tf.keras.layers.AveragePooling2D

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Average pooling operation for 2D spatial data.

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Main aliases

tf.keras.layers.AvgPool2D

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

Args
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 theimage_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":

Attributes
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`