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Given the wrong initial value, the KNet (architecture 1) does not converge #21
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Q1: KalmanNet assumes same distribution during training and inference. If your training dataset has the same distribution of random init value as inference dataset, KalmanNet can deal with this randomness. Best, |
Thank you very much for your patient answer.
At 2023-05-25 21:32:31, "XiaoyongNI" ***@***.***> wrote:
Q1: KalmanNet assumes same distribution during training and inference. If your training dataset has the same distribution of random init value as inference dataset, KalmanNet can deal with this randomness.
Q2: Similar as other Neural Network aided method, to accelerate your training, it's better to normalize your input to similar order of magnitude.
Best,
Xiaoyong
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Q1: It's under branch "architecure #1" of this "KalmanNet_TSP" repo. |
Q1: Do I have to give an exact initial value of "m1x_0"? When I try to have the initial value with error, KNet does not converge.
Q2: My state vector is 9-dimensional (x:[9x1]), and the order of magnitude difference between dimensions is large, do I need to change other data normalization methods?The nn.functional.normalize function is used in your source program.
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