RNN
keras.layers.RNN(cell, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)
Base class for recurrent layers.
Arguments

cell: A RNN cell instance. 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 argumentconstants
, 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 (which should be the same as the size of the cell output). This can also be a list/tuple of integers (one size per 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 thestate_size
.
It is also possible for
cell
to be a list of RNN cell instances, in which cases the cells get stacked on after the other in the RNN, implementing an efficient stacked RNN.  a

return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence.
 return_state: Boolean. 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 speedup a RNN, although it tends to be more memoryintensive. Unrolling is only suitable for short sequences.
 input_dim: dimensionality of the input (integer).
This argument (or alternatively,
the keyword argument
input_shape
) is required when using this layer as the first layer in a model.  input_length: Length of input sequences, to be specified
when it is constant.
This argument is required if you are going to connect
Flatten
thenDense
layers upstream (without it, the shape of the dense outputs cannot be computed). Note that if the recurrent layer is not the first layer in your model, you would need to specify the input length at the level of the first layer (e.g. via theinput_shape
argument)
Input shape
3D tensor with shape (batch_size, timesteps, input_dim)
.
Output shape
 if
return_state
: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each with shape(batch_size, units)
.  if
return_sequences
: 3D tensor with shape(batch_size, timesteps, units)
.  else, 2D tensor with shape
(batch_size, units)
.
Masking
This layer supports masking for input data with a variable number
of timesteps. To introduce masks to your data,
use an 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 onetoone 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
# 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 = K.dot(inputs, self.kernel)
output = h + K.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)
SimpleRNN
keras.layers.SimpleRNN(units, activation='tanh', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)
Fullyconnected RNN where the output is to be fed back to input.
Arguments
units: Positive integer, dimensionality of the output space.
activation: Activation function to use
(see activations).
Default: hyperbolic tangent (tanh
).
If you pass None
, no activation is applied
(ie. "linear" activation: a(x) = x
).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the kernel
weights matrix,
used for the linear transformation of the inputs
(see initializers).
recurrent_initializer: Initializer for the recurrent_kernel
weights matrix,
used for the linear transformation of the recurrent state
(see initializers).
bias_initializer: Initializer for the bias vector
(see initializers).
kernel_regularizer: Regularizer function applied to
the kernel
weights matrix
(see regularizer).
recurrent_regularizer: Regularizer function applied to
the recurrent_kernel
weights matrix
(see regularizer).
bias_regularizer: Regularizer function applied to the bias vector
(see regularizer).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see regularizer).
kernel_constraint: Constraint function applied to
the kernel
weights matrix
(see constraints).
recurrent_constraint: Constraint function applied to
the recurrent_kernel
weights matrix
(see constraints).
bias_constraint: Constraint function applied to the bias vector
(see constraints).
dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the inputs.
recurrent_dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the recurrent state.
return_sequences: Boolean. Whether to return the last output
in the output sequence, or the full sequence.
return_state: Boolean. 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 speedup a RNN,
although it tends to be more memoryintensive.
Unrolling is only suitable for short sequences.
GRU
keras.layers.GRU(units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, reset_after=False)
Gated Recurrent Unit  Cho et al. 2014.
There are two variants. The default one is based on 1406.1078v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original 1406.1078v1 and has the order reversed.
The second variant is compatible with CuDNNGRU (GPUonly) and allows
inference on CPU. Thus it has separate biases for kernel
and
recurrent_kernel
. Use 'reset_after'=True
and
recurrent_activation='sigmoid'
.
Arguments
 units: Positive integer, dimensionality of the output space.
 activation: Activation function to use
(see activations).
Default: hyperbolic tangent (
tanh
). If you passNone
, no activation is applied (ie. "linear" activation:a(x) = x
).  recurrent_activation: Activation function to use
for the recurrent step
(see activations).
Default: hard sigmoid (
hard_sigmoid
). If you passNone
, no activation is applied (ie. "linear" activation:a(x) = x
).  use_bias: Boolean, whether the layer uses a bias vector.
 kernel_initializer: Initializer for the
kernel
weights matrix, used for the linear transformation of the inputs (see initializers).  recurrent_initializer: Initializer for the
recurrent_kernel
weights matrix, used for the linear transformation of the recurrent state (see initializers).  bias_initializer: Initializer for the bias vector (see initializers).
 kernel_regularizer: Regularizer function applied to
the
kernel
weights matrix (see regularizer).  recurrent_regularizer: Regularizer function applied to
the
recurrent_kernel
weights matrix (see regularizer).  bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
 activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
 kernel_constraint: Constraint function applied to
the
kernel
weights matrix (see constraints).  recurrent_constraint: Constraint function applied to
the
recurrent_kernel
weights matrix (see constraints).  bias_constraint: Constraint function applied to the bias vector (see constraints).
 dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
 recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
 implementation: Implementation mode, either 1 or 2. Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications.
 return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence.
 return_state: Boolean. 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 speedup a RNN, although it tends to be more memoryintensive. Unrolling is only suitable for short sequences.
 reset_after: GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before" (default), True = "after" (CuDNN compatible).
