Global max pooling operation for spatial data.
Inherits From: Layer
, Module
tf.keras.layers.GlobalMaxPool2D(
data_format=None, keepdims=False, **kwargs
)
Examples:
input_shape = (2, 4, 5, 3)
x = tf.random.normal(input_shape)
y = tf.keras.layers.GlobalMaxPool2D()(x)
print(y.shape)
(2, 3)
Args |
data_format
|
A string,
one of channels_last (default) or channels_first .
The ordering of the dimensions in the inputs.
channels_last corresponds to inputs with shape
(batch, height, width, channels) while channels_first
corresponds to inputs with shape
(batch, channels, height, width) .
It defaults to the image_data_format value found in your
Keras config file at ~/.keras/keras.json .
If you never set it, then it will be "channels_last".
|
keepdims
|
A boolean, whether to keep the spatial dimensions or not.
If keepdims is False (default), the rank of the tensor is reduced
for spatial dimensions.
If keepdims is True , the spatial dimensions are retained with
length 1.
The behavior is the same as for tf.reduce_max or np.max .
|
|
- If
data_format='channels_last' :
4D tensor with shape (batch_size, rows, cols, channels) .
- If
data_format='channels_first' :
4D tensor with shape (batch_size, channels, rows, cols) .
|
Output shape |
- If
keepdims =False:
2D tensor with shape (batch_size, channels) .
- If
keepdims =True:
- If
data_format='channels_last' :
4D tensor with shape (batch_size, 1, 1, channels)
- If
data_format='channels_first' :
4D tensor with shape (batch_size, channels, 1, 1)
|