RMSprop is an extension of Adagrad that deals with Adagrad’s radically diminishing learning rates.
RMSprop divides the learning rate by an exponentially decaying average of squared gradients. Hinton suggests the set the fuzz factor epsilon to 0.9.
This optimizer is usually a good choice for recurrent neural networks (RNN).
A good default value for the learning rate is 0.001.
You can adjust these parameters:
The learning rate is controlling the size of the update steps along the gradient. This parameter sets how much of the gradient you update with, where 1 = 100% but normally you set much smaller learning rate, e.g., 0.001.
In our rolling ball analogy, we’re calculating where the ball should roll next in discrete steps (not continuous). How long these discrete steps are is the learning rate.
Choosing a good learning rate is important when training a neural network. The best learning rate is dependent on the individual problem & model. If the ball rolls carefully with a small learning rate we can expect to make consistent but very small progress (this corresponds to having a small learning rate). The risk though is that the ball gets stuck in a local minima not reaching the global minima.
Larger learning rate mean that the weights are changed more every iteration, so that they may reach their optimal value faster, but may also miss the exact optimum.
Smaller learning rate mean that the weights are changed less every iteration, so it may take longer time to reach their optimal value, but they are less likely to miss optima of the loss function.
Learning rate scheduling allows you to use large steps during the first few epochs, then progressively reduce the step size as the weights come closer to their optimal value.
p (epsilon) is called fuzz or decay factor.