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Loss by name


Compute the loss according to the one(s) selected during initialization.

Input parameters


Model in : model architecture.

y_true : array

name : string, name of output layer.
y_true_data : variant, true data(s) of the model.

Output parameters


Model out : model architecture.

Β loss_data :Β cluster

Β loss :Β float,Β loss value.
Β loss_derivate :Β array

Β name :Β string,Β name of layer.
Β index :Β integer,Β index of layer.
Β dimension :Β enum,Β dimension of the loss derivative.

          • 2D Loss Derivate
          • 3D Loss Derivate
          • 4D Loss Derivate
          • 5D Loss Derivate
          • 6D Loss Derivate

Β shape :Β array,Β shape of layer.
Β data :Β variant,Β output layer predictions.

Β model_informations :Β cluster

Β Model Name :Β string,Β name of model.
Β Execution Time :Β integer,Β execution time of the backward.
Β Device :Β enum,Β device where the process is executed.

      • Native LabVIEW
      • GPU



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 HAIBAL library to run it).

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