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tf.keras.layers.GRU

Class GRU

Gated Recurrent Unit - Cho et al. 2014.

Inherits From: RNN

Aliases:

  • Class tf.compat.v1.keras.layers.GRU
  • Class tf.keras.layers.GRU
View source on GitHub

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 pass None, 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 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.
  • recurrent_initializer: Initializer for the recurrent_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 the kernel weights matrix.
  • recurrent_regularizer: Regularizer function applied to the recurrent_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 the kernel weights matrix.
  • recurrent_constraint: Constraint function applied to the recurrent_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.
  • 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.
  • reset_after: GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before" (default), True = "after" (CuDNN compatible).

Call arguments:

  • inputs: A 3D tensor.
  • mask: Binary tensor of shape (samples, timesteps) indicating whether a given timestep should be masked.
  • 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.
  • initial_state: List of initial state tensors to be passed to the first call of the cell.

__init__

View source

__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

bias_constraint

bias_initializer

bias_regularizer

dropout

implementation

kernel_constraint

kernel_initializer

kernel_regularizer

recurrent_activation

recurrent_constraint

recurrent_dropout

recurrent_initializer

recurrent_regularizer

reset_after

states

units

use_bias

Methods

get_initial_state

View source

get_initial_state(inputs)

reset_states

View source

reset_states(states=None)