Function dynamic_rnn

modelzoo/features/embedding_variable/dien/script/rnn.py:440–626

Creates a recurrent neural network specified by RNNCell `cell`. Performs fully dynamic unrolling of `inputs`. Example: ```python # create a BasicRNNCell rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) # 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size] # d

(cell, inputs, att_scores=None, sequence_length=None, initial_state=None,
                dtype=None, parallel_iterations=None, swap_memory=False,
                time_major=False, scope=None)

def dynamic_rnn(cell, inputs, att_scores=None, sequence_length=None, initial_state=None,
                dtype=None, parallel_iterations=None, swap_memory=False,
                time_major=False, scope=None):
  """Creates a recurrent neural network specified by RNNCell `cell`.

  Performs fully dynamic unrolling of `inputs`.

  Example:

  ```python
  # create a BasicRNNCell
  rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)

  # 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size]

  # defining initial state
  initial_state = rnn_cell.zero_state(batch_size, dtype=tf.float32)

  # 'state' is a tensor of shape [batch_size, cell_state_size]
  outputs, state = tf.nn.dynamic_rnn(rnn_cell, input_data,
                                     initial_state=initial_state,
                                     dtype=tf.float32)
  ```

  ```python
  # create 2 LSTMCells
  rnn_layers = [tf.nn.rnn_cell.LSTMCell(size) for size in [128, 256]]

  # create a RNN cell composed sequentially of a number of RNNCells
  multi_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(rnn_layers)

  # 'outputs' is a tensor of shape [batch_size, max_time, 256]
  # 'state' is a N-tuple where N is the number of LSTMCells containing a
  # tf.contrib.rnn.LSTMStateTuple for each cell
  outputs, state = tf.nn.dynamic_rnn(cell=multi_rnn_cell,
                                     inputs=data,
                                     dtype=tf.float32)
  ```


  Args:
    cell: An instance of RNNCell.
    inputs: The RNN inputs.
      If `time_major == False` (default), this must be a `Tensor` of shape:
        `[batch_size, max_time, ...]`, or a nested tuple of such
        elements.
      If `time_major == True`, this must be a `Tensor` of shape:
        `[max_time, batch_size, ...]`, or a nested tuple of such
        elements.
      This may also be a (possibly nested) tuple of Tensors satisfying
      this property.  The first two dimensions must match across all the inputs,
      but otherwise the ranks and other shape components may differ.
      In this case, input to `cell` at each time-step will replicate the
      structure of these tuples, except for the time dimension (from which the
      time is taken).
      The input to `cell` at each time step will be a `Tensor` or (possibly
      nested) tuple of Tensors each with dimensions `[batch_size, ...]`.
    sequence_length: (optional) An int32/int64 vector sized `[batch_size]`.