References
 Learning Phrase Representations using RNN EncoderDecoder for Statistical Machine Translation
 On the Properties of Neural Machine Translation: EncoderDecoder Approaches
 Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
 A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
LSTM
keras.layers.LSTM(units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)
Long ShortTerm Memory layer  Hochreiter 1997.
Arguments
 units: Positive integer, dimensionality of the output space.
 activation: Activation function to use
(see activations).
Default: hyperbolic tangent (
tanh
). If you passNone
, no activation is applied (ie. "linear" activation:a(x) = x
).  recurrent_activation: Activation function to use
for the recurrent step
(see activations).
Default: hard sigmoid (
hard_sigmoid
). If you passNone
, no activation is applied (ie. "linear" activation:a(x) = x
).  use_bias: Boolean, whether the layer uses a bias vector.
 kernel_initializer: Initializer for the
kernel
weights matrix, used for the linear transformation of the inputs. (see initializers).  recurrent_initializer: Initializer for the
recurrent_kernel
weights matrix, used for the linear transformation of the recurrent state. (see initializers).  bias_initializer: Initializer for the bias vector (see initializers).
 unit_forget_bias: Boolean.
If True, add 1 to the bias of the forget gate at initialization.
Setting it to true will also force
bias_initializer="zeros"
. This is recommended in Jozefowicz et al. (2015).  kernel_regularizer: Regularizer function applied to
the
kernel
weights matrix (see regularizer).  recurrent_regularizer: Regularizer function applied to
the
recurrent_kernel
weights matrix (see regularizer).  bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
 activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
 kernel_constraint: Constraint function applied to
the
kernel
weights matrix (see constraints).  recurrent_constraint: Constraint function applied to
the
recurrent_kernel
weights matrix (see constraints).  bias_constraint: Constraint function applied to the bias vector (see constraints).
 dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
 recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
 implementation: Implementation mode, either 1 or 2. Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications.
 return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence.
 return_state: Boolean. 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 speedup a RNN, although it tends to be more memoryintensive. Unrolling is only suitable for short sequences.
References
 Long shortterm memory
 Learning to forget: Continual prediction with LSTM
 Supervised sequence labeling with recurrent neural networks
 A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
ConvLSTM2D
keras.layers.ConvLSTM2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, return_sequences=False, go_backwards=False, stateful=False, dropout=0.0, recurrent_dropout=0.0)
Convolutional LSTM.
It is similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.
Arguments
 filters: Integer, the dimensionality of the output space (i.e. the number output of filters in the convolution).
 kernel_size: An integer or tuple/list of n integers, specifying the dimensions of the convolution window.
 strides: An integer or tuple/list of n integers,
specifying the strides of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any
dilation_rate
value != 1.  padding: One of
"valid"
or"same"
(caseinsensitive).  data_format: A string,
one of
"channels_last"
(default) or"channels_first"
. The ordering of the dimensions in the inputs."channels_last"
corresponds to inputs with shape(batch, time, ..., channels)
while"channels_first"
corresponds to inputs with shape(batch, time, channels, ...)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be"channels_last"
.  dilation_rate: An integer or tuple/list of n integers, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any
dilation_rate
value != 1 is incompatible with specifying anystrides
value != 1.  activation: Activation function to use
(see activations).
If you don't specify anything, no activation is applied
(ie. "linear" activation:
a(x) = x
).  recurrent_activation: Activation function to use for the recurrent step (see activations).
 use_bias: Boolean, whether the layer uses a bias vector.
 kernel_initializer: Initializer for the
kernel
weights matrix, used for the linear transformation of the inputs. (see initializers).  recurrent_initializer: Initializer for the
recurrent_kernel
weights matrix, used for the linear transformation of the recurrent state. (see initializers).  bias_initializer: Initializer for the bias vector (see initializers).
 unit_forget_bias: Boolean.
If True, add 1 to the bias of the forget gate at initialization.
