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

Usage

layer_average_pooling_1d(
  object,
  pool_size,
  strides = NULL,
  padding = "valid",
  data_format = NULL,
  name = NULL,
  ...
)

Arguments

object: Object to compose the layer with. A tensor, array, or sequential model.
pool_size: int, size of the max pooling window.
strides: int or NULL. Specifies how much the pooling window moves for each pooling step. If NULL, 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".
name: String, name for the object
...: For forward/backward compatability.

Value

The return value depends on the value provided for the first argument. If object is:

a keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.
a keras_input(), then the output tensor from calling layer(input) is returned.
NULL or missing, then a Layer instance is returned.

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 <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1))
output <- x |>
  layer_average_pooling_1d(pool_size = 2,
                           strides = 1,
                           padding = "valid")
output

## tf.Tensor(
## [[[1.5]
##   [2.5]
##   [3.5]
##   [4.5]]], shape=(1, 4, 1), dtype=float32)

strides=2 and padding="valid":

x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1))
output <- x |>
  layer_average_pooling_1d(pool_size = 2,
                           strides = 2,
                           padding = "valid")
output

## tf.Tensor(
## [[[1.5]
##   [3.5]]], shape=(1, 2, 1), dtype=float32)

strides=1 and padding="same":

x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1))
output <- x |>
  layer_average_pooling_1d(pool_size = 2,
                           strides = 1,
                           padding = "same")
output

## tf.Tensor(
## [[[1.5]
##   [2.5]
##   [3.5]
##   [4.5]
##   [5. ]]], shape=(1, 5, 1), dtype=float32)

Other pooling layers:
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()

Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_average()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()