layer_spatial_dropout_1d
Spatial 1D version of Dropout.
Description
This version performs the same function as Dropout, however it drops entire 1D feature maps instead of individual elements. If adjacent frames within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, layer_spatial_dropout_1d will help promote independence between feature maps and should be used instead.
Usage
layer_spatial_dropout_1d(
object,
rate,
batch_size = NULL,
name = NULL,
trainable = NULL,
weights = NULL
) 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. |
| rate | float between 0 and 1. Fraction of the input units to drop. |
| batch_size | Fixed batch size for layer |
| name | An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn’t provided. |
| trainable | Whether the layer weights will be updated during training. |
| weights | Initial weights for layer. |
Section
Input shape
3D tensor with shape: (samples, timesteps, channels)
Output shape
Same as input
References
See Also
Other dropout layers: layer_dropout(), layer_spatial_dropout_2d(), layer_spatial_dropout_3d()