GlobalAveragePooling2D layer
- 원본 링크 : https://keras.io/api/layers/pooling_layers/global_average_pooling2d/
- 최종 확인 : 2024-11-24
GlobalAveragePooling2D
class
keras.layers.GlobalAveragePooling2D(data_format=None, keepdims=False, **kwargs)
Global average pooling operation for 2D data.
Arguments
- data_format: string, either
"channels_last"
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, features, height, weight)
. It defaults to theimage_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 temporal dimension or not. If
keepdims
isFalse
(default), the rank of the tensor is reduced for spatial dimensions. Ifkeepdims
isTrue
, the spatial dimension are retained with length 1. The behavior is the same as fortf.reduce_mean
ornp.mean
.
Input shape
- If
data_format='channels_last'
: 4D tensor with shape:(batch_size, height, width, channels)
- If
data_format='channels_first'
: 4D tensor with shape:(batch_size, channels, height, width)
Output shape
- If
keepdims=False
: 2D tensor with shape(batch_size, channels)
. - If
keepdims=True
: - Ifdata_format="channels_last"
: 4D tensor with shape(batch_size, 1, 1, channels)
- Ifdata_format="channels_first"
: 4D tensor with shape(batch_size, channels, 1, 1)
Example
>>> x = np.random.rand(2, 4, 5, 3)
>>> y = keras.layers.GlobalAveragePooling2D()(x)
>>> y.shape
(2, 3)