Recently, I worked with GANs for generating fake accelerometer data for human activity (i.e., mHEALTH dataset).
I have tried to implement Improved Technique for Training GAN (i.e., Conditional WGAN GP) from here. I have made some modifications on the code according to my data. My goal is to generate fake 1D time-series from single accelerometer data with size (100,1) from input noise vector with size of 100.
However, when I run the code (training process), I got "Nan" for both discriminator loss and generator loss.
Did anyone have same problem like this ? Why this problem happen and how to solve this problem ? Thanks for your help !
Here my complete source code
WARNING:tensorflow:Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?
0 [D loss: 8.756878] [G loss: -0.499155]
10 [D loss: 5.958789] [G loss: -0.488709]
20 [D loss: 4.041764] [G loss: -0.473811]
30 [D loss: 2.644764] [G loss: -0.463494]
40 [D loss: 2.244551] [G loss: -0.460721]
50 [D loss: nan] [G loss: nan]
60 [D loss: nan] [G loss: nan]
70 [D loss: nan] [G loss: nan]
80 [D loss: nan] [G loss: nan]
90 [D loss: nan] [G loss: nan]