Function rnn

tensorflow/python/keras/backend.py:3718–4077

Iterates over the time dimension of a tensor. Arguments: step_function: RNN step function. Args; input; Tensor with shape `(samples, ...)` (no time dimension), representing input for the batch of samples at a certain time step.

(step_function,
        inputs,
        initial_states,
        go_backwards=False,
        mask=None,
        constants=None,
        unroll=False,
        input_length=None,
        time_major=False,
        zero_output_for_mask=False)

@keras_export('keras.backend.rnn')
def rnn(step_function,
        inputs,
        initial_states,
        go_backwards=False,
        mask=None,
        constants=None,
        unroll=False,
        input_length=None,
        time_major=False,
        zero_output_for_mask=False):
  """Iterates over the time dimension of a tensor.

  Arguments:
      step_function: RNN step function.
          Args;
              input; Tensor with shape `(samples, ...)` (no time dimension),
                  representing input for the batch of samples at a certain
                  time step.
              states; List of tensors.
          Returns;
              output; Tensor with shape `(samples, output_dim)`
                  (no time dimension).
              new_states; List of tensors, same length and shapes
                  as 'states'. The first state in the list must be the
                  output tensor at the previous timestep.
      inputs: Tensor of temporal data of shape `(samples, time, ...)`
          (at least 3D), or nested tensors, and each of which has shape
          `(samples, time, ...)`.
      initial_states: Tensor with shape `(samples, state_size)`
          (no time dimension), containing the initial values for the states used
          in the step function. In the case that state_size is in a nested
          shape, the shape of initial_states will also follow the nested
          structure.
      go_backwards: Boolean. If True, do the iteration over the time
          dimension in reverse order and return the reversed sequence.
      mask: Binary tensor with shape `(samples, time, 1)`,
          with a zero for every element that is masked.
      constants: List of constant values passed at each step.
      unroll: Whether to unroll the RNN or to use a symbolic `while_loop`.
      input_length: If specified, assume time dimension is of this length.
      time_major: Boolean. If true, the inputs and outputs will be in shape
          `(timesteps, batch, ...)`, whereas in the False case, it will be
          `(batch, timesteps, ...)`. Using `time_major = True` is a bit more
          efficient because it avoids transposes at the beginning and end of the
          RNN calculation. However, most TensorFlow data is batch-major, so by
          default this function accepts input and emits output in batch-major
          form.
      zero_output_for_mask: Boolean. If True, the output for masked timestep
          will be zeros, whereas in the False case, output from previous
          timestep is returned.
  Returns:
      A tuple, `(last_output, outputs, new_states)`.
          last_output: the latest output of the rnn, of shape `(samples, ...)`
          outputs: tensor with shape `(samples, time, ...)` where each
              entry `outputs[s, t]` is the output of the step function
              at time `t` for sample `s`.
          new_states: list of tensors, latest states returned by
              the step function, of shape `(samples, ...)`.