Use in combination with
bias_initializer="zeros"
. This is recommended in Jozefowicz et al. (2015).  kernel_regularizer: Regularizer function applied to
the
kernel
weights matrix (see regularizer).  recurrent_regularizer: Regularizer function applied to
the
recurrent_kernel
weights matrix (see regularizer).  bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
 activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
 kernel_constraint: Constraint function applied to
the
kernel
weights matrix (see constraints).  recurrent_constraint: Constraint function applied to
the
recurrent_kernel
weights matrix (see constraints).  bias_constraint: Constraint function applied to the bias vector (see constraints).
 return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence.
 go_backwards: Boolean (default False). If True, process the input sequence backwards.
 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.
 dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
 recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
Input shape
 if data_format='channels_first'
5D tensor with shape:
(samples, time, channels, rows, cols)
 if data_format='channels_last'
5D tensor with shape:
(samples, time, rows, cols, channels)
Output shape
 if
return_sequences
 if data_format='channels_first'
5D tensor with shape:
(samples, time, filters, output_row, output_col)
 if data_format='channels_last'
5D tensor with shape:
(samples, time, output_row, output_col, filters)
 if data_format='channels_first'
5D tensor with shape:

else
 if data_format='channels_first'
4D tensor with shape:
(samples, filters, output_row, output_col)
 if data_format='channels_last'
4D tensor with shape:
(samples, output_row, output_col, filters)
where o_row and o_col depend on the shape of the filter and the padding
 if data_format='channels_first'
4D tensor with shape:
Raises
 ValueError: in case of invalid constructor arguments.
References
 Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting The current implementation does not include the feedback loop on the cells output
SimpleRNNCell
keras.layers.SimpleRNNCell(units, activation='tanh', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0)
Cell class for SimpleRNN.
Arguments
units: Positive integer, dimensionality of the output space.
activation: Activation function to use
(see activations).
Default: hyperbolic tangent (tanh
).
If you pass None
, no activation is applied
(ie. "linear" activation: a(x) = x
).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the kernel
weights matrix,
used for the linear transformation of the inputs
(see initializers).
recurrent_initializer: Initializer for the recurrent_kernel
weights matrix,
used for the linear transformation of the recurrent state
(see initializers).
bias_initializer: Initializer for the bias vector
(see initializers).
kernel_regularizer: Regularizer function applied to
the kernel
weights matrix
(see regularizer).
recurrent_regularizer: Regularizer function applied to
the recurrent_kernel
weights matrix
(see regularizer).
bias_regularizer: Regularizer function applied to the bias vector
(see regularizer).
kernel_constraint: Constraint function applied to
the kernel
weights matrix
(see constraints).
recurrent_constraint: Constraint function applied to
the recurrent_kernel
weights matrix
(see constraints).
bias_constraint: Constraint function applied to the bias vector
(see constraints).
dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the inputs.
recurrent_dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the recurrent state.
GRUCell
keras.layers.GRUCell(units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=1, reset_after=False)
Cell class for the GRU layer.
Arguments
units: Positive integer, dimensionality of the output space.
activation: Activation function to use
(see activations).
Default: hyperbolic tangent (tanh
).
If you pass None
, no activation is applied
(ie. "linear" activation: a(x) = x
).
recurrent_activation: Activation function to use
for the recurrent step
(see activations).
Default: hard sigmoid (hard_sigmoid
).
If you pass None
, no activation is applied
(ie. "linear" activation: a(x) = x
).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the kernel
weights matrix,
used for the linear transformation of the inputs
(see initializers).
recurrent_initializer: Initializer for the recurrent_kernel
weights matrix,
used for the linear transformation of the recurrent state
(see initializers).
bias_initializer: Initializer for the bias vector
(see initializers).
kernel_regularizer: Regularizer function applied to
the kernel
weights matrix
(see regularizer).
recurrent_regularizer: Regularizer function applied to
the recurrent_kernel
weights matrix
(see regularizer).
bias_regularizer: Regularizer function applied to the bias vector
(see regularizer).
kernel_constraint: Constraint function applied to
the kernel
weights matrix
(see constraints).
recurrent_constraint: Constraint function applied to
the recurrent_kernel
weights matrix
(see constraints).
bias_constraint: Constraint function applied to the bias vector
(see constraints).
dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the inputs.
recurrent_dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the recurrent state.
implementation: Implementation mode, either 1 or 2.
Mode 1 will structure its operations as a larger number of
smaller dot products and additions, whereas mode 2 will
batch them into fewer, larger operations. These modes will
have different performance profiles on different hardware and
for different applications.
reset_after: GRU convention (whether to apply reset gate after or
before matrix multiplication). False = "before" (default),
True = "after" (CuDNN compatible).
LSTMCell
keras.layers.LSTMCell(units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=1)
Cell class for the LSTM layer.
