Comments (7)
Ok great! Thanks again.
from cvnn.
Hello and thank you for your interest.
It is possible that the kernel_contraint
is not implemented but the bias parameter should be working...
Could you confirm me which version of the library are you using? Could you try using the last version and see if the issue is solved?
from cvnn.
Thank you for the response. I updated from v1.0.0 (via conda) to v1.2.14 (via pip). Like you said, the bias feature is now working properly. However, the kernel constraint still seems to have no effect.
from cvnn.
Yes sorry, I need to update conda's version. I will do so next week.
Indeed, the kernel_constraint
is not yet implemented.
I may add this feature in the future. I will let you know when (if) I do.
from cvnn.
I have been investigating about this. I could easily do:
- A real-valued
kernel_contraint
that applies to both real and imaginary parts (but separately) - Two real-valued
real_contraint
andimag_contraint
that applies to them separately.
This is a problem because it will prevent (I think) the idea of making a contraint on the phase or amplitude.
I understand that the ideal would be to be able to define a complex-valued contraint but that will require more work and I am not free.
Does this still work for you? If so, which feature do you think would be better suit for you?
from cvnn.
I think having two separate real_constraint
and imag_constraint
options would be especially useful.
In my specific application, I just needed to set certain weights to 0. And I actually made some modifications that worked for me here: master...j-peetz:master
But I only changed the ComplexDense class. And in general, it'd be nice to be able to specify separate real and imaginary constraints.
Thank you so much for being responsive! Having complex NNs opens up a lot of possibilities, and this library is super helpful.
from cvnn.
Congrats! I did a pull request of your code.
I leave this issue open as feature requests of:
- Add Bias contraint
- Add constrain to other layers (such as Conv)
- Add separate
real_constraint
andimag_constraint
- Best case scenario as
complex_constraint
. Related to issue 56217
from cvnn.
Related Issues (20)
- Error: Inputs to a layer should be tensors. Got: <cvnn.layers.core.ComplexInput object at ...> HOT 1
- ValueError: Unknown loss function:ComplexAverageCrossEntropy HOT 3
- Complex data type error with TensorFlow Functional API HOT 2
- Model subclassing compatibility HOT 4
- load CVNN model with succes HOT 1
- Terrible slow caused by ComplexBatchNormalization() HOT 4
- Custom Activation Functions with tensorflow 2.8.2 HOT 1
- Pytorch implementation HOT 3
- ComplexConv2D with bias vector slows down training a lot HOT 7
- "WARNING:tensorflow: You are casting an input of type complex64 to an incompatible dtype float32. This will discard the imaginary part and may not be what you intended." HOT 5
- ModuleNotFoundError: No module named 'cvnn.montecarlo' HOT 1
- Unknown activation function 'cart_relu': Please ensure this object is passed to 'custom objects' argument HOT 5
- Cant find Complex Softmax which takes complex input and output complex output HOT 1
- Best Activation Function in Complex Domain HOT 1
- using this function layers.complex_input(shape=input_shape + (3,)) gives off dtype error HOT 2
- Problem with loading complex valued model HOT 2
- Equivalent Data PreProcessing for complex-valued input
- Data Parallel Distributed support HOT 4
- Best way to convert Real data into complex data type HOT 1
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