RNN
classkeras.layers.RNN(
cell,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
zero_output_for_mask=False,
**kwargs
)
Base class for recurrent layers.
Arguments
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.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).output_size
attribute, a single integer.get_initial_state(batch_size=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
shape (batch_size, cell.state_size)
.
The cell might choose to create a tensor full of zeros,
or other values based on the cell's implementation.
inputs
is the input tensor to the RNN layer, with shape
(batch_size, timesteps, features)
.
If this method is not implemented
by the cell, the RNN layer will create a zero filled tensor
with shape (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.False
).
If True
, process the input sequence backwards and return the
reversed sequence.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.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.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, e.g.,
merging bidirectional RNNs.Call arguments
[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.Input shape
3-D tensor with shape (batch_size, timesteps, features)
.
Output shape
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 state_size
could
be a high dimension tensor shape.return_sequences
: 3D tensor with shape
(batch_size, timesteps, output_size)
.Masking:
This layer supports masking for input data with a variable number
of timesteps. To introduce masks to your data,
use a 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:
stateful=True
in the layer constructor.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)
.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.
Examples
from keras.src.layers import RNN
from keras.src import ops
# First, let's define a RNN Cell, as a layer subclass.
class MinimalRNNCell(keras.layers.Layer):
def __init__(self, units, **kwargs):
super().__init__(**kwargs)
self.units = units
self.state_size = units
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 = ops.matmul(inputs, self.kernel)
output = h + ops.matmul(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)