I was trying to replicate the same result of resent 110 in cifar 10 as in Deep Residual Learning for Image Recognition 2. It says something like this for resnet having less than 110 layers

We start with a learning rate of 0.1, divide it by 10 at 32k and 48k iterations, and terminate training at 64k iterations, which is determined on a 45k/5k train/val split.

and for a resnet with 110 layers it says

In this case, we find that the initial learning rate of 0.1 is slightly too large to start converging. So we use 0.01 to warm up the training until the training error is below 80% (about 400 iterations), and then go back to 0.1 and con- tinue training. The rest of the learning schedule is as done previously.

How can I change learning rate according to the error ??