This function allow to evaluate a trained neural network model on new test data-set.


ANNAPI in: The neural network model reference in.

Threshold: When classification task is performed, threshold value is used to isolate classes. For example, in a binary classification (0 and 1 classes), if the output is 0. 4 and threshold is 0.3, the output result will be classified as class 1.

Test data path: The path that points to the test data in csv format. This test data can content multiple samples, and each sample is in 1 row. This test format has the same structure as the training data set.

error in(no error): describes the error status before this VI or function runs. The default is no error. If an error occurred before this VI or function runs, the VI or function passes the error in value to error out. This VI or function runs normally only if no error occurred before this VI or function runs.


ANNAPI out: The neural network model reference out.

Inputs: The input data read from test data-set

Targets: The output data read from test data set (the output column has "output, target, and class" keywords in the header (first row)

Outputs: Predicted output calculated by feeding Inputs to a trained neural network model.

Accuracy: The predicted outputs will be compared against Targets to identify how many percentage match. The threshold is used in this comparison.

error out: contains error information. If error in indicates that an error occurred before this VI or function ran, error out contains the same error information. Otherwise, it describes the error status that this VI or function produces. Right-click the error out indicator on the front panel and select Explain Error from the shortcut menu for more information about the error.