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TensorBoard for visualising batch-level metrics.
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# TensorBoard for visualising batch-level metrics | |
# Based on code from various people at | |
# https://github.com/keras-team/keras/issues/6692 | |
import tensorflow as tf | |
from keras.callbacks import TensorBoard | |
class TensorBoardBatchMonitor(TensorBoard): | |
def __init__(self, log_every=1, **kwargs): | |
super().__init__(**kwargs) | |
self.log_every = log_every | |
self.counter = 0 | |
def on_batch_end(self, batch, logs=None): | |
self.counter += 1 | |
if self.counter % self.log_every == 0: | |
for name, value in logs.items(): | |
if name in ['batch', 'size']: | |
continue | |
summary = tf.Summary() | |
summary_value = summary.value.add() | |
summary_value.simple_value = value.item() | |
summary_value.tag = name | |
self.writer.add_summary(summary, self.counter) | |
self.writer.flush() | |
def on_epoch_end(self, epoch, logs=None): | |
for name, value in logs.items(): | |
if name in ['acc', 'loss', 'batch', 'size']: | |
continue | |
summary = tf.Summary() | |
summary_value = summary.value.add() | |
summary_value.simple_value = value.item() | |
summary_value.tag = name | |
self.writer.add_summary(summary, self.counter) | |
self.writer.flush() |
@tonyreina: That's strange. on_epoch_end
shouldn't have anything to do with whether the model converges or not. Is it not converging at all, or is it taking time to converge? If you increase log_every
in the constructor, the logging will be done less frequently, and the training process will be a little faster. It might also be worth checking if on_epoch_end
is being called at all, or how often it is being called, and what the items in logs
are.
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If I include the on_epoch_end, I find the model no longer converges. If I just add pass to that code, then it converges but I don't get the validation metrics logged. Not sure what is happening.