Contents

singleGateRNNGradients(_:recurrentWeight:sourceGradient:zState:inputWeight:bias:initState:mask:descriptor:name:)

Creates a single-gate RNN gradient operation and returns the gradient tensor values.

Declaration

func singleGateRNNGradients(_ source: MPSGraphTensor, recurrentWeight: MPSGraphTensor, sourceGradient: MPSGraphTensor, zState: MPSGraphTensor, inputWeight: MPSGraphTensor?, bias: MPSGraphTensor?, initState: MPSGraphTensor?, mask: MPSGraphTensor?, descriptor: MPSGraphSingleGateRNNDescriptor, name: String?) -> [MPSGraphTensor]

Parameters

  • source:

    A tensor that contains the source data x[t] with the data layout [T,N,I]. In case inputWeight = nil and bidirectional = NO then the layout is [T,N,H] and for inputWeight = nil and bidirectional = YES the layout is [T,N,2H].

  • recurrentWeight:

    A tensor containing the recurrent weights R. For bidirectional the layout is [2,H,H] and otherwise it is [H,H]. Note: For bidirectional this tensor must have a static shape.

  • sourceGradient:

    The input gradient, that is the gradient of a tensor with respect to the first output of the forward pass.

  • zState:

    The second output of Singlegaternn(_:recurrentweight:inputweight:bias:initstate:mask:descriptor:name:) with descriptor.training = YES.

  • inputWeight:

    A tensor containing the input weights matrix W - optional, if missing the operation assumes a diagonal unit-matrix. For bidirectional the layout is [2H,I] and otherwise it is [H,I].

  • bias:

    A tensor containing the bias b - optional, if missing the operation assumes zeroes. For bidirectional the layout is [2H] and otherwise it is [H].

  • initState:

    The initial internal state of the RNN h[-1] - optional, if missing the operation assumes zeroes. For bidirectional the layout is [N,2H] and otherwise it is [N,H].

  • mask:

    A tensor containing the mask m - optional, if missing the operation assumes ones. This is useful for dropout support.

  • descriptor:

    A descriptor that defines the parameters for the RNN operation.

  • name:

    The name for the operation.

Return Value

A valid MPSGraphTensor array containing gradients for each input tensor, except for sourceGradient and mask. In case an input is nil, no gradient will be returned for it. The order of the gradients will be: for source, for recurrentWeight, for inputWeight, for bias and finally for initState.

Discussion

For details of this operation and parameters, refer to documentation of singleGateRNN(_:recurrentWeight:inputWeight:bias:initState:mask:descriptor:name:).