# ULM Through DL 11(Far Field)

This is the continuation of the previous blog post. Only difference is that we change the loss function as below:

$loss = MSEloss(z-v) + \lambda \times L1loss(z)$

Summary:

 sigmaoff $$\lambda$$ learningrate numberofepochs batchsize Q1 Q2 Q1perbubble Q2perbubble % gap betweentrainingand validation exp1 1 0.005 2e-5 300 1 13.431614 0.474453 0.531866 0.018680 58.3 epx2 2 0.01 2e-5 300 1 20.737143 1.180204 0.821757 0.046736 4.9

Experiment 1: learning rate=2e-5, sigma of kernel f=1, regularizer parameter=0.005

Percentage gap between training and validation error ( $$\frac{validation errror – training error}{training error}*100$$ ) = 58.3

Average quantitative metric1 is 13.431614

Average metric1 per bubble is 0.531866

Average quantitative metric2 is 0.474453

Average metric2 per bubble is 0.018680

Worst Case:Center Location(x,z) = 29.6065 mm and 48.8026 mm , Average Metric1 per bubble: 0.874942, Average Metric2 per bubble: : 0.052237

Best Case:Center Location(x,z) =9.894499999999999 mm, 39.9322 mm, Average Metric1 per bubble: 0.483357, Average Metric2 per bubble: : 0.014553

Experiment 2: learning rate=2e-5, sigma of kernel f=2, regularizer parameter=0.01

Percentage gap between training and validation error ( $$\frac{validation errror – training error}{training error}*100$$ ) = 4.9

Average quantitative metric1 is 20.737143

Average metric1 per bubble is 0.821757

Average quantitative metric2 is 1.180204

Average metric2 per bubble is 0.046736

Worst Case:Center Location(x,z) = 29.6065 mm and 48.8026 mm , Average Metric1 per bubble: 0.955968, Average Metric2 per bubble: : 0.059352

Best Case:Center Location(x,z) =6.1985 mm, 36.4826 mm, Average Metric1 per bubble: 0.798583, Average Metric2 per bubble: : 0.043044