► Keras 3 API documentation / Layers API / Pooling layers / AveragePooling1D layer

AveragePooling1D layer

[source]

AveragePooling1D class

keras.layers.AveragePooling1D(
    pool_size, strides=None, padding="valid", data_format=None, name=None, **kwargs
)

Average pooling for temporal data.

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

Arguments

  • pool_size: int, size of the max pooling window.
  • strides: int or None. Specifies how much the pooling window moves for each pooling step. If None, it will default to pool_size.
  • 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, steps, features) while "channels_first" corresponds to inputs with shape (batch, features, steps). It defaults to the image_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": 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).

Examples

strides=1 and padding="valid":

>>> x = np.array([1., 2., 3., 4., 5.])
>>> x = np.reshape(x, [1, 5, 1])
>>> avg_pool_1d = keras.layers.AveragePooling1D(pool_size=2,
...    strides=1, padding="valid")
>>> avg_pool_1d(x)

strides=2 and padding="valid":

>>> x = np.array([1., 2., 3., 4., 5.])
>>> x = np.reshape(x, [1, 5, 1])
>>> avg_pool_1d = keras.layers.AveragePooling1D(pool_size=2,
...    strides=2, padding="valid")
>>> avg_pool_1d(x)

strides=1 and padding="same":

>>> x = np.array([1., 2., 3., 4., 5.])
>>> x = np.reshape(x, [1, 5, 1])
>>> avg_pool_1d = keras.layers.AveragePooling1D(pool_size=2,
...    strides=1, padding="same")
>>> avg_pool_1d(x)
AveragePooling1D layer
AveragePooling1D class