tf.compat.v1.keras.layers.GRU

Gated Recurrent Unit - Cho et al. 2014.

Inherits From: RNN

tf.compat.v1.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, **kwargs
)

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.

Attributes
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

reset_states

View source

reset_states(
    states=None
)

Reset the recorded states for the stateful RNN layer.

Can only be used when RNN layer is constructed with stateful = True. Args: states: Numpy arrays that contains the value for the initial state, which will be feed to cell at the first time step. When the value is None, zero filled numpy array will be created based on the cell state size.

Raises
AttributeError	When the RNN layer is not stateful.
ValueError	When the batch size of the RNN layer is unknown.
ValueError	When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise.