Fix keras API docs.
PiperOrigin-RevId: 290392438
Change-Id: If70b8787e09a92c2ab63742d00db1599e43bcb9c
diff --git a/tensorflow/python/keras/layers/merge.py b/tensorflow/python/keras/layers/merge.py
index 1deae97..0ea700a 100644
--- a/tensorflow/python/keras/layers/merge.py
+++ b/tensorflow/python/keras/layers/merge.py
@@ -225,18 +225,24 @@
Examples:
- ```python
- import keras
+ >>> input_shape = (2, 3, 4)
+ >>> x1 = tf.random.normal(input_shape)
+ >>> x2 = tf.random.normal(input_shape)
+ >>> y = tf.keras.layers.Add()([x1, x2])
+ >>> print(y.shape)
+ (2, 3, 4)
- input1 = keras.layers.Input(shape=(16,))
- x1 = keras.layers.Dense(8, activation='relu')(input1)
- input2 = keras.layers.Input(shape=(32,))
- x2 = keras.layers.Dense(8, activation='relu')(input2)
- # equivalent to `added = keras.layers.add([x1, x2])`
- added = keras.layers.Add()([x1, x2])
- out = keras.layers.Dense(4)(added)
- model = keras.models.Model(inputs=[input1, input2], outputs=out)
- ```
+ Used in a functional model:
+
+ >>> input1 = tf.keras.layers.Input(shape=(16,))
+ >>> x1 = tf.keras.layers.Dense(8, activation='relu')(input1)
+ >>> input2 = tf.keras.layers.Input(shape=(32,))
+ >>> x2 = tf.keras.layers.Dense(8, activation='relu')(input2)
+ >>> # equivalent to `added = tf.keras.layers.add([x1, x2])`
+ >>> added = tf.keras.layers.Add()([x1, x2])
+ >>> out = tf.keras.layers.Dense(4)(added)
+ >>> model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)
+
"""
def _merge_function(self, inputs):
@@ -592,29 +598,34 @@
@keras_export('keras.layers.add')
def add(inputs, **kwargs):
- """Functional interface to the `Add` layer.
+ """Functional interface to the `tf.keras.layers.Add` layer.
Arguments:
- inputs: A list of input tensors (at least 2).
+ inputs: A list of input tensors (at least 2) with the same shape.
**kwargs: Standard layer keyword arguments.
Returns:
- A tensor, the sum of the inputs.
+ A tensor as the sum of the inputs. It has the same shape as the inputs.
Examples:
- ```python
- import keras
+ >>> input_shape = (2, 3, 4)
+ >>> x1 = tf.random.normal(input_shape)
+ >>> x2 = tf.random.normal(input_shape)
+ >>> y = tf.keras.layers.add([x1, x2])
+ >>> print(y.shape)
+ (2, 3, 4)
- input1 = keras.layers.Input(shape=(16,))
- x1 = keras.layers.Dense(8, activation='relu')(input1)
- input2 = keras.layers.Input(shape=(32,))
- x2 = keras.layers.Dense(8, activation='relu')(input2)
- added = keras.layers.add([x1, x2])
+ Used in a functiona model:
- out = keras.layers.Dense(4)(added)
- model = keras.models.Model(inputs=[input1, input2], outputs=out)
- ```
+ input1 = tf.keras.layers.Input(shape=(16,))
+ x1 = tf.keras.layers.Dense(8, activation='relu')(input1)
+ input2 = tf.keras.layers.Input(shape=(32,))
+ x2 = tf.keras.layers.Dense(8, activation='relu')(input2)
+ added = tf.keras.layers.add([x1, x2])
+ out = tf.keras.layers.Dense(4)(added)
+ model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)
+
"""
return Add(**kwargs)(inputs)
diff --git a/tensorflow/python/keras/layers/pooling.py b/tensorflow/python/keras/layers/pooling.py
index b429328..aab56bd 100644
--- a/tensorflow/python/keras/layers/pooling.py
+++ b/tensorflow/python/keras/layers/pooling.py
@@ -719,6 +719,14 @@
class GlobalAveragePooling1D(GlobalPooling1D):
"""Global average pooling operation for temporal data.
+ Examples:
+
+ >>> input_shape = (2, 3, 4)
+ >>> x = tf.random.normal(input_shape)
+ >>> y = tf.keras.layers.GlobalAveragePooling1D()(x)
+ >>> print(y.shape)
+ (2, 4)
+
Arguments:
data_format: A string,
one of `channels_last` (default) or `channels_first`.
@@ -827,6 +835,14 @@
class GlobalAveragePooling2D(GlobalPooling2D):
"""Global average pooling operation for spatial data.
+ Examples:
+
+ >>> input_shape = (2, 4, 5, 3)
+ >>> x = tf.random.normal(input_shape)
+ >>> y = tf.keras.layers.GlobalAveragePooling2D()(x)
+ >>> print(y.shape)
+ (2, 3)
+
Arguments:
data_format: A string,
one of `channels_last` (default) or `channels_first`.
@@ -860,6 +876,14 @@
class GlobalMaxPooling2D(GlobalPooling2D):
"""Global max pooling operation for spatial data.
+ Examples:
+
+ >>> input_shape = (2, 4, 5, 3)
+ >>> x = tf.random.normal(input_shape)
+ >>> y = tf.keras.layers.GlobalMaxPool2D()(x)
+ >>> print(y.shape)
+ (2, 3)
+
Arguments:
data_format: A string,
one of `channels_last` (default) or `channels_first`.
