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")])

Answer 2

class XTensorBoard(TensorBoard):
    def on_epoch_begin(self, epoch, logs=None):
        # get values
        lr = float(K.get_value(self.model.optimizer.lr))
        decay = float(K.get_value(self.model.optimizer.decay))
        # computer lr
        lr = lr * (1. / (1 + decay * epoch))
        K.set_value(self.model.optimizer.lr, lr)

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

callbacks_list = [XTensorBoard('./logs')]
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=20, batch_size=32, verbose=2, callbacks=callbacks_list)

lr curve in tensorboard

Answer 3

You gave the optimizer's code twice, instead of TensorBoard Callback. Anyway, I didn`t find the way to display the learning rate on TensorBoard. I am plotting it after the training finished, taking data from History object:

nb_epoch = len(history1.history['loss'])
learning_rate=history1.history['lr']
xc=range(nb_epoch)
plt.figure(3,figsize=(7,5))
plt.plot(xc,learning_rate)
plt.xlabel('num of Epochs')
plt.ylabel('learning rate')
plt.title('Learning rate')
plt.grid(True)
plt.style.use(['seaborn-ticks'])

The chart looks like this: LR plot

Sorry, that is not exactly what you are asking about, but perhaps could help.

Answer 4

Note that with the current nightly version of tf (2.5 - probably earlier) learning rates using LearningRateSchedule are automatically added to tensorboard's logs. The following solution is only necessary if you're adapting the learning rate some other way - e.g. via ReduceLROnPlateau or LearningRateScheduler (different to LearningRateSchedule) callbacks.

While extending tf.keras.callbacks.TensorBoard is a viable option, I prefer composition over subclassing.

class LearningRateLogger(tf.keras.callbacks.Callback):
    def __init__(self):
        super().__init__()
        self._supports_tf_logs = True

    def on_epoch_end(self, epoch, logs=None):
        if logs is None or "learning_rate" in logs:
            return
        logs["learning_rate"] = self.model.optimizer.lr

This allows us to compose multiple similar callbacks, and use the logged learning rate in multiple other callbacks (e.g. if you add a CSVLogger it should also write the learning rate values to file).

Then in model.fit

model.fit(
    callbacks=[
        LearningRateLogger(),
        # other callbacks that update `logs`
        tf.keras.callbacks.TensorBoard(path),
        # other callbacks that use updated logs, e.g. CSVLogger
    ],
    **kwargs
)