nswamy commented on a change in pull request #14685: [Fit API] improve event
handlers
URL: https://github.com/apache/incubator-mxnet/pull/14685#discussion_r277221859
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File path: python/mxnet/gluon/contrib/estimator/event_handler.py
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@@ -16,85 +16,169 @@
# under the License.
# coding: utf-8
-# pylint: disable=wildcard-import
+# pylint: disable=wildcard-import, unused-argument
"""Gluon EventHandlers for Estimators"""
-__all__ = ['EventHandler', 'LoggingHandler']
import logging
import os
import time
import warnings
import numpy as np
+from ....metric import EvalMetric, Loss
-class EventHandler(object):
- """Basic for event handlers
- :py:class:`EventHandler` can perform user defined functions at
- different stages of training: train begin, epoch begin, batch begin,
- batch end, epoch end, train end.
-
- Parameters
- ----------
- estimator : Estimator
- The :py:class:`Estimator` to get training statistics
- """
+class TrainBegin(object):
+ def train_begin(self, estimator, *args, **kwargs):
+ pass
- def __init__(self):
- self._estimator = None
- @property
- def estimator(self):
- return self._estimator
+class TrainEnd(object):
+ def train_end(self, estimator, *args, **kwargs):
+ pass
- @estimator.setter
- def estimator(self, estimator):
- self._estimator = estimator
- def train_begin(self):
+class EpochBegin(object):
+ def epoch_begin(self, estimator, *args, **kwargs):
pass
- def train_end(self):
- pass
- def batch_begin(self):
- pass
+class EpochEnd(object):
+ def epoch_end(self, estimator, *args, **kwargs):
+ return False
- def batch_end(self):
- pass
- def epoch_begin(self):
+class BatchBegin(object):
+ def batch_begin(self, estimator, *args, **kwargs):
pass
- def epoch_end(self):
- pass
+class BatchEnd(object):
+ def batch_end(self, estimator, *args, **kwargs):
+ return False
+
+
+class MetricHandler(EpochBegin, BatchEnd):
+ """Metric Handler that update metric values at batch end
+
+ :py:class:`MetricHandler` takes model predictions and true labels
+ and update the metrics, it also update metric wrapper for loss with loss
values
+ Validation loss and metrics will be handled by
:py:class:`ValidationHandler`
+
+ Parameters
+ ----------
+ train_metrics : List of EvalMetrics
+ training metrics to be updated at batch end
+ """
+
+ def __init__(self, train_metrics):
+ self.train_metrics = train_metrics or []
+ # order to be called among all callbacks
+ # metrics need to be calculated before other callbacks can access them
+ self.priority = -np.Inf
Review comment:
lets call this out explicitly in the documentation.
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