|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v2.15.0/keras/backend.py#L4721-L5200) | Iterates over the time dimension of a tensor. tf.keras.backend.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, return_all_outputs=True )


| ## Args || |---|---| | `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` | An integer or a 1-D Tensor, depending on whether the time dimension is fixed-length or not. In case of variable length input, it is used for masking in case there's no mask specified. | | `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. | | `return_all_outputs` | Boolean. If True, return the recurrent outputs for all timesteps in the sequence. If False, only return the output for the last timestep (which consumes less memory). |



| ## Returns || |---|---| | A tuple, `(last_output, outputs, new_states)`. last_output: the latest output of the rnn, of shape `(samples, ...)` outputs:
- If `return_all_outputs=True`: a tensor with shape `(samples, time, ...)` where each entry `outputs[s, t]` is the output of the step function at time `t` for sample `s` - Else, a tensor equal to `last_output` with shape `(samples, 1, ...)` new_states: list of tensors, latest states returned by the step function, of shape `(samples, ...)`. ||



| ## Raises || |---|---| | `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). |