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Hands-On Generative Adversarial Networks with PyTorch

Optimizations and Learning Rate

Explore various gradient-based optimization methods including SGD, Momentum, Nesterov, AdaGrad, RMSprop, and Adam to understand their roles in training GANs. Learn how to set and adjust learning rates for efficient training and when to use techniques like gradient and weight clipping to ensure model stability and convergence.

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Here, we will only discuss gradient-based optimization methods, which are most commonly used in GANs. Different gradient methods have their own strengths and weaknesses. There isn't a universal optimization method that can solve every problem. Therefore, we should choose them wisely when it comes to different practical problems.

Types of optimization methods

Let’s have a look at some now:

  1. SGD (calling optim.SGD with momentum=0 and nesterov=False): It works fast and well for shallow networks. However, it can be very slow for deeper networks and may not even converge for deep networks:

In this equation, θt\theta_t is the parameters at iteration step tt, η\eta is the learning rate, and ∇J\nabla J is the gradient of the objective function, JJ.

  1. Momentum (calling optim.SGD with the momentum argument when it's larger than 0 and nestrov=False): It is one of the most commonly used optimization methods. This method combines the updates of the previous step with the gradient at the current step so that it takes a smoother trajectory than SGD. The training speed of Momentum is often faster than SGD and it generally works well for both shallow and deep networks:

In this equation, ...