Aim: To find a model that trains the simulated data of Gaussian beam spot moving in a vertical direction by the application of a sinusoidal signal. The data also includes random uniform noise ranging from 0 to 10.
All the attachments are in the zip folder.
I simulated images 128*128 at 10 frames/sec by applying a sine wave of frequency 0.2Hz that moves the beam spot, added random uniform noise ranging from 0 to 10 & resized the image frame using opencv to 64*64. 1000 cycles of this data is taken as train & 300 cycles as test data for the following cases. Optimizer = Nadam (learning rate = 0.001), loss function used = mean squared error, batch size = 32,
256 (dropout = 0.1) -> 256 (dropout = 0.1) -> 1
Activation : selu selu
Number of epochs = 240.
Variation in loss value of train & test datasets is given in Attachment 1 of the attached zip folder & the applied signal as well as the output of neural network given in Attachments 2 & 3 (zoomed version of 2).
The model fits well but there is no training since test loss is lower than train loss value. I found in several sites that dropout of some of the nodes during training but retaining them during test could be the probable reason for this (https://stackoverflow.com/questions/48393438/validation-loss-when-using-dropout , http://forums.fast.ai/t/validation-loss-lower-than-training-loss/4581 ). So I removed dropout while training next time.
Activation : selu selu linear
Number of epochs = 200.
Variation in loss value of train & test datasets is given in Attachment 4 of the attached zip folder & the applied signal as well as the output of neural network given in Attachments 5 & 6 (zoomed version of 2).
But still no improvement.
I changed the optimizer to Adam and tried with the same model topology & hyperparameters as case 2 with no success (Attachments 7,8 & 9).
Finally I think this is because I'm training & testing on the same data. So I'm now training with the simulated video but moving it by a maximum of 2 pixels only and testing with a video of ETMY that we had captured earlier.