This version performs the same function as Dropout, however it drops entire
3D feature maps instead of individual elements. If adjacent voxels 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_3d will help promote independence between
feature maps and should be used instead.
layer_spatial_dropout_3d(
object,
rate,
data_format = NULL,
batch_size = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)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.
float between 0 and 1. Fraction of the input units to drop.
'channels_first' or 'channels_last'. In 'channels_first'
mode, the channels dimension (the depth) is at index 1, in 'channels_last'
mode is it at index 4. 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".
Fixed batch size for layer
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.
Whether the layer weights will be updated during training.
Initial weights for layer.
5D tensor with shape: (samples, channels, dim1, dim2, dim3) if data_format='channels_first' or 5D tensor with shape: (samples, dim1, dim2, dim3, channels) if data_format='channels_last'.
Same as input
- Efficient Object Localization Using Convolutional Networks
Other dropout layers:
layer_dropout(),
layer_spatial_dropout_1d(),
layer_spatial_dropout_2d()