Recurrent..::..GRU Method

Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014. Their performance on polyphonic music modeling and speech signal modeling was found to be similar to that of long short-term memory.[1] They have fewer parameters than LSTM, as they lack an output gate

Namespace:  SiaNet.NN
Assembly:  SiaNet (in SiaNet.dll)

Syntax


public static Function GRU(
	int inputDim,
	uint hiddenSize,
	uint numLayers,
	bool bidirectional,
	string weightInitializer
)
Public Shared Function GRU ( _
	inputDim As Integer, _
	hiddenSize As UInteger, _
	numLayers As UInteger, _
	bidirectional As Boolean, _
	weightInitializer As String _
) As Function
public:
static Function^ GRU(
	int^ inputDim, 
	unsigned int^ hiddenSize, 
	unsigned int^ numLayers, 
	bool^ bidirectional, 
	String^ weightInitializer
)

Parameters

inputDim
Type: Int32
The input dimension.
hiddenSize
Type: UInt32
Size of the hidden layer.
numLayers
Type: UInt32
The number layers.
bidirectional
Type: Boolean
If bidirectional RNN
weightInitializer
Type: String
The weight initializer.

Return Value