tf.keras.backend.rnn
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). |