layer_global_average_pooling_3d {keras3}

Global average pooling operation for 3D data.

Description

Global average pooling operation for 3D data.

Usage

layer_global_average_pooling_3d(
  object,
  data_format = NULL,
  keepdims = FALSE,
  ...
)

Arguments

object

Object to compose the layer with. A tensor, array, or sequential model.

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, spatial_dim1, spatial_dim2, spatial_dim3, channels)⁠ while "channels_first" corresponds to inputs with shape ⁠(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)⁠. 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".

keepdims

A boolean, whether to keep the temporal dimension or not. If keepdims is FALSE (default), the rank of the tensor is reduced for spatial dimensions. If keepdims is TRUE, the spatial dimension are retained with length 1. The behavior is the same as for tf$reduce_mean() or op_mean().

...

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': 5D tensor with shape: ⁠(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)⁠

• If data_format='channels_first': 5D tensor with shape: ⁠(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)⁠

Output Shape

• If keepdims=FALSE: 2D tensor with shape ⁠(batch_size, channels)⁠.

• If keepdims=TRUE:

  • If data_format="channels_last": 5D tensor with shape ⁠(batch_size, 1, 1, 1, channels)⁠

  • If data_format="channels_first": 5D tensor with shape ⁠(batch_size, channels, 1, 1, 1)⁠

Examples

x <- random_uniform(c(2, 4, 5, 4, 3))
y <- x |> layer_global_average_pooling_3d()
shape(y)

## shape(2, 3)

See Also

• https://keras.io/api/layers/pooling_layers/global_average_pooling3d#globalaveragepooling3d-class

Other pooling layers:
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
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_1d()
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_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()