tf.keras.layers.AveragePooling2D

Average pooling operation for 2D spatial data.

Inherits From: Layer, Operation

tf.keras.layers.AveragePooling2D(
    pool_size,
    strides=None,
    padding='valid',
    data_format=None,
    name=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 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])
avg_pool_2d = keras.layers.AveragePooling2D(pool_size=(2, 2),
   strides=(1, 1), padding="valid")
avg_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])
avg_pool_2d = keras.layers.AveragePooling2D(pool_size=(2, 2),
   strides=(2, 2), padding="valid")
avg_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])
avg_pool_2d = keras.layers.AveragePooling2D(pool_size=(2, 2),
   strides=(1, 1), padding="same")
avg_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

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symbolic_call(
    *args, **kwargs
)