Batch Processing

Layer Input

The backward loss softmax cross-entropy layer accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Input ID

Input

inputGradient

Pointer to the tensor that stores the input gradient computed on the preceding layer. This input can be an object of any class derived from Tensor.

inputFromForward

Collection of data needed for the backward loss softmax cross-entropy layer. The collection may contain objects of any class derived from Tensor.

Element ID

Element

auxProbabilities

Pointer to the tensor S of size n1 x n2 x ... x nk x ... x np that stores probabilities for the forward loss softmax cross-entropy layer. This input can be an object of any class derived from Tensor.

auxGroundTruth

Pointer to the tensor T of size n1 x n2 x ... x 1 x ... x np that stores the ground truth data for the forward loss softmax cross-entropy layer. This input can be an object of any class derived from Tensor.

Note

The layer does not use inputGradient because there is no input gradient on the last layer.

Layer Parameters

For common parameters of neural network layers, see Common Parameters.

In addition to the common parameters, the backward loss softmax cross-entropy layer has the following parameters:

Parameter

Default Value

Description

algorithmFPType

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

defaultDense

Performance-oriented computation method, the only method supported by the layer.

dimension

1

The dimension index to calculate softmax cross-entropy.

Layer Output

The backward loss softmax cross-entropy layer calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Result ID

Result

gradient

Pointer to the tensor Z of size n1 x n2 x ... x nk x ... x np that stores the result of the backward loss softmax cross-entropy layer. This input can be an object of any class derived from Tensor.

For more complete information about compiler optimizations, see our Optimization Notice.
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