Keras: how to output learning rate onto tensorboard - keras

Question

I added a callback to decay the learning rate:
keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=100,
verbose=0, mode='auto',epsilon=0.00002, cooldown=20, min_lr=0)
Here is my tensorboard callback:
keras.callbacks.TensorBoard(log_dir='./graph/rank{}'.format(hvd.rank()), histogram_freq=10, batch_size=FLAGS.batch_size,
write_graph=True, write_grads=True, write_images=False)
I want to make sure the learning rate scheduler has kicked in during training, so I want to output the learning rate onto tensorboard. But I can not find where I can set it.
I also checked the optimizer api, but no luck.
keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
How can I output the learning rate to tensorboad?

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):
# add other arguments to __init__ if you need
def __init__(self, log_dir, **kwargs):
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

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
)

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

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 5

For tensorflor 2.5 if you have some custom Learning Rate Scheduler:
class LearningRateLogger(tf.keras.callbacks.Callback):
def __init__(self, log_dir):
super().__init__()
self._supports_tf_logs = True
self.log_dir = log_dir
def set_model(self, model):
self.model = model
self.sess = tf.compat.v1.keras.backend.get_session()
self.writer = tf.summary.create_file_writer(self.log_dir)
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
logs.update({'learning_rate': self.model.optimizer.lr})
self._write_logs(logs, epoch)
def _write_logs(self, logs, index):
with self.writer.as_default():
for name, value in logs.items():
if name in ['batch', 'size']:
continue
if isinstance(value, np.ndarray):
tf.summary.scalar(name, value.item(), step=index)
else:
tf.summary.scalar(name, value, step=index)
self.writer.flush()
Then for calling the callback in your model.fit:
model.fit(x=..., y=..., callbacks=[LearningRateLogger(log_dir="/path/to/folder/where/tensorboard/is/logging")])