View source on GitHub |
Spatial 3D version of Dropout.
Inherits From: Dropout
, Layer
, Operation
tf.keras.layers.SpatialDropout3D(
rate, data_format=None, seed=None, name=None, dtype=None
)
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, SpatialDropout3D will help promote independence between feature maps and should be used instead.
Call arguments | |
---|---|
inputs
|
A 5D tensor. |
training
|
Python boolean indicating whether the layer should behave in training mode (applying dropout) or in inference mode (pass-through). |
Input shape | |
---|---|
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'.
|
Output shape: Same as input.
Reference:
Methods
from_config
@classmethod
from_config( config )
Creates a layer from its config.
This method is the reverse of get_config
,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights
).
Args | |
---|---|
config
|
A Python dictionary, typically the output of get_config. |
Returns | |
---|---|
A layer instance. |
symbolic_call
symbolic_call(
*args, **kwargs
)