seq2seq.py 文件源码

def latent_to_decoder(latent_vector,
                      embedding_size,
                      latent_dim,
                      num_layers,
                      activation=tf.nn.relu,
                      use_lstm=False,
                      dtype=None):

  concat_state_size = num_layers * embedding_size
  if use_lstm:
    concat_state_size *= 2
  with tf.variable_scope('latent_to_decoder'):
    w = tf.get_variable("w",[latent_dim, concat_state_size],
      dtype=dtype)
    b = tf.get_variable("b", [concat_state_size], dtype=dtype)
    decoder_initial_state = prelu(tf.matmul(latent_vector, w) + b)
  if num_layers > 1:
    decoder_initial_state = tuple(tf.split(1, num_layers, decoder_initial_state))
    if use_lstm:
      decoder_initial_state = [tuple(tf.split(1, 2, single_layer_state)) for single_layer_state in decoder_initial_state]
  elif use_lstm:
    decoder_initial_state = tuple(tf.split(1, 2, decoder_initial_state))

  return decoder_initial_state