Class GRU
Inherits From: RNN
Defined in tensorflow/python/keras/layers/recurrent.py
.
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 (GPU-only) 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. 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. 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 thekernel
weights matrix, used for the linear transformation of the inputs.recurrent_initializer
: Initializer for therecurrent_kernel
weights matrix, used for the linear transformation of the recurrent state.bias_initializer
: Initializer for the bias vector.kernel_regularizer
: Regularizer function applied to thekernel
weights matrix.recurrent_regularizer
: Regularizer function applied to therecurrent_kernel
weights matrix.bias_regularizer
: Regularizer function applied to the bias vector.activity_regularizer
: Regularizer function applied to the output of the layer (its "activation")..kernel_constraint
: Constraint function applied to thekernel
weights matrix.recurrent_constraint
: Constraint function applied to therecurrent_kernel
weights matrix.bias_constraint
: Constraint function applied to the bias vector.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 speed-up a RNN, although it tends to be more memory-intensive. 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).
__init__
__init__(
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,
**kwargs
)
Properties
activation
activity_regularizer
Optional regularizer function for the output of this layer.
bias_constraint
bias_initializer
bias_regularizer
dropout
dtype
implementation
input
Retrieves the input tensor(s) of a layer.
Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.
Returns:
Input tensor or list of input tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.
Raises:
RuntimeError
: If called in Eager mode.AttributeError
: If no inbound nodes are found.
input_mask
Retrieves the input mask tensor(s) of a layer.
Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
Returns:
Input mask tensor (potentially None) or list of input mask tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.
input_shape
Retrieves the input shape(s) of a layer.
Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.
Returns:
Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).
Raises:
AttributeError
: if the layer has no defined input_shape.RuntimeError
: if called in Eager mode.
kernel_constraint
kernel_initializer
kernel_regularizer
losses
Losses which are associated with this Layer
.
Variable regularization tensors are created when this property is accessed,
so it is eager safe: accessing losses
under a tf.GradientTape
will
propagate gradients back to the corresponding variables.
Returns:
A list of tensors.
name
non_trainable_variables
non_trainable_weights
output
Retrieves the output tensor(s) of a layer.
Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.
Returns:
Output tensor or list of output tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.RuntimeError
: if called in Eager mode.
output_mask
Retrieves the output mask tensor(s) of a layer.
Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
Returns:
Output mask tensor (potentially None) or list of output mask tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.
output_shape
Retrieves the output shape(s) of a layer.
Only applicable if the layer has one output, or if all outputs have the same shape.
Returns:
Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).
Raises:
AttributeError
: if the layer has no defined output shape.RuntimeError
: if called in Eager mode.
recurrent_activation
recurrent_constraint
recurrent_dropout
recurrent_initializer
recurrent_regularizer
reset_after
states
trainable_variables
trainable_weights
units
updates
use_bias
variables
Returns the list of all layer variables/weights.
Alias of self.weights
.
Returns:
A list of variables.
weights
Returns the list of all layer variables/weights.
Returns:
A list of variables.
Methods
tf.keras.layers.GRU.__call__
__call__(
inputs,
initial_state=None,
constants=None,
**kwargs
)
Wraps call
, applying pre- and post-processing steps.
Arguments:
inputs
: input tensor(s).*args
: additional positional arguments to be passed toself.call
.**kwargs
: additional keyword arguments to be passed toself.call
.
Returns:
Output tensor(s).
Raises:
ValueError
: if the layer'scall
method returns None (an invalid value).
tf.keras.layers.GRU.__setattr__
__setattr__(
name,
value
)
Implement setattr(self, name, value).
tf.keras.layers.GRU.apply
apply(
inputs,
*args,
**kwargs
)
Apply the layer on a input.
This is an alias of self.__call__
.
Arguments:
inputs
: Input tensor(s).*args
: additional positional arguments to be passed toself.call
.**kwargs
: additional keyword arguments to be passed toself.call
.
Returns:
Output tensor(s).
tf.keras.layers.GRU.build
build(input_shape)
Creates the variables of the layer (optional, for subclass implementers).
This is a method that implementers of subclasses of Layer
or Model
can override if they need a state-creation step in-between
layer instantiation and layer call.
This is typically used to create the weights of Layer
subclasses.
Arguments:
input_shape
: Instance ofTensorShape
, or list of instances ofTensorShape
if the layer expects a list of inputs (one instance per input).
tf.keras.layers.GRU.compute_mask
compute_mask(
inputs,
mask
)
Computes an output mask tensor.
Arguments:
inputs
: Tensor or list of tensors.mask
: Tensor or list of tensors.
Returns:
None or a tensor (or list of tensors, one per output tensor of the layer).
tf.keras.layers.GRU.compute_output_shape
compute_output_shape(input_shape)
Computes the output shape of the layer.
Assumes that the layer will be built to match that input shape provided.
Arguments:
input_shape
: Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.
Returns:
An input shape tuple.
tf.keras.layers.GRU.count_params
count_params()
Count the total number of scalars composing the weights.
Returns:
An integer count.
Raises:
ValueError
: if the layer isn't yet built (in which case its weights aren't yet defined).
tf.keras.layers.GRU.from_config
@classmethod
from_config(
cls,
config
)
Creates a layer from its config.
This method is the reverse of get_config
,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights
).
Arguments:
config
: A Python dictionary, typically the output of get_config.
Returns:
A layer instance.
tf.keras.layers.GRU.get_config
get_config()
Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by Network
(one layer of abstraction above).
Returns:
Python dictionary.
tf.keras.layers.GRU.get_initial_state
get_initial_state(inputs)
tf.keras.layers.GRU.get_input_at
get_input_at(node_index)
Retrieves the input tensor(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A tensor (or list of tensors if the layer has multiple inputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.GRU.get_input_mask_at
get_input_mask_at(node_index)
Retrieves the input mask tensor(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A mask tensor (or list of tensors if the layer has multiple inputs).
tf.keras.layers.GRU.get_input_shape_at
get_input_shape_at(node_index)
Retrieves the input shape(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A shape tuple (or list of shape tuples if the layer has multiple inputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.GRU.get_losses_for
get_losses_for(inputs)
Retrieves losses relevant to a specific set of inputs.
Arguments:
inputs
: Input tensor or list/tuple of input tensors.
Returns:
List of loss tensors of the layer that depend on inputs
.
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.GRU.get_output_at
get_output_at(node_index)
Retrieves the output tensor(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A tensor (or list of tensors if the layer has multiple outputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.GRU.get_output_mask_at
get_output_mask_at(node_index)
Retrieves the output mask tensor(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A mask tensor (or list of tensors if the layer has multiple outputs).
tf.keras.layers.GRU.get_output_shape_at
get_output_shape_at(node_index)
Retrieves the output shape(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A shape tuple (or list of shape tuples if the layer has multiple outputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.GRU.get_updates_for
get_updates_for(inputs)
Retrieves updates relevant to a specific set of inputs.
Arguments:
inputs
: Input tensor or list/tuple of input tensors.
Returns:
List of update ops of the layer that depend on inputs
.
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.GRU.get_weights
get_weights()
Returns the current weights of the layer.
Returns:
Weights values as a list of numpy arrays.
tf.keras.layers.GRU.reset_states
reset_states(states=None)
tf.keras.layers.GRU.set_weights
set_weights(weights)
Sets the weights of the layer, from Numpy arrays.
Arguments:
weights
: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output ofget_weights
).
Raises:
ValueError
: If the provided weights list does not match the layer's specifications.