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
)
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
|
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.
|
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).
|
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TensorFlow 1 version
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