def _build_summary_op(self, results=None, features=None, labels=None):
"""Builds summaries for this model.
The summaries are one value (or more) of:
* (`ACTIVATIONS`, `VARIABLES`, `GRADIENTS`, `LOSS`, `LEARNING_RATE`)
"""
summary_op = []
for summary in self.summaries:
if summary == summarizer.SummaryOptions.ACTIVATIONS:
activations = get_tracked(tf.GraphKeys.ACTIVATIONS)
summary_op += summarizer.add_activations_summary(activations)
elif summary == summarizer.SummaryOptions.VARIABLES:
variables = tf.trainable_variables()
summary_op += summarizer.add_trainable_vars_summary(variables)
elif summary == summarizer.SummaryOptions.GRADIENTS and self._clip_gradients > 0.0:
summary_op += summarizer.add_gradients_summary(
self._grads_and_vars)
elif summary == summarizer.SummaryOptions.LOSS:
summary_op += summarizer.add_loss_summaries(
self._total_loss, self._loss)
elif summary == summarizer.SummaryOptions.LEARNING_RATE:
summary_op += summarizer.add_learning_rate_summaries()
elif summary == summarizer.SummaryOptions.IMAGE_INPUT:
summary_op += summarizer.add_image_summary(features,
op_name='inputs')
elif summary == summarizer.SummaryOptions.IMAGE_RESULT:
summary_op += summarizer.add_image_summary(results,
op_name='results')
# no need to tf.summary.merge(summary_op), for now we merge all at hook level
return summary_op
def _build_summary_op(self, results=None, features=None, labels=None):
"""Builds summaries for this model.
The summaries are one value (or more) of:
* (`ACTIVATIONS`, `VARIABLES`, `GRADIENTS`, `LOSS`, `LEARNING_RATE`)
"""
summary_op = []
for summary in self.summaries:
if summary == summarizer.SummaryOptions.ACTIVATIONS:
activations = get_tracked(tf.GraphKeys.ACTIVATIONS)
summary_op += summarizer.add_activations_summary(activations)
elif summary == summarizer.SummaryOptions.VARIABLES:
variables = tf.trainable_variables()
summary_op += summarizer.add_trainable_vars_summary(variables)
elif summary == summarizer.SummaryOptions.GRADIENTS and self._clip_gradients > 0.0:
summary_op += summarizer.add_gradients_summary(self._grads_and_vars)
elif summary == summarizer.SummaryOptions.LOSS:
summary_op += summarizer.add_loss_summaries(self._total_loss, self._loss)
elif summary == summarizer.SummaryOptions.LEARNING_RATE:
summary_op += summarizer.add_learning_rate_summaries()
elif summary == summarizer.SummaryOptions.IMAGE_INPUT:
summary_op += summarizer.add_image_summary(features, op_name='inputs')
elif summary == summarizer.SummaryOptions.IMAGE_RESULT:
summary_op += summarizer.add_image_summary(results, op_name='results')
# no need to tf.summary.merge(summary_op), for now we merge all at hook level
return summary_op