tf.keras.layers.AveragePooling1D

Average pooling for temporal data.

Inherits From: Layer, Module

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.])