public struct GRUCell<Scalar> : RecurrentLayerCell where Scalar : TensorFlowFloatingPoint
An GRU cell.
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Declaration
public var updateKernel: Tensor<Scalar>
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Declaration
public var updateRecurrentKernel: Tensor<Scalar>
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Declaration
public var resetKernel: Tensor<Scalar>
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Declaration
public var resetRecurrentKernel: Tensor<Scalar>
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Declaration
public var outputKernel: Tensor<Scalar>
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Declaration
public var outputRecurrentKernel: Tensor<Scalar>
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Declaration
public var updateBias: Tensor<Scalar>
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Declaration
public var updateRecurrentBias: Tensor<Scalar>
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Declaration
public var resetBias: Tensor<Scalar>
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Declaration
public var resetRecurrentBias: Tensor<Scalar>
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Declaration
public var outputBias: Tensor<Scalar>
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Declaration
public var outputRecurrentBias: Tensor<Scalar>
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Declaration
@noDerivative public var stateShape: TensorShape { get }
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Declaration
public typealias State = Tensor<Scalar>
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Declaration
public typealias TimeStepInput = Tensor<Scalar>
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Declaration
public typealias TimeStepOutput = State
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Declaration
public typealias Input = RNNCellInput<TimeStepInput, State>
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Declaration
public typealias Output = RNNCellOutput<TimeStepOutput, State>
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Creates a
GRUCell
with the specified input size and hidden state size.Declaration
public init( inputSize: Int, hiddenSize: Int, kernelInitializer: ParameterInitializer<Scalar> = glorotUniform(), biasInitializer: ParameterInitializer<Scalar> = zeros() )
Parameters
inputSize
The number of features in 2-D input tensors.
hiddenSize
The number of features in 2-D hidden states.