tf.keras.layers.MaxPool2D

Max pooling operation for 2D spatial data.

Inherits From: Layer, Operation

tf.keras.layers.MaxPool2D(
    pool_size=(2, 2),
    strides=None,
    padding='valid',
    data_format=None,
    name=None,
    **kwargs
)

Used in the notebooks

Used in the guide Used in the tutorials

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

pool_size int or tuple of 2 integers, factors by which to downscale (dim1, dim2). If only one integer is specified, the same window length will be used for all dimensions.
strides int or tuple of 2 integers, or None. Strides values. If None, it will default to pool_size. If only one int is specified, the same stride size will be used for all dimensions.
padding string, either "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 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, 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, height, width, channels).
  • If data_format="channels_first": 4D tensor with shape (batch_size, channels, height, width).

Output shape:

  • If data_format="channels_last": 4D tensor with shape (batch_size, pooled_height, pooled_width, channels).
  • If data_format="channels_first": 4D tensor with shape (batch_size, channels, pooled_height, pooled_width).

Examples:

strides=(1, 1) and padding="valid":

x = np.array([[1., 2., 3.],
              [4., 5., 6.],
              [7., 8., 9.]])
x = np.reshape(x, [1, 3, 3, 1])
max_pool_2d = keras.layers.MaxPooling2D(pool_size=(2, 2),
   strides=(1, 1), padding="valid")
max_pool_2d(x)

strides=(2, 2) and padding="valid":

x = np.array([[1., 2., 3., 4.],
              [5., 6., 7., 8.],
              [9., 10., 11., 12.]])
x = np.reshape(x, [1, 3, 4, 1])
max_pool_2d = keras.layers.MaxPooling2D(pool_size=(2, 2),
   strides=(2, 2), padding="valid")
max_pool_2d(x)

stride=(1, 1) and padding="same":

x = np.array([[1., 2., 3.],
              [4., 5., 6.],
              [7., 8., 9.]])
x = np.reshape(x, [1, 3, 3, 1])
max_pool_2d = keras.layers.MaxPooling2D(pool_size=(2, 2),
   strides=(1, 1), padding="same")
max_pool_2d(x)

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

View source

symbolic_call(
    *args, **kwargs
)