Mono Loss Input 5D

Graiphic · 2023-05-30

Public technical reference documentation from Graiphic. For current SOTA installation and toolkit guides, visit the documentation center.

Description

Execute inference forward with Mono 5D Float Input Data (Academic Training Session).
Documentation illustration (illustration unavailable in the archive).

Input parameters

Academic Training in : object, academic training session.
5D Input Data : array, 5D array of data with any type : integers (signed/unsigned), floats, doubles, booleans, or strings.

Output parameters

Academic Training out : object, academic training session.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).