layer_global_average_pooling_1d
Global average pooling operation for temporal data.
Description
Global average pooling operation for temporal data.
Usage
layer_global_average_pooling_1d(
object,
data_format = "channels_last",
keepdims = FALSE,
...
) Arguments
| Arguments | Description |
|---|---|
| object | What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is: - missing or NULL, the Layer instance is returned. - a Sequential model, the model with an additional layer is returned. - a Tensor, the output tensor from layer_instance(object) is returned. |
| data_format | One of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. |
| keepdims | A boolean, whether to keep the spatial dimensions or not. If keepdims is FALSE (default), the rank of the tensor is reduced for spatial dimensions. If keepdims is TRUE, the spatial dimensions are retained with length 1. The behavior is the same as for tf.reduce_mean or np.mean. |
| … | standard layer arguments. |
Section
Input shape
3D tensor with shape: (batch_size, steps, features).
Output shape
2D tensor with shape: (batch_size, channels)
See Also
Other pooling layers: layer_average_pooling_1d(), layer_average_pooling_2d(), layer_average_pooling_3d(), 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()