Generative Adversarial Networks Projects

Vanishing gradients

During backpropagation, gradient flows backward, from the final layer to the first layer. As it flows backward, it gets increasingly smaller. Sometimes, the gradient is so small that the initial layers learn very slowly or stop learning completely. In this case, the gradient doesn't change the weight values of the initial layers at all, so the training of the initial layers in the network is effectively stopped. This is known as the vanishing gradients problem.

This problem gets worse if we train a bigger network with gradient-based optimization methods. Gradient-based optimization methods optimize a parameter's value by calculating the change in the network's output when we change the parameter's value by a small amount. ...