Batch Processing

Layer Input

The forward concat 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

inputLayerData

Collection of tensors of size n1 x ... x nki x ... x np that stores the input data for the forward concat layer. This collection can contain objects of any class derived from Tensor.

Layer Parameters

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

In addition to the common parameters, the forward concat layer has the following parameters:

Parameter

Default Value

Description

concatDimension

0

Index of the dimension along which concatenation should be implemented.

Layer Output

The forward concat 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

value

Pointer to the tensor of size n1 x ... x nk x ... x np that stores the result of the forward concat layer. This input can be an object of any class derived from Tensor.

resultForBackward

Collection of data needed for the backward concat layer.

Element ID

Element

auxInputDimensions

Collection of integers that stores the sizes of the input tensors along concatDimension: nk1, ..., nkt.

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