name("conv_decoder_28_binary", counters)
print("construct", name, "...")
with tf.variable_scope(name):
with arg_scope([deconv2d, dense],
nonlinearity=nonlinearity,
bn=bn,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
is_training=is_training,
counters=counters):
outputs = dense(inputs, 128)
outputs = tf.reshape(outputs, [-1, 1, 1, 128])
outputs = deconv2d(outputs, 128, 4, 1, "VALID")
outputs = deconv2d(outputs, 64, 4, 1, "VALID")
outputs = deconv2d(outputs, 64, 4, 2, "SAME")
outputs = deconv2d(outputs, 32, 4, 2, "SAME")
outputs = deconv2d(outputs,
1,
1,
1,
"SAME",
nonlinearity=None,
bn=False)
return outputs
