contrib.tensorboard

TensorBoard functions that can be used to log various status during epoch.

Classes

LogMetricsCallback(logging_dir[, prefix])

Log metrics periodically in TensorBoard.
class mxnet.contrib.tensorboard.LogMetricsCallback(logging_dir, prefix=None)[source]

Bases: 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 https://github.com/dmlc/tensorboard

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
>>> model.fit(train,
>>>     ...
>>>     batch_end_callback = batch_end_callbacks,
>>>     eval_end_callback  = eval_end_callbacks)
>>> # Then use `tensorboard --logdir=logs/` to launch TensorBoard visualization.