Comments (3)
Initialising the model after putting the constraints is the right way to go. Note that this is described in the main example in the README:
# Weights are initialized to a random value when you put the constraints, but
# you may re-initialize them to a different value by assigning to them
but also in some parts of the docs.
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I have worked around the issue by setting my initial values after registering the constraints. It might be worth mentioning in the documentation somewhere about how/when to set initial values so other people avoid the same confusion.
from geotorch.
Closing this one for now.
from geotorch.
Related Issues (20)
- ObliqueManifold HOT 5
- Parameterization of Orthogonal Group HOT 3
- "Close to" constraint? HOT 5
- geotorch.optim HOT 10
- Does Euclidean optimizer fail when the matrix is not skew-symmetric or skew-Hermitian? HOT 1
- can we expect geojax? HOT 8
- seems to fail with large tensors HOT 12
- redundant computations? HOT 3
- Complex numbers: hermitian and unitary HOT 1
- paper about low-rank HOT 2
- Constraints API doesn't work when OpenCV is imported HOT 2
- Posing orthogonality condition on convolution weights in v0.3.0 HOT 9
- Fail to apply constraints when layer is on cuda HOT 4
- Can symmetric parameterization used in CNN? HOT 1
- I am very interested in this subject. HOT 2
- Can two parametrizations be used in the same tensor? HOT 1
- Does geotorch.orthogonal is a mapping from Euclidean space onto the corresponding manifold space? HOT 4
- Use 'geotorch.orthogonal' (and similarly 'torch.nn.utils.parametrizations.orthogonal') on a linear layer but got 'grad = None' of the weight of the layer HOT 3
- Annoying warning in Pytorch 1.12. HOT 1
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