rnn(cell, inputs, initial_states=None, sequence_length=None, time_major=False, is_reverse=False, **kwargs)
rnn creates a recurrent neural network specified by RNNCell cell, which performs
cell.call()repeatedly until reachs to the maximum length of inputs.
cell (RNNCell) – An instance of RNNCell.
inputs (Variable) – A (possibly nested structure of) tensor variable[s]. The shape of tensor should be [batch_size, sequence_length, …] for time_major == False or [sequence_length, batch_size, …] for time_major == True. It represents the inputs to be unrolled in RNN.
initial_states (Variable, optional) – A (possibly nested structure of) tensor variable[s], representing the initial state for RNN. If not provided, cell.get_initial_states would be used to produce the initial state. Default None.
sequence_length (Variable, optional) – A tensor with shape [batch_size]. It stores real length of each instance, thus enables users to extract the last valid state when past a batch element’s sequence length for correctness. If not provided, the padddings would be treated same as non-padding inputs. Default None.
time_major (bool, optional) – Indicate the data layout of Tensor included in input and output tensors. If False, the data layout would be batch major with shape [batch_size, sequence_length, …]. If True, the data layout would be time major with shape [sequence_length, batch_size, …]. Default: False.
is_reverse (bool, optional) – Indicate whether to calculate in the reverse order of input sequences. Default: False.
**kwargs – Additional keyword arguments. Arguments passed to cell.call.
(final_outputs, final_states)) including the final outputs and states, both are Tensor or nested structure of Tensor. final_outputs has the same structure and data types as the returned outputs of
cell.call, and each Tenser in final_outputs stacks all time steps’ counterpart in outputs thus has shape [batch_size, sequence_length, …] for time_major == False or [sequence_length, batch_size, …] for time_major == True. final_states is the counterpart at last time step of initial states, thus has the same structure with it and has tensors with same shapes and data types.
- Return type
import paddle.fluid as fluid inputs = fluid.data(name="inputs", shape=[-1, 32, 128], dtype="float32") cell = fluid.layers.GRUCell(hidden_size=128) outputs = fluid.layers.rnn(cell=cell, inputs=inputs)