How to provide learning rate value to tensorboard in keras

Question

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])

Answer 1

I'm not sure how to pass it to Tensorboard, but you can monitor it from python.

from keras.callbacks import Callback
class LossHistory(Callback):
    def on_train_begin(self, logs={}):
        self.losses = []
        self.lr = []

    def on_epoch_end(self, batch, logs={}):
        self.losses.append(logs.get('loss'))
        self.lr.append(initial_lr * 0.95 ** len(self.losses))

loss_hist = LossHistory()

Then just add loss_hist to your callbacks.

Update:

Based on this answer:

class LRTensorBoard(TensorBoard):

    def __init__(self, log_dir='./logs', **kwargs):
        super(LRTensorBoard, self).__init__(log_dir, **kwargs)

        self.lr_log_dir = log_dir

    def set_model(self, model):
        self.lr_writer = tf.summary.FileWriter(self.lr_log_dir)
        super(LRTensorBoard, self).set_model(model)

    def on_epoch_end(self, epoch, logs=None):
        lr = initial_lr * 0.95 ** epoch

        summary = tf.Summary(value=[tf.Summary.Value(tag='lr',
                                                     simple_value=lr)])
        self.lr_writer.add_summary(summary, epoch)
        self.lr_writer.flush()

        super(LRTensorBoard, self).on_epoch_end(epoch, logs)

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

Just use it like the normal TensorBoard.