I'm using Keras 2.1.* and want to change the learning rate during training. I know about the scheduled callback, but I don't use the fit function and I don't have callbacks. I use train_on_batch. Is it possible in Keras?
If you use .fit function you can use scheduler for it like this.
def scheduler(epoch):
if epoch == 5:
model.lr.set_value(.02)
return model.lr.get_value()
change_lr = LearningRateScheduler(scheduler)
model.fit(x_embed, y, nb_epoch=1, batch_size = batch_size, show_accuracy=True,
callbacks=[chage_lr])But if you use other functions like train_on_batch, you can change the learning rate with this way
from keras import backend as K
old_lr = K.get_value(model.optimizer.lr)
new_lr = old_lr * 0.1 # change however you want
K.set_value(model.optimizer.lr, new_lr)By the way. If you use decay for learning rate, remember that it always calculating during training, and old_lr will be the learning rate you passed from beging.