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tf.keras.backend.rnn

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
)

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

Iterates over the time dimension of a tensor.

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: If specified, assume time dimension is of this length.
  • 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).