Should learning rate decay?
Yes, absolutely. From my own experience, it’s very useful to Adam with learning rate decay. Without decay, you have to set a very small learning rate so the loss won’t begin to diverge after decrease to a point.
What will happen when the learning rate is set to 0?
If your learning rate is set too low, training will progress very slowly as you are making very tiny updates to the weights in your network. However, if your learning rate is set too high, it can cause undesirable divergent behavior in your loss function.
How do you set learning rate decay?
The mathematical form of time-based decay is lr = lr0/(1+kt) where lr , k are hyperparameters and t is the iteration number. Looking into the source code of Keras, the SGD optimizer takes decay and lr arguments and update the learning rate by a decreasing factor in each epoch.
How is the decay of a learning rate modeled?
Modeled mathematically, we can define our step-based decay equation as: Where is the initial learning rate, is the factor value controlling the rate in which the learning date drops, D is the “Drop every” epochs value, and E is the current epoch.
What is the decay factor of the Keras schedule?
Figure 2: Keras learning rate step-based decay. The schedule in red is a decay factor of 0.5 and blue is a factor of 0.25. One popular learning rate scheduler is step-based decay where we systematically drop the learning rate after specific epochs during training.
When do we drop the learning rate in keras?
For example, let’s suppose our initial learning rate is . After 10 epochs we drop the learning rate to . In fact, this is the exact same learning rate schedule that is depicted in Figure 2 ( red line ). . Modeled mathematically, we can define our step-based decay equation as:
How does the decay rate of a polynomial function work?
The rate in which the learning rate is decayed is based on the parameters to the polynomial function. A smaller exponent/power to the polynomial will cause the learning rate to decay “more slowly”, whereas larger exponents decay the learning rate “more quickly”.