def generative_network(z):
 with pt.defaults_scope(activation_fn=tf.nn.elu, batch_normalize=True, scale_after_normalization=True):
  return pt.wrap(z).reshape([M, 1, 1, d]).deconv2d(3, 128, edges='VALID').deconv2d(5, 64, edges='VALID').deconv2d(5, 32, stride=2).deconv2d(5, 1, stride=2, activation_fn=None).flatten().tensor

def generative_network ( z ) : with pt . defaults_scope ( activation_fn = tf . nn . elu , batch_normalize = True , scale_after_normalization = True ) : return pt . wrap ( z ) . reshape ( [ M , 1 , 1 , d ] ) . deconv2d ( 3 , 128 , edges = 'VALID' ) . deconv2d ( 5 , 64 , edges = 'VALID' ) . deconv2d ( 5 , 32 , stride = 2 ) . deconv2d ( 5 , 1 , stride = 2 , activation_fn = None ) . flatten ( ) . tensor
generative network to parameterize generative model .