Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 4

Batch Processing

Layer Input

The backward reshape 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 tensor of size m1 x ... x mq that stores the input gradient G computed on the preceding layer. This input can be an object of any class derived from Tensor.

inputFromForward

Collection of input data needed for the backward reshape layer.

Element ID

Element

auxInputDimensions

Collection of integers that stores the dimension sizes of the input tensors in the forward computation step: n1, ... , np.

Layer Output

The backward reshape 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 tensor of size n1 × n2 × ... × np that stores the result of the backward reshape layer. This input can be an object of any class derived from Tensor.

Examples

C++: reshape_layer_dense_batch.cpp

Java*: ReshapeLayerDenseBatch.java

Python*: reshape_layer_dense_batch.py

See Also