tf.keras.backend.rnn( step_function, inputs, initial_states, go_backwards=False, mask=None, constants=None, unroll=False, input_length=None )
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 (
scandepending on backend).
input_length: Unused; exists for API compatibility.
(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
ValueError: if input dimension is less than 3.
Truebut input timestep is not a fixed number.
maskis provided (not
None) but states is not provided (
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.