nn_impl.py cleanup: used keepdims instead of deprecated keep_dims.

PiperOrigin-RevId: 177972555

1 files changed, 8 insertions, 8 deletions

diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py
index 18bf5897cf..19a86df6a9 100644
--- a/tensorflow/python/ops/nn_impl.py
+++ b/tensorflow/python/ops/nn_impl.py
@@ -341,7 +341,7 @@ def l2_normalize(x, axis=None, epsilon=1e-12, name=None, dim=None):
   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), axis, keep_dims=True)
+    square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keepdims=True)
     x_inv_norm = math_ops.rsqrt(math_ops.maximum(square_sum, epsilon))
     return math_ops.multiply(x, x_inv_norm, name=name)
 
@@ -593,8 +593,8 @@ def sufficient_statistics(x, axes, shift=None, keep_dims=False, name=None):
     else:  # no shift.
       m_ss = x
       v_ss = math_ops.square(x)
-    m_ss = math_ops.reduce_sum(m_ss, axes, keep_dims=keep_dims, name="mean_ss")
-    v_ss = math_ops.reduce_sum(v_ss, axes, keep_dims=keep_dims, name="var_ss")
+    m_ss = math_ops.reduce_sum(m_ss, axes, keepdims=keep_dims, name="mean_ss")
+    v_ss = math_ops.reduce_sum(v_ss, axes, keepdims=keep_dims, name="var_ss")
   return counts, m_ss, v_ss, shift
 
 
@@ -664,12 +664,12 @@ def moments(x, axes,
     # on 32-bit floats before converting the mean and variance back to fp16
     y = math_ops.cast(x, dtypes.float32) if x.dtype == dtypes.float16 else x
     # Compute true mean while keeping the dims for proper broadcasting.
-    mean = math_ops.reduce_mean(y, axes, keep_dims=True, name="mean")
+    mean = math_ops.reduce_mean(y, axes, keepdims=True, name="mean")
     # sample variance, not unbiased variance
     variance = math_ops.reduce_mean(
         math_ops.squared_difference(y, array_ops.stop_gradient(mean)),
         axes,
-        keep_dims=True,
+        keepdims=True,
         name="variance")
     if not keep_dims:
       mean = array_ops.squeeze(mean, axes)
@@ -714,7 +714,7 @@ def weighted_moments(x, axes, frequency_weights, name=None, keep_dims=False):
     # Note that we use keep_dims=True for our reductions regardless of the arg;
     # this is so that the results remain broadcast-compatible with the inputs.
     weighted_input_sum = math_ops.reduce_sum(
-        frequency_weights * x, axes, name="weighted_input_sum", keep_dims=True)
+        frequency_weights * x, axes, name="weighted_input_sum", keepdims=True)
 
     # The shape of the weights isn't necessarily the same as x's
     # shape, just broadcast-compatible with it -- so this expression
@@ -725,7 +725,7 @@ def weighted_moments(x, axes, frequency_weights, name=None, keep_dims=False):
     broadcasted_weights = frequency_weights + array_ops.zeros_like(x)
 
     sum_of_weights = math_ops.reduce_sum(
-        broadcasted_weights, axes, name="sum_of_weights", keep_dims=True)
+        broadcasted_weights, axes, name="sum_of_weights", keepdims=True)
 
     divisor = math_ops.reciprocal(sum_of_weights, name="inv_weight_sum")
 
@@ -736,7 +736,7 @@ def weighted_moments(x, axes, frequency_weights, name=None, keep_dims=False):
         frequency_weights * math_ops.squared_difference(x, weighted_mean),
         axes,
         name="weighted_distsq",
-        keep_dims=True)
+        keepdims=True)
 
     weighted_variance = math_ops.multiply(weighted_distsq, divisor)