Source code for mxnet.contrib.tensorboard

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# coding: utf-8
"""TensorBoard functions that can be used to log various status during epoch."""
from __future__ import absolute_import

import logging

[docs]class LogMetricsCallback(object): """Log metrics periodically in TensorBoard. This callback works almost same as `callback.Speedometer`, but write TensorBoard event file for visualization. For more usage, please refer Parameters ---------- logging_dir : str TensorBoard event file directory. After that, use `tensorboard --logdir=path/to/logs` to launch TensorBoard visualization. prefix : str Prefix for a metric name of `scalar` value. You might want to use this param to leverage TensorBoard plot feature, where TensorBoard plots different curves in one graph when they have same `name`. The follow example shows the usage(how to compare a train and eval metric in a same graph). Examples -------- >>> # log train and eval metrics under different directories. >>> training_log = 'logs/train' >>> evaluation_log = 'logs/eval' >>> # in this case, each training and evaluation metric pairs has same name, >>> # you can add a prefix to make it separate. >>> batch_end_callbacks = [mx.contrib.tensorboard.LogMetricsCallback(training_log)] >>> eval_end_callbacks = [mx.contrib.tensorboard.LogMetricsCallback(evaluation_log)] >>> # run >>>, >>> ... >>> batch_end_callback = batch_end_callbacks, >>> eval_end_callback = eval_end_callbacks) >>> # Then use `tensorboard --logdir=logs/` to launch TensorBoard visualization. """ def __init__(self, logging_dir, prefix=None): self.prefix = prefix try: from mxboard import SummaryWriter self.summary_writer = SummaryWriter(logging_dir) except ImportError: logging.error('You can install mxboard via `pip install mxboard`.') def __call__(self, param): """Callback to log training speed and metrics in TensorBoard.""" if param.eval_metric is None: return name_value = param.eval_metric.get_name_value() for name, value in name_value: if self.prefix is not None: name = '%s-%s' % (self.prefix, name) self.summary_writer.add_scalar(name, value, global_step=param.epoch)