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Base class for recurrent layers.

Inherits From: Layer, Module

Used in the notebooks

Used in the guide Used in the tutorials

See the Keras RNN API guide for details about the usage of RNN API.

cell A RNN cell instance or a list of RNN cell instances. A RNN cell is a class that has:

  • A call(input_at_t, states_at_t) method, returning (output_at_t, states_at_t_plus_1). The call method of the cell can also take the optional argument constants, see section "Note on passing external constants" below.
  • A state_size attribute. This can be a single integer (single state) in which case it is the size of the recurrent state. This can also be a list/tuple of integers (one size per state). The state_size can also be TensorShape or tuple/list of TensorShape, to represent high dimension state.
  • A output_size attribute. This can be a single integer or a TensorShape, which represent the shape of the output. For backward compatible reason, if this attribute is not available for the cell, the value will be inferred by the first element of the state_size.
  • A get_initial_state(inputs=None, batch_size=None, dtype=None) method that creates a tensor meant to be fed to call() as the initial state, if the user didn't specify any initial state via other means. The returned initial state should have a shape of [batch_size, cell.state_size]. The cell might choose to create a tensor full of zeros, or full of other values based on the cell's implementation. inputs is the input tensor to the RNN layer, which should contain the batch size as its shape[0], and also dtype. Note that the shape[0] might be None during the graph construction. Either the inputs or the pair of batch_size and dtype are provided. batch_size is a scalar tensor that represents the batch size of the inputs. dtype is tf.DType that represents the dtype of the inputs. For backward compatibility, if this method is not implemented by the cell, the RNN layer will create a zero filled tensor with the size of [batch_size, cell.state_size]. In the case that cell is a list of RNN cell instances, the cells will be stacked on top of each other in the RNN, resulting in an efficient stacked RNN.
return_sequences Boolean (default False). Whether to return the last output in the output sequence, or the full sequence.
return_state Boolean (default False). Whether to return the last state in addition to the output.
go_backwards Boolean (default False). If True, process the input sequence backwards and return the reversed sequence.
stateful Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
unroll Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.
time_major The shape format of the inputs and outputs tensors. 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 (default False). Whether the output should use zeros for the masked timesteps. Note that this field is only used when return_sequences is True and mask is provided. It can useful if you want to reuse the raw output sequence of the RNN without interference from the masked timesteps, eg, merging bidirectional RNNs.

Call arguments:

  • inputs: Input tensor.
  • mask: Binary tensor of shape [batch_size, timesteps] indicating whether a given timestep should be masked. An individual True entry indicates that the corresponding timestep should be utilized, while a False entry indicates that the corresponding timestep should be ignored.
  • training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is for use with cells that use dropout.
  • initial_state: List of initial state tensors to be passed to the first call of the cell.