Merge pull request #29435 from lufol:lukas-fix-4

PiperOrigin-RevId: 256370188

tensorflow/python/keras/activations.py

@@ -12,8 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Built-in activation functions.
-"""
+"""Built-in activation functions."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -287,17 +286,14 @@ def linear(x):
 
   Returns:
       The linear activation: `x`.
-
-  Note:
-      Often used as last layer of regression networks.
   """
   return x
 
 
 @keras_export('keras.activations.serialize')
 def serialize(activation):
-  if (hasattr(activation, '__name__')
-      and activation.__name__ in _TF_ACTIVATIONS_V2):
+  if (hasattr(activation, '__name__') and
+      activation.__name__ in _TF_ACTIVATIONS_V2):
     return _TF_ACTIVATIONS_V2[activation.__name__]
   return serialize_keras_object(activation)
 
@@ -322,8 +318,7 @@ def get(identifier):
     return identifier
   elif isinstance(identifier, dict):
     return deserialize_keras_object(
-        identifier,
-        printable_module_name='activation')
+        identifier, printable_module_name='activation')
   else:
     raise TypeError(
         'Could not interpret activation function identifier: {}'.format(

tensorflow/python/keras/backend.py

@@ -2284,7 +2284,19 @@ def maximum(x, y):
       y: Tensor or variable.
 
   Returns:
-      A tensor.
+      A tensor with the element wise maximum value(s) of `x` and `y`.
+
+  Examples:
+  ```python
+      # maximum of two tensors
+      >>> x = tf.Variable([[1, 2], [3, 4]])
+      >>> y = tf.Variable([[2, 1], [0, -1]])
+      >>> m = tf.keras.backend.maximum(x, y)
+      >>> m
+      <tf.Tensor: id=42, shape=(2, 2), dtype=int32, numpy=
+      array([[2, 2],
+             [3, 4]], dtype=int32)>
+  ```
   """
   return math_ops.maximum(x, y)