contrib.keras.backend.rnn

tf.contrib.keras.backend.rnn

tf.contrib.keras.backend.rnn

rnn(
    step_function,
    inputs,
    initial_states,
    go_backwards=False,
    mask=None,
    constants=None,
    unroll=False
)

Defined in tensorflow/contrib/keras/python/keras/backend.py.

Iterates over the time dimension of a tensor.

Arguments:

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).

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, ...)`.

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).