Train Output

Train Output

Description

Setup and add “Output Train” node into the model during the definition graph step.

 

Documentation illustration (illustration unavailable in the archive).

Input parameters

 

 index : integer, this parameter refers to the position of the input within the ONNX graph. When executing a model with multiple inputs, the index helps you identify which input you are targeting. It is especially useful when configuring input data, using the Input Data polymorph found in the Deep Learning → Runtime palette.
 Graph in : object, ONNX model architecture.
 Parameters : cluster

dtype : enum, the data type for the elements of the output tensor.
Default value “FLOAT”.
 Loss : cluster, this cluster defines the loss function used for model training.

Documentation illustration (illustration unavailable in the archive). enum : enum, an enumeration indicating the loss type (e.g., MSE, CrossEntropy, etc.). If enum is set to CustomLoss, the custom class on the right will be used as the loss function. Otherwise, the selected loss will be applied with its default configuration.
Documentation illustration (illustration unavailable in the archive). Class : object, a custom loss class instance.

 name (optional) : string, name of the node.

 

Documentation illustration (illustration unavailable in the archive).

Output parameters

 

 Graph out : object, ONNX model architecture.

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).