Control Neural Network Training

Training Set

The largest single determinant affecting the network training phase is the composition of the Training Set. The best method for controlling the network training phase is to construct a Training Image Sample Set for your tool. In this way, you can separate images/views into categories that allow you to determine if your tool is generalizing your images/views properly.

Validation Loss (High Detail Mode)

As High Detail modes (Green Classify High Detail Mode and Red Analyze High Detail Mode) provide the training with validation, you can control the network training with monitoring validation loss. During training at the end of every 1/8 epoch, the neural network calculates the loss value from the validation set you previously configured.

The validation loss stands for the performance of your trained network in terms of accuracy of classification (Green Classify High Detail Mode) or segmentation (Red Analyze High Detail Mode), which means that smaller loss generally means a better network. So it is better to have this value close to 0.

Though, to gain the full-sight regarding how your network truly performs well, you have to test the trained network against some separate data (Test Data) to prevent overfitting.

The validation loss of Green High Detail Mode is calculated per view as the classification is executed on each view. Meanwhile, The validation loss of Red Analyze High Detail Mode is calculated per pixel as the segmentation, which is the binary classification among "Good" or "Defect", is executed on each pixel.

 

Validation Loss (%)

• Green Classify High Detail mode

  • 100 - The average value of the classification precisions of each class

• Red Analyze High Detail mode

  • 100 - The recall of "Defect" class + 40 x (100 - The recall of "Good" class)