GRU

namespace: Rindow\NeuralNetworks\Layer
classname: GRU

Gated Recurrent Unit RNN Layer.

Methods

constructor

$builer->GRU(
    int $units,
    array $input_shape=null,
    string|object $activation='tanh',
    string|object $recurrent_activation='sigmoid',
    bool $use_bias=true,
    string|callable $kernel_initializer='glorot_uniform',
    string|callable $recurrent_initializer='orthogonal',
    string|callable $bias_initializer='zeros',
    bool $return_sequences=false,
    bool $return_state=false,
    bool $go_backwards=false,
    bool $stateful=false,
    bool $reset_after=true,
    string $name=null,
)

You can create a GRU layer instances with the Layer Builder.

Arguments

units: Dimensionality of the output space.

Options

input_shape: Tell the first layer the shape of the input data. In input_shape, the batch dimension is not included.
activation: Activation function. Default is ‘tanh’.
recurrent_activation: Recurrent Activation function. Default is ‘sigmoid’.
use_bias: Use bias. Default is true.
kernel_initializer: Default is ‘glorot_uniform’.
recurrent_initializer: Default is ‘orthogonal’.
bias_initializer: Default is ‘zeros’.
return_sequences: Whether to return the last output in the output sequence, or the full sequence. Default is false.
return_state: Whether to return the last state in addition to the output. Default is false.
go_backwards: If True, process the input sequence backwards and return the reversed sequence. Default is false.
stateful: N/A.
reset_after: whether to apply reset gate after or before matrix multiplication. Default is true.

forward

public function forward(
    NDArray $inputs,
    Variable|bool $training,
    array $initialStates=null,
    array $options=null
)

Arguments

inputs: A 3D NDArray with shape [batch, timesteps, feature].
training: When training, it is true.
initialStates: List of initial state. Number of the state is one. Shape of the state is [batch, units]. When it have no state, give a null.
options: N/A

Input shape

3D array of shape.

[batchsize, timesteps, features]

Output shape

If “return_state” is true then it returns the list of [outputs, states]. If “return_state” is false then it returns just outputs.

If “return_sequences” is false then the shape of outputs is 2D [batchsize, units]. If “return_sequences” is true then the shape of outputs is 3D [batchsize, timesteps, units].

States shape

The states is list of state. Shape of it is 2D [batchsize, units].

$rnn = $builder->layers()->GRU($units=256,
    return_sequences:true,return_state:true);
....
$inputs = $mo->ones([64,32,128]);
$initialStates = [$mo->ones([64,256])];
....
[$outputs,$states] = $rnn->forward($inputs,true,$initialStates);
# $outputs->shape() : [64,32,256]
# $states[0]->shape() : [64,256]

Example of usage

class Foo extends AbstractModel
{
    public function __construct($backend,$builder)
    {
        ...
        $this->rnn = $builder->layers()->GRU($units=256,
            return_sequences:true,return_state:true);
        ....
    }

    protected function call(.....) 
    {
        ...
        [$outputs,$states] = $this->rnn->forward($inputs,$training,$initialStates);
        ...
    }
}

Rindow Neural Networks

GRU