Comments (1)
It is likely that if your data is already real-valued, you are good to go with real-valued (conventional) algorithms, no need to convert them just to make it complex.
If you want to convert your real data to complex anyway, you can indeed try something as the Hilbert transform. Or something that makes physical sense, like if you have a time-series signal, making the Fourier transform.
On the contrary, many papers I've seen, that convert complex values to real, use the absolute value of the complex data, ignoring the phase. Or, I would advice if you can, if you have a ND matrix of complex data, transform to (2*N)D matrix with real and imaginary part.
But this is more liked with your application in particular. I cannot do better here than the experts on the topic.
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Related Issues (20)
- 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
- 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
- Type type error for "ComplexInput" HOT 2
- Error while adding a layer HOT 2
- ValueError: Invalid dtype: complex64 with TF 2.16+ HOT 14
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