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TensorFlow 1 version
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View source on GitHub
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Max pooling operation for 2D spatial data.
tf.keras.layers.MaxPool2D(
pool_size=(2, 2), strides=None, padding='valid', data_format=None,
**kwargs
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
Downsamples the input representation by taking the maximum value over the
window defined by pool_size for each dimension along the features axis.
The window is shifted by strides in each dimension. The resulting output
when using "valid" padding option has a shape(number of rows or columns) of:
output_shape = (input_shape - pool_size + 1) / strides)
The resulting output shape when using the "same" padding option is:
output_shape = input_shape / strides
For example, for stride=(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 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])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, 3, 1), dtype=float32, numpy=array([[[[ 6.],[ 7.],[ 8.]],[[10.],[11.],[12.]]]], 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)>
Arguments | |
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pool_size
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integer or tuple of 2 integers,
window size over which to take the maximum.
(2, 2) will take the max value over a 2x2 pooling window.
If only one integer is specified, the same window length
will be used for both dimensions.
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strides
|
Integer, tuple of 2 integers, or None.
Strides values. Specifies how far the pooling window moves
for each pooling step. If None, it will default to pool_size.
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padding
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One of "valid" or "same" (case-insensitive).
"valid" means no padding. "same" results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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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".
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Input shape:
- 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
data_format='channels_last': 4D tensor with shape(batch_size, pooled_rows, pooled_cols, channels). - If
data_format='channels_first': 4D tensor with shape(batch_size, channels, pooled_rows, pooled_cols).
Returns | |
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| A tensor of rank 4 representing the maximum pooled values. See above for output shape. |