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Max pooling operation for 2D spatial data.
tf.keras.layers.MaxPooling2D(
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 maximum 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 the "valid"
padding option, has a spatial 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])
max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
strides=(1, 1), padding='valid')
max_pool_2d(x)
<tf.Tensor: shape=(1, 2, 2, 1), dtype=float32, numpy=
array([[[[5.],
[6.]],
[[8.],
[9.]]]], dtype=float32)>
For example, for strides=(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])
max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
strides=(2, 2), padding='valid')
max_pool_2d(x)
<tf.Tensor: shape=(1, 1, 2, 1), dtype=float32, numpy=
array([[[[6.],
[8.]]]], dtype=float32)>
Usage Example:
input_image = tf.constant([[[[1.], [1.], [2.], [4.]],
[[2.], [2.], [3.], [2.]],
[[4.], [1.], [1.], [1.]],
[[2.], [2.], [1.], [4.]]]])
output = tf.constant([[[[1], [0]],
[[0], [1]]]])
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
input_shape=(4, 4, 1)))
model.compile('adam', 'mean_squared_error')
model.predict(input_image, steps=1)
array([[[[2.],
[4.]],
[[4.],
[4.]]]], dtype=float32)
For example, for stride=(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])
max_pool_2d = tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
strides=(1, 1), padding='same')
max_pool_2d(x)
<tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
array([[[[5.],
[6.],
[6.]],
[[8.],
[9.],
[9.]],
[[8.],
[9.],
[9.]]]], dtype=float32)>
Input shape | |
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|
Output shape | |
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Returns | |
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A tensor of rank 4 representing the maximum pooled values. See above for output shape. |