Name ops and vars in head and in various places in contrib/.

Change: 140431877

1 files changed, 31 insertions, 29 deletions

diff --git a/tensorflow/contrib/losses/python/losses/loss_ops.py b/tensorflow/contrib/losses/python/losses/loss_ops.py
index 7610f9275f..8078d9f51a 100644
--- a/tensorflow/contrib/losses/python/losses/loss_ops.py
+++ b/tensorflow/contrib/losses/python/losses/loss_ops.py
@@ -144,12 +144,13 @@ def _safe_mean(losses, num_present):
 @deprecated_args(
     "2016-11-25", "`weight` is being deprecated, use `weights`.", "weight")
 def compute_weighted_loss(
-    losses, weights=_WEIGHT_SENTINEL, weight=_WEIGHT_SENTINEL):
+    losses, weights=_WEIGHT_SENTINEL, scope=None, weight=_WEIGHT_SENTINEL):
   """Computes the weighted loss.
 
   Args:
     losses: A tensor of size [batch_size, d1, ... dN].
     weights: A tensor of size [1] or [batch_size, d1, ... dK] where K < N.
+    scope: the scope for the operations performed in computing the loss.
     weight: Deprecated alias for `weights`.
 
   Returns:
@@ -161,27 +162,28 @@ def compute_weighted_loss(
       `weights` is missing.
   """
   weights = _weights(weights, weight)
-  losses = ops.convert_to_tensor(losses)
-  input_dtype = losses.dtype
-  losses = math_ops.to_float(losses)
-  weights = math_ops.to_float(ops.convert_to_tensor(weights))
+  with ops.name_scope(scope, "weighted_loss", [losses, weights]):
+    losses = ops.convert_to_tensor(losses)
+    input_dtype = losses.dtype
+    losses = math_ops.to_float(losses)
+    weights = math_ops.to_float(ops.convert_to_tensor(weights))
 
-  if losses.get_shape().ndims is None:
-    raise ValueError("losses.get_shape().ndims cannot be None")
-  weights_shape = weights.get_shape()
-  if weights_shape.ndims is None:
-    raise ValueError("weight.get_shape().ndims cannot be None")
+    if losses.get_shape().ndims is None:
+      raise ValueError("losses.get_shape().ndims cannot be None")
+    weights_shape = weights.get_shape()
+    if weights_shape.ndims is None:
+      raise ValueError("weight.get_shape().ndims cannot be None")
 
-  if weights_shape.ndims > 1 and weights_shape.dims[-1].is_compatible_with(1):
-    weights = array_ops.squeeze(weights, [-1])
+    if weights_shape.ndims > 1 and weights_shape.dims[-1].is_compatible_with(1):
+      weights = array_ops.squeeze(weights, [-1])
 
-  total_loss = _scale_losses(losses, weights)
-  num_present = _num_present(losses, weights)
-  mean_loss = _safe_mean(total_loss, num_present)
-  # convert the result back to the input type
-  mean_loss = math_ops.cast(mean_loss, input_dtype)
-  add_loss(mean_loss)
-  return mean_loss
+    total_loss = _scale_losses(losses, weights)
+    num_present = _num_present(losses, weights)
+    mean_loss = _safe_mean(total_loss, num_present)
+    # convert the result back to the input type
+    mean_loss = math_ops.cast(mean_loss, input_dtype)
+    add_loss(mean_loss)
+    return mean_loss
 
 
 def _num_present(losses, weights, per_batch=False):
@@ -334,7 +336,7 @@ def absolute_difference(
     predictions = math_ops.to_float(predictions)
     labels = math_ops.to_float(labels)
     losses = math_ops.abs(math_ops.sub(predictions, labels))
-    return compute_weighted_loss(losses, weights)
+    return compute_weighted_loss(losses, weights, scope=scope)
 
 
 @deprecated_args(
@@ -373,7 +375,7 @@ def sigmoid_cross_entropy(
   """
   weights = _weights(weights, weight)
   with ops.name_scope(scope, "sigmoid_cross_entropy_loss",
-                      [logits, multi_class_labels, weights]):
+                      [logits, multi_class_labels, weights]) as scope:
     logits.get_shape().assert_is_compatible_with(multi_class_labels.get_shape())
 
     multi_class_labels = math_ops.cast(multi_class_labels, logits.dtype)
@@ -384,7 +386,7 @@ def sigmoid_cross_entropy(
 
     losses = nn.sigmoid_cross_entropy_with_logits(logits, multi_class_labels,
                                                   name="xentropy")
-    return compute_weighted_loss(losses, weights)
+    return compute_weighted_loss(losses, weights, scope=scope)
 
 
 @deprecated_args(
@@ -421,7 +423,7 @@ def softmax_cross_entropy(
   """
   weights = _weights(weights, weight)
   with ops.name_scope(scope, "softmax_cross_entropy_loss",
-                      [logits, onehot_labels, weights]):
+                      [logits, onehot_labels, weights]) as scope:
     logits.get_shape().assert_is_compatible_with(onehot_labels.get_shape())
 
     onehot_labels = math_ops.cast(onehot_labels, logits.dtype)
@@ -435,7 +437,7 @@ def softmax_cross_entropy(
 
     losses = nn.softmax_cross_entropy_with_logits(logits, onehot_labels,
                                                   name="xentropy")
-    return compute_weighted_loss(losses, weights)
+    return compute_weighted_loss(losses, weights, scope=scope)
 
 
 @deprecated_args(
@@ -468,13 +470,13 @@ def sparse_softmax_cross_entropy(
   """
   weights = _weights(weights, weight)
   with ops.name_scope(scope, "sparse_softmax_cross_entropy_loss",
-                      [logits, labels, weights]):
+                      [logits, labels, weights]) as scope:
     labels = array_ops.reshape(labels, shape=[array_ops.shape(labels)[0]])
     weights = array_ops.squeeze(weights)
 
     losses = nn.sparse_softmax_cross_entropy_with_logits(logits, labels,
                                                          name="xentropy")
-    return compute_weighted_loss(losses, weights)
+    return compute_weighted_loss(losses, weights, scope=scope)
 
 
 @deprecated_args(
@@ -523,7 +525,7 @@ def log_loss(
         labels,
         math_ops.log(predictions + epsilon)) - math_ops.mul(
             (1 - labels), math_ops.log(1 - predictions + epsilon))
-    return compute_weighted_loss(losses, weights)
+    return compute_weighted_loss(losses, weights, scope=scope)
 
 
 @deprecated_args(
@@ -597,7 +599,7 @@ def mean_squared_error(
     predictions = math_ops.to_float(predictions)
     labels = math_ops.to_float(labels)
     losses = math_ops.square(math_ops.sub(predictions, labels))
-    return compute_weighted_loss(losses, weights)
+    return compute_weighted_loss(losses, weights, scope=scope)
 
 
 @deprecated_args(
@@ -732,4 +734,4 @@ def cosine_distance(
 
     radial_diffs = math_ops.mul(predictions, labels)
     losses = 1 - math_ops.reduce_sum(radial_diffs, reduction_indices=[dim,])
-    return compute_weighted_loss(losses, weights)
+    return compute_weighted_loss(losses, weights, scope=scope)