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I have a binary classification problem with 60.000 pos and 60.000 neg examples of 40x20 images. I tried two different models. One is simple one hidden layer fully connected model and the other is a CIFAR model provided in the examples. Fully connected model can reach 96% at the end but CIFAR model does not improve and its loss values is just waving around a constant value. I tried to give learning rate 0 to see something wrong about the data but loss value stays same expect some changes around ~0.00001. I also tried very small learning rate but the result is same and loss value does not decrease. I believe if there would be something wrong about the data fully connected model would also not toe converge. What would you suggest to investigate the problem more?