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-rw-r--r--tensorflow/cc/BUILD1
-rw-r--r--tensorflow/python/kernel_tests/relu_op_test.py4
-rw-r--r--tensorflow/python/ops/nn_ops.py2
3 files changed, 4 insertions, 3 deletions
diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index f56521dac0..e99d15f85d 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -410,6 +410,7 @@ tf_cc_test(
srcs = ["gradients/nn_grad_test.cc"],
deps = [
":cc_ops",
+ ":cc_ops_internal",
":grad_op_registry",
":grad_testutil",
":gradient_checker",
diff --git a/tensorflow/python/kernel_tests/relu_op_test.py b/tensorflow/python/kernel_tests/relu_op_test.py
index 86d9c90e83..d97a1613b9 100644
--- a/tensorflow/python/kernel_tests/relu_op_test.py
+++ b/tensorflow/python/kernel_tests/relu_op_test.py
@@ -351,7 +351,7 @@ class LeakyReluTest(test.TestCase):
self.assertLess(err, 1e-10)
def testGradGradFloat32(self):
- with compat.forward_compatibility_horizon(2018, 10, 2):
+ with compat.forward_compatibility_horizon(2018, 11, 2):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
@@ -369,7 +369,7 @@ class LeakyReluTest(test.TestCase):
self.assertLess(err, 1e-4)
def testGradGradFloat64(self):
- with compat.forward_compatibility_horizon(2018, 10, 2):
+ with compat.forward_compatibility_horizon(2018, 11, 2):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py
index d646245ce3..2861f40586 100644
--- a/tensorflow/python/ops/nn_ops.py
+++ b/tensorflow/python/ops/nn_ops.py
@@ -1601,7 +1601,7 @@ def leaky_relu(features, alpha=0.2, name=None):
features = ops.convert_to_tensor(features, name="features")
if features.dtype.is_integer:
features = math_ops.to_float(features)
- if compat.forward_compatible(2018, 10, 1):
+ if compat.forward_compatible(2018, 11, 1):
return gen_nn_ops.leaky_relu(features, alpha=alpha, name=name)
alpha = ops.convert_to_tensor(alpha, dtype=features.dtype, name="alpha")
return math_ops.maximum(alpha * features, features, name=name)