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Base class for recurrent layers.
tf.keras.layers.RNN(
cell,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
time_major=False,
**kwargs
)
See the Keras RNN API guide for details about the usage of RNN API.
Args | |
---|---|
cell
|
A RNN cell instance or a list of RNN cell instances.
A RNN cell is a class that has:
|
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.
|
Input shape | |
---|---|
N-D tensor with shape [batch_size, timesteps, ...] or
[timesteps, batch_size, ...] when time_major is True.
|
Output shape | |
---|---|
|
Masking | |
---|---|
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 mask_zero parameter
set to True .
|
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:
- Specify `stateful=True` in the layer constructor.
- Specify a fixed batch size for your model, by passing
If sequential model:
`batch_input_shape=(...)` to the first layer in your model.
Else for functional model with 1 or more Input layers:
`batch_shape=(...)` to all the first layers in your model.
This is the expected shape of your inputs
*including the batch size*.
It should be a tuple of integers, e.g. `(32, 10, 100)`.
- Specify `shuffle=False` when calling `fit()`.
To reset the states of your model, call .reset_states()
on either
a specific layer, or on your entire model.
Note on specifying the initial state of RNNs:
You can specify the initial state of RNN layers symbolically by
calling them with the keyword argument initial_state
. The value of
initial_state
should be a tensor or list of tensors representing
the initial state of the RNN layer.
You can specify the initial state of RNN layers numerically by
calling reset_states
with the keyword argument states
. The value of
states
should be a numpy array or list of numpy arrays representing
the initial state of the RNN layer.
Note on passing external constants to RNNs:
You can pass "external" constants to the cell using the constants
keyword argument of RNN.__call__
(as well as RNN.call
) method. This
requires that the cell.call
method accepts the same keyword argument
constants
. Such constants can be used to condition the cell
transformation on additional static inputs (not changing over time),
a.k.a. an attention mechanism.
Examples:
from keras.src.layers import RNN
from keras.src import backend
# First, let's define a RNN Cell, as a layer subclass.
class MinimalRNNCell(keras.layers.Layer):
def __init__(self, units, **kwargs):
self.units = units
self.state_size = units
super(MinimalRNNCell, self).__init__(**kwargs)
def build(self, input_shape):
self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
initializer='uniform',
name='kernel')
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units),
initializer='uniform',
name='recurrent_kernel')
self.built = True
def call(self, inputs, states):
prev_output = states[0]
h = backend.dot(inputs, self.kernel)
output = h + backend.dot(prev_output, self.recurrent_kernel)
return output, [output]
# Let's use this cell in a RNN layer:
cell = MinimalRNNCell(32)
x = keras.Input((None, 5))
layer = RNN(cell)
y = layer(x)
# Here's how to use the cell to build a stacked RNN:
cells = [MinimalRNNCell(32), MinimalRNNCell(64)]
x = keras.Input((None, 5))
layer = RNN(cells)
y = layer(x)
Attributes | |
---|---|
states
|
Methods
reset_states
reset_states(
states=None
)
Reset the recorded states for the stateful RNN layer.
Can only be used when RNN layer is constructed with stateful
= True
.
Args:
states: Numpy arrays that contains the value for the initial state,
which will be feed to cell at the first time step. When the value is
None, zero filled numpy array will be created based on the cell
state size.
Raises | |
---|---|
AttributeError
|
When the RNN layer is not stateful. |
ValueError
|
When the batch size of the RNN layer is unknown. |
ValueError
|
When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise. |