Defined in tensorflow/python/keras/_impl/keras/

Iterates over the time dimension of a tensor.


  • step_function: RNN step function. Parameters; 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).
  • initial_states: tensor with shape (samples, output_dim) (no time dimension), containing the initial values for the states used in the step function.
  • 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: a list of constant values passed at each step.
  • unroll: whether to unroll the RNN or to use a symbolic loop (while_loop or scan depending on backend).
  • input_length: Unused; exists for API compatibility.


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, ...).


  • ValueError: if input dimension is less than 3.
  • ValueError: if unroll is True but input timestep is not a fixed number.
  • ValueError: if mask is provided (not None) but states is not provided (len(states) == 0).