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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 | |
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`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 | |
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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 | |
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`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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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/backend/rnn