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# tf.keras.layers.MaxPool2D

Max pooling operation for 2D spatial data.

Inherits From: Layer, Module

### Used in the notebooks

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),
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),
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()
input_shape=(4, 4, 1)))
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),
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)>

pool_size 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.
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.
padding 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.
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".

#### 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).

A tensor of rank 4 representing the maximum pooled values. See above for output shape.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]