(rnn_layer_sizes, state_is_tuple=False)
return cell
Makes a default LSTM cell for use in the NoteRNNLoader graph.

  This model is only to be used for loading the checkpoint from the research
  paper. In general, events_rnn_graph.make_rnn_cell should be used instead.

  Args:
    rnn_layer_sizes: A list of integer sizes (in units) for each layer of the
        RNN.
    state_is_tuple: A boolean specifying whether to use tuple of hidden matrix
        and cell matrix as a state instead of a concatenated matrix.

  Returns:
      A tf.rnn.rnn_cell.MultiRNNCell based on the given hyperparameters.
Makes a default LSTM cell for use in the NoteRNNLoader graph.
Makes a default LSTM cell for use in the NoteRNNLoader graph .
def make_rnn_cell(rnn_layer_sizes, state_is_tuple=False):
  """Makes a default LSTM cell for use in the NoteRNNLoader graph.

  This model is only to be used for loading the checkpoint from the research
  paper. In general, events_rnn_graph.make_rnn_cell should be used instead.

  Args:
    rnn_layer_sizes: A list of integer sizes (in units) for each layer of the
        RNN.
    state_is_tuple: A boolean specifying whether to use tuple of hidden matrix
        and cell matrix as a state instead of a concatenated matrix.

  Returns:
      A tf.rnn.rnn_cell.MultiRNNCell based on the given hyperparameters.
  """
  cells = []
  for num_units in rnn_layer_sizes:
    cell = tf.nn.rnn_cell.LSTMCell(num_units, state_is_tuple=state_is_tuple)
    cells.append(cell)

  cell = tf.nn.rnn_cell.MultiRNNCell(cells, state_is_tuple=state_is_tuple)

  return cell
def make_rnn_cell ( rnn_layer_sizes , state_is_tuple = False ) : cells = [ ] for num_units in rnn_layer_sizes : cell = tf . nn . rnn_cell . LSTMCell ( num_units , state_is_tuple = state_is_tuple ) cells . append ( cell ) cell = tf . nn . rnn_cell . MultiRNNCell ( cells , state_is_tuple = state_is_tuple ) return cell