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.
Args
rate
Float between 0 and 1. Fraction of the input units to drop.
data_format
"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".
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'.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2026-11-04 UTC."],[],[]]