# Learning Rate based on error of the network

I am not an expert and do not have theoretical justification for that, but it seems to me that the smaller network error is, the smaller learning rate should be.

Is there an algorithm to dynamically update learning rate based on total error of the network without relying on any hyper-parameters ?

Your intuition is on point, and shrinking the learning rate like this is often referred to as "annealing". But linking the learning rate to error magnitude neglects certain problematic error surface topologies. An excellent motivating example is the Rosenbrock "Banana" Function, which is often used as a test case for optimization algorithms. The "banana" is a low error valley which hides the global minimum. If an optimization path finds its way into this valley, the path to the global minimum is along a nearly flat gradient. If you use an optimization algorithm that naively shrinks the learning rate relative to the error, you're going to get stuck as soon as you hit the valley. On the one hand: congrats! You've achieved a low error solution. But you're not necessarily anywhere near the global minimum. So how can we do better?

An approach used by modern gradient-based methods like Adagrad, RMSProp, and Adam is to separately assign learning rates to each parameter, and tie the learning rate to the magnitude of the respective parameter's update. The Stanford CS231n lecture notes explains:

# Assume the gradient dx and parameter vector x cache += dx**2 x += - learning_rate * dx / (np.sqrt(cache) + eps)
• shall I add eps to denominator only if np.sqrt(cache) equals 0 ? – koryakinp Mar 8 '18 at 19:39