2 files changed, 4 insertions, 4 deletions
diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index cf13b52617..ab08865532 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -183,13 +183,13 @@ class Layer(base_layer.Layer):
       use_resource: Whether to use `ResourceVariable`.
       synchronization: Indicates when a distributed a variable will be
         aggregated. Accepted values are constants defined in the class
-        @{tf.VariableSynchronization}. By default the synchronization is set to
+        `tf.VariableSynchronization`. By default the synchronization is set to
         `AUTO` and the current `DistributionStrategy` chooses
         when to synchronize. If `synchronization` is set to `ON_READ`,
         `trainable` must not be set to `True`.
       aggregation: Indicates how a distributed variable will be aggregated.
         Accepted values are constants defined in the class
-        @{tf.VariableAggregation}.
+        `tf.VariableAggregation`.
       partitioner: (optional) partitioner instance (callable).  If
         provided, when the requested variable is created it will be split
         into multiple partitions according to `partitioner`.  In this case,
diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py
index 261281ae7e..50a56736fc 100644
--- a/tensorflow/python/layers/core.py
+++ b/tensorflow/python/layers/core.py
@@ -203,7 +203,7 @@ class Dropout(keras_layers.Dropout, base.Layer):
       to be the same for all timesteps, you can use
       `noise_shape=[batch_size, 1, features]`.
     seed: A Python integer. Used to create random seeds. See
-      @{tf.set_random_seed}.
+      `tf.set_random_seed`.
       for behavior.
     name: The name of the layer (string).
   """
@@ -248,7 +248,7 @@ def dropout(inputs,
       to be the same for all timesteps, you can use
       `noise_shape=[batch_size, 1, features]`.
     seed: A Python integer. Used to create random seeds. See
-      @{tf.set_random_seed}
+      `tf.set_random_seed`
       for behavior.
     training: Either a Python boolean, or a TensorFlow boolean scalar tensor
       (e.g. a placeholder). Whether to return the output in training mode