Adds scalar summary for the centered biases.

Change: 123889304

tensorflow/contrib/layers/python/layers/summaries.py

@@ -21,24 +21,24 @@ from __future__ import print_function
 import functools
 import re
 
+import numpy as np
+
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import standard_ops
 
-__all__ = ['summarize_tensor', 'summarize_activation', 'summarize_tensors',
+__all__ = ['assert_summary_tag_unique', 'is_summary_tag_unique',
+           'summarize_tensor', 'summarize_activation', 'summarize_tensors',
            'summarize_collection', 'summarize_variables', 'summarize_weights',
-           'summarize_biases', 'summarize_activations']
+           'summarize_biases', 'summarize_activations',]
 
 # TODO(wicke): add more unit tests for summarization functions.
 
 
-def _assert_summary_tag_unique(tag):
-  for summary in ops.get_collection(ops.GraphKeys.SUMMARIES):
-    old_tag = tensor_util.constant_value(summary.op.inputs[0])
-    if tag.encode() == old_tag:
-      raise ValueError('Conflict with summary tag: %s exists on summary %s %s' %
-                       (tag, summary, old_tag))
+def assert_summary_tag_unique(tag):
+  if not is_summary_tag_unique(tag):
+    raise ValueError('Conflict with summary tag: %s already exists' % tag)
 
 
 def _add_scalar_summary(tensor, tag=None):
@@ -56,7 +56,7 @@ def _add_scalar_summary(tensor, tag=None):
   """
   tensor.get_shape().assert_has_rank(0)
   tag = tag or tensor.op.name
-  _assert_summary_tag_unique(tag)
+  assert_summary_tag_unique(tag)
   return standard_ops.scalar_summary(tag, tensor, name='%s_summary' % tag)
 
 
@@ -74,10 +74,26 @@ def _add_histogram_summary(tensor, tag=None):
     ValueError: If the tag is already in use.
   """
   tag = tag or tensor.op.name
-  _assert_summary_tag_unique(tag)
+  assert_summary_tag_unique(tag)
   return standard_ops.histogram_summary(tag, tensor, name='%s_summary' % tag)
 
 
+def is_summary_tag_unique(tag):
+  """Checks if a summary tag is unique.
+
+  Args:
+    tag: The tag to use
+
+  Returns:
+    True if the summary tag is unique.
+  """
+  existing_tags = [tensor_util.constant_value(summary.op.inputs[0])
+                   for summary in ops.get_collection(ops.GraphKeys.SUMMARIES)]
+  existing_tags = [name.tolist() if isinstance(name, np.ndarray) else name
+                   for name in existing_tags]
+  return tag.encode() not in existing_tags
+
+
 def summarize_activation(op):
   """Summarize an activation.
 

tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py

@@ -267,7 +267,9 @@ class _DNNLinearCombinedBaseEstimator(estimator.BaseEstimator):
         collections=[self._centered_bias_weight_collection,
                      ops.GraphKeys.VARIABLES],
         name="centered_bias_weight")
-    # TODO(zakaria): Create summaries for centered_bias
+    logging_ops.scalar_summary(
+        ["centered_bias_%d" % cb for cb in range(self._num_label_columns())],
+        array_ops.reshape(centered_bias, [-1]))
     return centered_bias
 
   def _centered_bias_step(self, targets, weight_tensor):

tensorflow/contrib/learn/python/learn/graph_actions.py

@@ -35,7 +35,6 @@ from tensorflow.contrib.learn.python.learn import monitors as monitors_lib
 from tensorflow.python.client import session as tf_session
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import logging_ops
@@ -429,14 +428,8 @@ def evaluate(graph,
   global_step_tensor = contrib_variables.assert_or_get_global_step(
       graph, global_step_tensor)
 
-  # Add scalar summaries for every tensor in evaluation dict if there is not
-  # one existing already or it's a string.
-  existing_tags = [tensor_util.constant_value(summary.op.inputs[0])
-                   for summary in ops.get_collection(ops.GraphKeys.SUMMARIES)]
-  existing_tags = [name.tolist() if isinstance(name, np.ndarray) else name
-                   for name in existing_tags]
   for key, value in eval_dict.items():
-    if key.encode() in existing_tags:
+    if not summaries.is_summary_tag_unique(key):
       continue
     if isinstance(value, ops.Tensor):
       summaries.summarize_tensor(value, tag=key)