1 files changed, 10 insertions, 6 deletions
diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py
index 431ea1186a..da037a7983 100644
--- a/tensorflow/python/ops/nn_impl.py
+++ b/tensorflow/python/ops/nn_impl.py
@@ -32,6 +32,8 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import sparse_ops
 from tensorflow.python.ops import variables
+from tensorflow.python.util.deprecation import deprecated_args
+from tensorflow.python.util.deprecation import deprecated_argument_lookup
 
 
 def log_poisson_loss(targets, log_input, compute_full_loss=False, name=None):
@@ -313,19 +315,20 @@ def swish(features):
   return features * math_ops.sigmoid(features)
 
 
-def l2_normalize(x, dim, epsilon=1e-12, name=None):
-  """Normalizes along dimension `dim` using an L2 norm.
+@deprecated_args(None, "dim is deprecated, use axis instead", "dim")
+def l2_normalize(x, axis=None, epsilon=1e-12, name=None, dim=None):
+  """Normalizes along dimension `axis` using an L2 norm.
 
-  For a 1-D tensor with `dim = 0`, computes
+  For a 1-D tensor with `axis = 0`, computes
 
       output = x / sqrt(max(sum(x**2), epsilon))
 
   For `x` with more dimensions, independently normalizes each 1-D slice along
-  dimension `dim`.
+  dimension `axis`.
 
   Args:
     x: A `Tensor`.
-    dim: Dimension along which to normalize.  A scalar or a vector of
+    axis: Dimension along which to normalize.  A scalar or a vector of
       integers.
     epsilon: A lower bound value for the norm. Will use `sqrt(epsilon)` as the
       divisor if `norm < sqrt(epsilon)`.
@@ -335,8 +338,9 @@ def l2_normalize(x, dim, epsilon=1e-12, name=None):
     A `Tensor` with the same shape as `x`.
   """
   with ops.name_scope(name, "l2_normalize", [x]) as name:
+    axis = deprecated_argument_lookup("axis", axis, "dim", dim)
     x = ops.convert_to_tensor(x, name="x")
-    square_sum = math_ops.reduce_sum(math_ops.square(x), dim, keep_dims=True)
+    square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keep_dims=True)
     x_inv_norm = math_ops.rsqrt(math_ops.maximum(square_sum, epsilon))
     return math_ops.multiply(x, x_inv_norm, name=name)