Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 5
The forward 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 |
|
---|---|---|
data |
Pointer to tensor of size n1 × n2 × ... × np that stores the input data for the forward reshape layer. This input can be an object of any class derived from Tensor. |
For common parameters of neural network layers, see Common Parameters.
In addition to the common parameters, the forward reshape layer has the following parameters:
Parameter |
Default Value |
Description |
|
---|---|---|---|
reshapeDimensions |
Not applicable |
Collection of dimension sizes: m1, m2, ... , mq for the output Tensor. |
The forward 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 |
|
---|---|---|
value |
Pointer to tensor of size m1 × m2 × ... x mq that stores the result of the forward reshape layer. This input can be an object of any class derived from Tensor. |
|
layerData |
Collection of data needed for the backward reshape layer. |
|
Element ID |
Element |
|
auxInputDimensions |
Collection of integers that stores the dimension sizes of the input tensor in the forward computation step: n1, n2, ... np. |
C++: reshape_layer_dense_batch.cpp
Java*: ReshapeLayerDenseBatch.java
Python*: reshape_layer_dense_batch.py