Contents

GRUGradients(_:recurrentWeight:sourceGradient:zState:outputFwd:inputWeight:bias:descriptor:name:)

Creates a GRU gradient operation and returns the gradient tensor values.

Declaration

func GRUGradients(_ source: MPSGraphTensor, recurrentWeight: MPSGraphTensor, sourceGradient: MPSGraphTensor, zState: MPSGraphTensor, outputFwd: MPSGraphTensor, inputWeight: MPSGraphTensor?, bias: MPSGraphTensor?, descriptor: MPSGraphGRUDescriptor, name: String?) -> [MPSGraphTensor]

Parameters

  • source:

    A tensor containing 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,3H] and for inputWeight = nil and bidirectional = YES the layout is [T,N,6H].

  • recurrentWeight:

    A tensor containing the recurrent weights R. For bidirectional the layout is [2,3H,H] and otherwise it is [3H,H].

  • 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 Gru(_:recurrentweight:inputweight:bias:initstate:descriptor:name:) with descriptor.training = YES.

  • outputFwd:

    The first output of Gru(_:recurrentweight:inputweight:bias:initstate: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 [6H,I] and otherwise it is [3H,I].

  • bias:

    A tensor containing the bias b - optional, if missing the operation assumes zeroes. For bidirectional the layout is [6H] and otherwise it is [3H].

  • descriptor:

    A descriptor that defines the parameters for the GRU 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 and for bias.

Discussion

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