Arguments
units: Positive integer, dimensionality of the output space.
activation: Activation function to use
(see activations).
Default: hyperbolic tangent (tanh
).
If you pass None
, no activation is applied
(ie. "linear" activation: a(x) = x
).
recurrent_activation: Activation function to use
for the recurrent step
(see activations).
Default: hard sigmoid (hard_sigmoid
).
If you pass None
, no activation is applied
(ie. "linear" activation: a(x) = x
).x
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the kernel
weights matrix,
used for the linear transformation of the inputs
(see initializers).
recurrent_initializer: Initializer for the recurrent_kernel
weights matrix,
used for the linear transformation of the recurrent state
(see initializers).
bias_initializer: Initializer for the bias vector
(see initializers).
unit_forget_bias: Boolean.
If True, add 1 to the bias of the forget gate at initialization.
Setting it to true will also force bias_initializer="zeros"
.
This is recommended in Jozefowicz et al. (2015).
kernel_regularizer: Regularizer function applied to
the kernel
weights matrix
(see regularizer).
recurrent_regularizer: Regularizer function applied to
the recurrent_kernel
weights matrix
(see regularizer).
bias_regularizer: Regularizer function applied to the bias vector
(see regularizer).
kernel_constraint: Constraint function applied to
the kernel
weights matrix
(see constraints).
recurrent_constraint: Constraint function applied to
the recurrent_kernel
weights matrix
(see constraints).
bias_constraint: Constraint function applied to the bias vector
(see constraints).
dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the inputs.
recurrent_dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the recurrent state.
implementation: Implementation mode, either 1 or 2.
Mode 1 will structure its operations as a larger number of
smaller dot products and additions, whereas mode 2 will
batch them into fewer, larger operations. These modes will
have different performance profiles on different hardware and
for different applications.
CuDNNGRU
keras.layers.CuDNNGRU(units, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, return_sequences=False, return_state=False, stateful=False)
Fast GRU implementation backed by CuDNN.
Can only be run on GPU, with the TensorFlow backend.
Arguments
units: Positive integer, dimensionality of the output space.
kernel_initializer: Initializer for the kernel
weights matrix,
used for the linear transformation of the inputs.
(see initializers).
recurrent_initializer: Initializer for the recurrent_kernel
weights matrix,
used for the linear transformation of the recurrent state.
(see initializers).
bias_initializer: Initializer for the bias vector
(see initializers).
kernel_regularizer: Regularizer function applied to
the kernel
weights matrix
(see regularizer).
recurrent_regularizer: Regularizer function applied to
the recurrent_kernel
weights matrix
(see regularizer).
bias_regularizer: Regularizer function applied to the bias vector
(see regularizer).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see regularizer).
kernel_constraint: Constraint function applied to
the kernel
weights matrix
(see constraints).
recurrent_constraint: Constraint function applied to
the recurrent_kernel
weights matrix
(see constraints).
bias_constraint: Constraint function applied to the bias vector
(see constraints).
return_sequences: Boolean. Whether to return the last output.
in the output sequence, or the full sequence.
return_state: Boolean. Whether to return the last state
in addition to the output.
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.
CuDNNLSTM
keras.layers.CuDNNLSTM(units, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, return_sequences=False, return_state=False, stateful=False)
Fast LSTM implementation with CuDNN.
Can only be run on GPU, with the TensorFlow backend.
Arguments
units: Positive integer, dimensionality of the output space.
kernel_initializer: Initializer for the kernel
weights matrix,
used for the linear transformation of the inputs.
(see initializers).
unit_forget_bias: Boolean.
If True, add 1 to the bias of the forget gate at initialization.
Setting it to true will also force bias_initializer="zeros"
.
This is recommended in Jozefowicz et al. (2015).
recurrent_initializer: Initializer for the recurrent_kernel
weights matrix,
used for the linear transformation of the recurrent state.
(see initializers).
bias_initializer: Initializer for the bias vector
(see initializers).
kernel_regularizer: Regularizer function applied to
the kernel
weights matrix
(see regularizer).
recurrent_regularizer: Regularizer function applied to
the recurrent_kernel
weights matrix
(see regularizer).
bias_regularizer: Regularizer function applied to the bias vector
(see regularizer).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see regularizer).
kernel_constraint: Constraint function applied to
the kernel
weights matrix
(see constraints).
recurrent_constraint: Constraint function applied to
the recurrent_kernel
weights matrix
(see constraints).
bias_constraint: Constraint function applied to the bias vector
(see constraints).
return_sequences: Boolean. Whether to return the last output.
in the output sequence, or the full sequence.
return_state: Boolean. Whether to return the last state
in addition to the output.
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.