Dropout3D
neuralpy.layers.regularizers.Dropout3D(p=0.5, name=None)
info
Dropout2D is mostly stable and can be used for any project. In the future, any chance of breaking changes is very low.
Applies the Dropout3Dlayer to the input tensor.
The Dropout3D layer randomly sets entire channel units to 0 with a frequency of rate of p
at each step during training time. It helps prevent overfitting.
Usually the input comes from nn.Conv3D
modules.
For more information, check this page
Supported Arguments
p=0.5
: (Float) Probability of an element to be zeroed. The value should be between 0.0 and 1.0.name=None
: (String) Name of the layer, if not provided then automatically calculates a unique name for the layer
Example Code
from neuralpy.models import Sequential
from neuralpy.layers.regularizers import Dropout3D
# Initializing the Sequential models
model = Sequential()
...
...
...
model.add(Dropout3D(p=0.5, name="MyDropoutLayer"))