Pooling (`pyeddl.layers.pooling`)¶

`MaxPooling2D`	Max pooling operation for spatial data.
`AveragePooling2D`	Average pooling operation for spatial data.

MaxPooling2D¶

class pyeddl.layers.pooling.MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None, **kwargs)[source]¶

Max pooling operation for spatial data.

Args:

pool_size: integer or tuple of 2 integers,: factors by which to downscale (vertical, horizontal). (2, 2) will halve the input in both spatial dimension. 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. If None, it will default to pool_size.

padding: One of “valid” or “same” (case-insensitive). 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

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)

__init__(pool_size=(2, 2), strides=None, padding='valid', data_format=None, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

AveragePooling2D¶

class pyeddl.layers.pooling.AveragePooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None, **kwargs)[source]¶

Average pooling operation for spatial data.