Base class for recurrent layers.
cell, return_sequences=False, return_state=False, go_backwards=False,
stateful=False, unroll=False, time_major=False, **kwargs
Used in the notebooks
See the Keras RNN API guide
for details about the usage of RNN API.
A RNN cell instance or a list of RNN cell instances.
A RNN cell is a class that has:
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
section "Note on passing external constants" below.
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).
state_size can also be TensorShape or tuple/list of
TensorShape, to represent high dimension state.
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
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
inputs is the input tensor to the RNN layer, which should
contain the batch size as its shape, and also dtype. Note that
the shape might be
None during the graph construction. Either
inputs or the pair of
dtype are provided.
batch_size is a scalar tensor that represents the batch size
of the inputs.
tf.DType that represents the dtype of
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.
False). Whether to return the last
output in the output sequence, or the full sequence.
False). Whether to return the last state
in addition to the output.
If True, process the input sequence backwards and return the
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.
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.
The shape format of the
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
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
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
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.
constants: List of constant tensors to be passed to the cell at each
N-D tensor with shape
[batch_size, timesteps, ...] or
[timesteps, batch_size, ...] when time_major is True.
return_state: a list of tensors. The first tensor is
the output. The remaining tensors are the last states,
each with shape
[batch_size, state_size], where
be a high dimension tensor shape.
return_sequences: N-D tensor with shape
[batch_size, timesteps, output_size], where
be a high dimension tensor shape, or
[timesteps, batch_size, output_size] when
time_major is True.
- Else, N-D tensor with shape
[batch_size, output_size], where
output_size could be a high dimension tensor shape.
This layer supports masking for input data with a variable number
of timesteps. To introduce masks to your data,
use an [tf.keras.layers.Embedding] layer with the
Note on using statefulness in RNNs:
You can set RNN layers to be 'stateful', which means that the states
computed for the samples in one batch will be reused as initial states
for the samples in the next batch. This assumes a one-to-one mapping
between samples in different successive batches.
To enable statefulness: