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Forward by name


Execute the forward pass of the model (alls layers).

Input parameters


Model in : model architecture.

data in : array

name : string, name of input layer.
input_data : variant, input(s) data of the model.

train_mode : enum

    • Nothing :Β do nothing.
    • Training :Β Do calculation to prepare future loss and backward function. This mode allow the future use of loss and backward function. Caution : if you use this mode with GPUΒ you must connect loss and backward to free memory and avoid memory leak.
    • Predict :Β Do optimized prediction whithout loss and backward preparation. Does not allow to use loss and backward (error 5009). If you want to get metric loss, use metric function after forward.


Output parameters


Model out : model architecture.

Β outputs_data :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 prediction.

      • 2D Prediction
      • 3D Prediction
      • 4D Prediction
      • 5D Prediction
      • 6D Prediction

Β shape :Β array,Β shape of layer.
Β data :Β variant,Β output layer prediction. This data correspond to an array of type [sgl] of dimension 2D, 3D, 4D, 5D or 6D (indicated by dimension).

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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