tf.keras.layers.AveragePooling1D

Average pooling for temporal data.

Inherits From: Layer, Module

View aliases

Main aliases

tf.keras.layers.AvgPool1D

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.AveragePooling1D, tf.compat.v1.keras.layers.AvgPool1D

tf.keras.layers.AveragePooling1D(
    pool_size=2, strides=None, padding='valid',
    data_format='channels_last', **kwargs
)

Downsamples the input representation by taking the average value over the window defined by pool_size. The window is shifted by strides. The resulting output when using "valid" padding option has a shape of: output_shape = (input_shape - pool_size + 1) / strides)

The resulting output shape when using the "same" padding option is: output_shape = input_shape / strides

For example, for strides=1 and padding="valid":

x = tf.constant([1., 2., 3., 4., 5.])
x = tf.reshape(x, [1, 5, 1])
x
<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
  array([[[1.],
          [2.],
          [3.],
          [4.],
          [5.]], dtype=float32)>
avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
   strides=1, padding='valid')
avg_pool_1d(x)
<tf.Tensor: shape=(1, 4, 1), dtype=float32, numpy=
array([[[1.5],
        [2.5],
        [3.5],
        [4.5]]], dtype=float32)>

For example, for strides=2 and padding="valid":

x = tf.constant([1., 2., 3., 4., 5.])
x = tf.reshape(x, [1, 5, 1])
x
<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
  array([[[1.],
          [2.],
          [3.],
          [4.],
          [5.]], dtype=float32)>
avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
   strides=2, padding='valid')
avg_pool_1d(x)
<tf.Tensor: shape=(1, 2, 1), dtype=float32, numpy=
array([[[1.5],
        [3.5]]], dtype=float32)>

For example, for strides=1 and padding="same":

x = tf.constant([1., 2., 3., 4., 5.])
x = tf.reshape(x, [1, 5, 1])
x
<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
  array([[[1.],
          [2.],
          [3.],
          [4.],
          [5.]], dtype=float32)>
avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
   strides=1, padding='same')
avg_pool_1d(x)
<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
array([[[1.5],
        [2.5],
        [3.5],
        [4.5],
        [5.]]], dtype=float32)>

Args
pool_size	Integer, size of the average pooling windows.
strides	Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. 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, steps, features) while channels_first corresponds to inputs with shape (batch, features, steps).

Input shape:

• If data_format='channels_last': 3D tensor with shape (batch_size, steps, features).
• If data_format='channels_first': 3D tensor with shape (batch_size, features, steps).

Output shape:

• If data_format='channels_last': 3D tensor with shape (batch_size, downsampled_steps, features).
• If data_format='channels_first': 3D tensor with shape (batch_size, features, downsampled_steps).