def _single_cell()

in nmt/model_helper.py [0:0]

• 37 lines of code
• 10 McCabe index (conditional complexity)

def _single_cell(unit_type, num_units, forget_bias, dropout, mode,
                 residual_connection=False, device_str=None, residual_fn=None):
  """Create an instance of a single RNN cell."""
  # dropout (= 1 - keep_prob) is set to 0 during eval and infer
  dropout = dropout if mode == tf.contrib.learn.ModeKeys.TRAIN else 0.0

  # Cell Type
  if unit_type == "lstm":
    utils.print_out("  LSTM, forget_bias=%g" % forget_bias, new_line=False)
    single_cell = tf.contrib.rnn.BasicLSTMCell(
        num_units,
        forget_bias=forget_bias)
  elif unit_type == "gru":
    utils.print_out("  GRU", new_line=False)
    single_cell = tf.contrib.rnn.GRUCell(num_units)
  elif unit_type == "layer_norm_lstm":
    utils.print_out("  Layer Normalized LSTM, forget_bias=%g" % forget_bias,
                    new_line=False)
    single_cell = tf.contrib.rnn.LayerNormBasicLSTMCell(
        num_units,
        forget_bias=forget_bias,
        layer_norm=True)
  elif unit_type == "nas":
    utils.print_out("  NASCell", new_line=False)
    single_cell = tf.contrib.rnn.NASCell(num_units)
  else:
    raise ValueError("Unknown unit type %s!" % unit_type)

  # Dropout (= 1 - keep_prob)
  if dropout > 0.0:
    single_cell = tf.contrib.rnn.DropoutWrapper(
        cell=single_cell, input_keep_prob=(1.0 - dropout))
    utils.print_out("  %s, dropout=%g " %(type(single_cell).__name__, dropout),
                    new_line=False)

  # Residual
  if residual_connection:
    single_cell = tf.contrib.rnn.ResidualWrapper(
        single_cell, residual_fn=residual_fn)
    utils.print_out("  %s" % type(single_cell).__name__, new_line=False)

  # Device Wrapper
  if device_str:
    single_cell = tf.contrib.rnn.DeviceWrapper(single_cell, device_str)
    utils.print_out("  %s, device=%s" %
                    (type(single_cell).__name__, device_str), new_line=False)

  return single_cell