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ENH: Bayesian neural network architectures with training requisites #126
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please add some tests, to make sure the code does what it should do. this is not realistic training, but just to exercise the code on some dummy but predefined data. |
Thanks. I will add them. |
Bayesian decoder
full bayesian Vnet
Adding new architectures. Tests will be added soon for all of them. |
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
adapted for multi_class_segmentation
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
Updated for multi-class
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
Bayesian Training is updated now with tests. |
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def vnet( | ||
n_classes=1, | ||
input_shape=(256, 256, 256, 1), |
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@Aakanksha-Rana - does this actually work at this resolution?
Fixes #104 |
Fixes #107 |
Types of changes
Summary
Checklist
(we are using
black
: you canpip install pre-commit
,run
pre-commit install
in thepydra
directoryand
black
will be run automatically with each commit)Acknowledgment