CLN: remove unnecessary math_ops.maximum

5 files changed, 11 insertions, 17 deletions

diff --git a/tensorflow/contrib/losses/python/losses/loss_ops.py b/tensorflow/contrib/losses/python/losses/loss_ops.py
index 66322140cb..7e5ab05987 100644
--- a/tensorflow/contrib/losses/python/losses/loss_ops.py
+++ b/tensorflow/contrib/losses/python/losses/loss_ops.py
@@ -78,9 +78,7 @@ def _safe_mean(losses, num_present):
       then zero is returned.
   """
   total_loss = math_ops.reduce_sum(losses)
-  return math_ops.div_no_nan(total_loss,
-                             math_ops.maximum(num_present, 0),
-                             name="value")
+  return math_ops.div_no_nan(total_loss, num_present, name="value")
 
 
 @deprecated("2016-12-30", "Use tf.losses.compute_weighted_loss instead.")
@@ -585,10 +583,9 @@ def mean_pairwise_squared_error(predictions,
         math_ops.square(diffs), reduction_indices=reduction_indices)
     num_present_per_batch = _num_present(diffs, weights, per_batch=True)
 
-    term1 = 2.0 * math_ops.div_no_nan(
-        sum_squares_diff_per_batch,
-        math_ops.maximum(num_present_per_batch, 0),
-        name="value")
+    term1 = 2.0 * math_ops.div_no_nan(sum_squares_diff_per_batch,
+                                      num_present_per_batch,
+                                      name="value")
 
     sum_diff = math_ops.reduce_sum(diffs, reduction_indices=reduction_indices)
     term2 = 2.0 * math_ops.div_no_nan(math_ops.square(sum_diff),
diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py
index d7c73c8f99..91939b5bf2 100644
--- a/tensorflow/contrib/metrics/python/ops/metric_ops.py
+++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py
@@ -3222,11 +3222,11 @@ def streaming_covariance(predictions,
     # batch_mean_prediction is E[x_B] in the update equation
     batch_mean_prediction = math_ops.div_no_nan(
         math_ops.reduce_sum(weighted_predictions),
-        math_ops.maximum(batch_count, 0),
+        batch_count,
         name='batch_mean_prediction')
     delta_mean_prediction = math_ops.div_no_nan(
         (batch_mean_prediction - mean_prediction) * batch_count,
-        math_ops.maximum(update_count, 0),
+        update_count,
         name='delta_mean_prediction')
     update_mean_prediction = state_ops.assign_add(mean_prediction,
                                                   delta_mean_prediction)
@@ -3236,11 +3236,11 @@ def streaming_covariance(predictions,
     # batch_mean_label is E[y_B] in the update equation
     batch_mean_label = math_ops.div_no_nan(
         math_ops.reduce_sum(weighted_labels),
-        math_ops.maximum(batch_count, 0),
+        batch_count,
         name='batch_mean_label')
     delta_mean_label = math_ops.div_no_nan(
         (batch_mean_label - mean_label) * batch_count,
-        math_ops.maximum(update_count, 0),
+        update_count,
         name='delta_mean_label')
     update_mean_label = state_ops.assign_add(mean_label, delta_mean_label)
     # prev_mean_label is E[y_A] in the update equation
diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py
index 9082b9f0fa..c23168ccef 100644
--- a/tensorflow/python/keras/engine/training_utils.py
+++ b/tensorflow/python/keras/engine/training_utils.py
@@ -613,8 +613,7 @@ def weighted_masked_objective(fn):
       score_array = math_ops.multiply(score_array, weights)
       score_array = math_ops.reduce_sum(score_array)
       weights = math_ops.reduce_sum(weights)
-      score_array = math_ops.div_no_nan(score_array,
-                                        math_ops.maximum(weights, 0))
+      score_array = math_ops.div_no_nan(score_array, weights)
     return K.mean(score_array)
 
   return weighted
diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py
index 4050eb95a4..f85b6554bd 100644
--- a/tensorflow/python/keras/metrics.py
+++ b/tensorflow/python/keras/metrics.py
@@ -488,7 +488,7 @@ class Mean(Metric):
     state_ops.assign_add(self.count, num_values)
 
   def result(self):
-    return math_ops.div_no_nan(self.total, math_ops.maximum(self.count, 0))
+    return math_ops.div_no_nan(self.total, self.count)
 
 
 class MeanMetricWrapper(Mean):
diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py
index 2035aaf9fe..fe4950a475 100644
--- a/tensorflow/python/ops/losses/losses_impl.py
+++ b/tensorflow/python/ops/losses/losses_impl.py
@@ -86,9 +86,7 @@ def _safe_mean(losses, num_present):
       then zero is returned.
   """
   total_loss = math_ops.reduce_sum(losses)
-  return math_ops.div_no_nan(total_loss,
-                             math_ops.maximum(num_present, 0),
-                             name="value")
+  return math_ops.div_no_nan(total_loss, num_present, name="value")
 
 
 def _num_present(losses, weights, per_batch=False):