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Average pooling operation for spatial data.
tf.keras.layers.AveragePooling2D(
pool_size=(2, 2),
strides=None,
padding='valid',
data_format=None,
**kwargs
)
Downsamples the input along its spatial dimensions (height and width)
by taking the average value over an input window
(of size defined by pool_size
) for each channel of the input.
The window is shifted by strides
along each dimension.
The resulting output when using "valid"
padding option has a shape
(number of rows or columns) of:
output_shape = math.floor((input_shape - pool_size) / strides) + 1
(when input_shape >= pool_size
)
The resulting output shape when using the "same"
padding option is:
output_shape = math.floor((input_shape - 1) / strides) + 1
For example, for strides=(1, 1)
and padding="valid"
:
x = tf.constant([[1., 2., 3.],
[4., 5., 6.],
[7., 8., 9.]])
x = tf.reshape(x, [1, 3, 3, 1])
avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
strides=(1, 1), padding='valid')
avg_pool_2d(x)
<tf.Tensor: shape=(1, 2, 2, 1), dtype=float32, numpy=
array([[[[3.],
[4.]],
[[6.],
[7.]]]], dtype=float32)>
For example, for stride=(2, 2)
and padding="valid"
:
x = tf.constant([[1., 2., 3., 4.],
[5., 6., 7., 8.],
[9., 10., 11., 12.]])
x = tf.reshape(x, [1, 3, 4, 1])
avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
strides=(2, 2), padding='valid')
avg_pool_2d(x)
<tf.Tensor: shape=(1, 1, 2, 1), dtype=float32, numpy=
array([[[[3.5],
[5.5]]]], dtype=float32)>
For example, for strides=(1, 1)
and padding="same"
:
x = tf.constant([[1., 2., 3.],
[4., 5., 6.],
[7., 8., 9.]])
x = tf.reshape(x, [1, 3, 3, 1])
avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
strides=(1, 1), padding='same')
avg_pool_2d(x)
<tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
array([[[[3.],
[4.],
[4.5]],
[[6.],
[7.],
[7.5]],
[[7.5],
[8.5],
[9.]]]], dtype=float32)>
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Output shape | |
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