Keras: how to output learning rate onto tensorboard

Question

I add a callback to decay learning rate:

 keras.callbacks.ReduceLROnPlateau(monitor=\'val_loss\', factor=0.5, patience=100, 
                                   v         


        

Answer 1

According to the author of Keras, the proper way is to subclass the TensorBoard callback:

from keras import backend as K
from keras.callbacks import TensorBoard

class LRTensorBoard(TensorBoard):
    def __init__(self, log_dir, **kwargs):  # add other arguments to __init__ if you need
        super().__init__(log_dir=log_dir, **kwargs)

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        logs.update({'lr': K.eval(self.model.optimizer.lr)})
        super().on_epoch_end(epoch, logs)

Then pass it as part of the callbacks argument to model.fit (credit Finncent Price):

model.fit(x=..., y=..., callbacks=[LRTensorBoard(log_dir="/tmp/tb_log")])