AveragePooling2D layer

[source]

AveragePooling2D class

tf_keras.layers.AveragePooling2D(
    pool_size=(2, 2), strides=None, padding="valid", data_format=None, **kwargs
)

Average pooling operation for spatial data.

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 "valid" padding option has a 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])
>>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
...    strides=(1, 1), padding='valid')
>>> avg_pool_2d(x)
<tf.Tensor: shape=(1, 2, 2, 1), dtype=float32, numpy=
  array([[[[3.],
           [4.]],
          [[6.],
           [7.]]]], dtype=float32)>

For example, for stride=(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])
>>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
...    strides=(2, 2), padding='valid')
>>> avg_pool_2d(x)
<tf.Tensor: shape=(1, 1, 2, 1), dtype=float32, numpy=
  array([[[[3.5],
           [5.5]]]], dtype=float32)>

For example, for strides=(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])
>>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
...    strides=(1, 1), padding='same')
>>> avg_pool_2d(x)
<tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
  array([[[[3.],
           [4.],
           [4.5]],
          [[6.],
           [7.],
           [7.5]],
          [[7.5],
           [8.5],
           [9.]]]], dtype=float32)>

Arguments

• 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). "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). When unspecified, uses image_data_format value found in your TF-Keras config file at ~/.keras/keras.json (if exists) else 'channels_last'. Defaults to '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).