tf.keras.layers.GRU

See Stable See Nightly

Gated Recurrent Unit - Cho et al. 2014.

Inherits From: RNN, Layer, Operation

tf.keras.layers.GRU(
    units,
    activation='tanh',
    recurrent_activation='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,
    seed=None,
    return_sequences=False,
    return_state=False,
    go_backwards=False,
    stateful=False,
    unroll=False,
    reset_after=True,
    use_cudnn='auto',
    **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Working with preprocessing layers	Transfer learning for video classification with MoViNet Neural machine translation with attention Recommending movies: retrieval using a sequential model Text generation with an RNN

Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or backend-native) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation when using the TensorFlow backend.

The requirements to use the cuDNN implementation are:

1. activation == tanh
2. recurrent_activation == sigmoid
3. recurrent_dropout == 0
4. unroll is False
5. use_bias is True
6. reset_after is True
7. Inputs, if use masking, are strictly right-padded.
8. Eager execution is enabled in the outermost context.

There are two variants of the GRU implementation. The default one is based on v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original 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. To use this variant, set reset_after=True and recurrent_activation='sigmoid'.

For example:

inputs = np.random.random((32, 10, 8))
gru = keras.layers.GRU(4)
output = gru(inputs)
output.shape
(32, 4)
gru = keras.layers.GRU(4, return_sequences=True, return_state=True)
whole_sequence_output, final_state = gru(inputs)
whole_sequence_output.shape
(32, 10, 4)
final_state.shape
(32, 4)

Args
units	Positive integer, dimensionality of the output space.
activation	Activation function to use. 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. Default: sigmoid (sigmoid). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).
use_bias	Boolean, (default True), whether the layer should use a bias vector.
kernel_initializer	Initializer for the kernel weights matrix, used for the linear transformation of the inputs. Default: "glorot_uniform".
recurrent_initializer	Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state. Default: "orthogonal".
bias_initializer	Initializer for the bias vector. Default: "zeros".
kernel_regularizer	Regularizer function applied to the kernel weights matrix. Default: None.
recurrent_regularizer	Regularizer function applied to the recurrent_kernel weights matrix. Default: None.
bias_regularizer	Regularizer function applied to the bias vector. Default: None.
activity_regularizer	Regularizer function applied to the output of the layer (its "activation"). Default: None.
kernel_constraint	Constraint function applied to the kernel weights matrix. Default: None.
recurrent_constraint	Constraint function applied to the recurrent_kernel weights matrix. Default: None.
bias_constraint	Constraint function applied to the bias vector. Default: None.
dropout	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0.
recurrent_dropout	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0.
seed	Random seed for dropout.
return_sequences	Boolean. Whether to return the last output in the output sequence, or the full sequence. Default: False.
return_state	Boolean. Whether to return the last state in addition to the output. Default: False.
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 is "before", True is "after" (default and cuDNN compatible).
use_cudnn	Whether to use a cuDNN-backed implementation. "auto" will attempt to use cuDNN when feasible, and will fallback to the default implementation if not.

Call arguments
inputs	A 3D tensor, with shape (batch, timesteps, feature).
mask	Binary tensor of shape (samples, timesteps) indicating whether a given timestep should be masked (optional). An individual True entry indicates that the corresponding timestep should be utilized, while a False entry indicates that the corresponding timestep should be ignored. Defaults to None.
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 only relevant if dropout or recurrent_dropout is used (optional). Defaults to None.
initial_state	List of initial state tensors to be passed to the first call of the cell (optional, None causes creation of zero-filled initial state tensors). Defaults to None.

Attributes
activation
bias_constraint
bias_initializer
bias_regularizer
dropout
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
kernel_constraint
kernel_initializer
kernel_regularizer
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.
recurrent_activation
recurrent_constraint
recurrent_dropout
recurrent_initializer
recurrent_regularizer
reset_after
units
use_bias

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config	A Python dictionary, typically the output of get_config.

Returns
An operation instance.

get_initial_state

View source

get_initial_state(
    batch_size
)

inner_loop

View source

inner_loop(
    sequences, initial_state, mask, training=False
)

reset_state

View source

reset_state()

reset_states

View source

reset_states()