Comments (3)
is it worth to migrate to Pytorch style of implementation?
This seems a more personal question. However, I'll give my opinion.
Pytorch is almost at the same level than tensorflow. The only difference that I know of is that it has the convolution operation.
I would recommend the library complexPytorch if you prefer Pytorch. However, recent advancements on pytorch might indicate that CVNNs might be fully supported in the future.
As if you question is if I should particularly do it. Well no, the change would also be as starting from zero. So a huge investment of my time which I don't have.
is it possible to build similar architectures in Pytorch?
Yes! Again, there is complexPytorch for that.
Is there advantages in using Pytorch that you do not have in Keras?
Not particularly that I know of. Other than the ones you can find in a "tensorflow vs pytorch" google search.
As a conclusion:
I would say: use the library you like best.
If you have no preference, and your only use case is CVNNs and you plan it to be a long term project, then Pytorch has higher chances of fully supporting CVNNs in the future.
from cvnn.
Thanks for the fast reply!
Apologies if sounded personal but I was asking, mainly, because of the updates that you were posting concerning Pytorch and I was curious whether you were comparing/considering using Pytorch (or if they are supported already). I had seen the library that you mentioned and I was almost sure it was becoming obsolete due to the advances/support with the main Pytorch modules but, I will double check.
In all honestly, I think your library is very good and straightfoward for my current use, was really just curious whether the CVNNs were supported by Pytorch already and if the results similar.
from cvnn.
I understand.
So as far as I am concerned. Both have:
- Complex gradient
- Complex Dtypes
Pytorch also has complex convolution since mid-2022
Both libraries lack full support by not having losses, activation functions, layers, etc. So yet, both mine and complexPytorch are still useful, I imagine they will stay so for at least a couple of years.
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Related Issues (20)
- I'm not getting complex valued output HOT 8
- CVNN API 3D layers HOT 6
- 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
- Implement complex-valued constraint parameter HOT 7
- Terrible slow caused by ComplexBatchNormalization() HOT 4
- Custom Activation Functions with tensorflow 2.8.2 HOT 1
- 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
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