How to provide learning rate value to tensorboard in keras

user_5165781 0
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I'm using keras and want to implement custom learning rate via keras.callbacks.LearningRateScheduler

How can I pass learning rate to be able to monitor it in tensorboard ? (keras.callbacks.TensorBoard)

Currently I have:

lrate = LearningRateScheduler(lambda epoch: initial_lr * 0.95 ** epoch)

tensorboard = TensorBoard(log_dir=LOGDIR, histogram_freq=1,
                          batch_size=batch_size, embeddings_freq=1,
                          embeddings_layer_names=embedding_layer_names )

model.fit_generator(train_generator, steps_per_epoch=n_steps,
                    epochs=n_epochs,
                    validation_data=(val_x, val_y),
                    callbacks=[lrate, tensorboard])
tensorboard keras tensorflow python
2 Answers

TensorFlow 2.x:

Creating LearningRateScheduler logs for TensorBoard can be done with the following:

from tensorflow.keras.callbacks import LearningRateScheduler, TensorBoard

# Define your scheduling function
def scheduler(epoch):
  return return 0.001 * 0.95 ** epoch

# Define scheduler
lr_scheduler = LearningRateScheduler(scheduler)
# Alternatively, use an anonymous function
# lr_scheduler = LearningRateScheduler(lambda epoch: initial_lr * 0.95 ** epoch)

# Define TensorBoard callback child class
class LRTensorBoard(TensorBoard):

  def __init__(self, log_dir, **kwargs):
    super().__init__(log_dir, **kwargs)
    self.lr_writer = tf.summary.create_file_writer(self.log_dir + '/learning')

  def on_epoch_end(self, epoch, logs=None):
    lr = getattr(self.model.optimizer, 'lr', None)
    with self.lr_writer.as_default():
      summary = tf.summary.scalar('learning_rate', lr, epoch)

    super().on_epoch_end(epoch, logs)

  def on_train_end(self, logs=None):
    super().on_train_end(logs)
    self.lr_writer.close()

# Create callback object
tensorboard_callback = LRTensorBoard(log_dir='./logs/', histogram_freq=1)

# Compile the model
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
r = model.fit(X_train, y_train,
              validation_data=(X_val, y_val),
              epochs=25, batch_size=200,
              callbacks=[tensorboard_callback, lr_scheduler])

The learning rate can then be viewed in TensorBoard via

# Load the TensorBoard notebook extension
%load_ext tensorboard
#Start TensorBoard
%tensorboard --logdir ./logs

Learning rate logging in TensorBoard

user_37416240
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