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| __init__ (self, np.ndarray x, Callable|float step, Callable|None grad=None, Callable|None prox=None, float b1=0.9, float b2=0.999, float eps=1e-8, float p=0.25, np.ndarray|None m0=None, np.ndarray|None v0=None, np.ndarray|None vhat0=None, str scheme="amsgrad", float prox_e_rel=1e-6) |
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| | update (self, int it, np.ndarray input_grad, *args) |
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| __init__ (self, np.ndarray x, dict[str, np.ndarray] helpers, Callable|float step, Callable|None grad=None, Callable|None prox=None) |
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| float | step (self) |
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| tuple[int,...] | shape (self) |
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| npt.DTypeLike | dtype (self) |
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| Parameter | copy (self) |
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| | resize (self, Box old_box, Box new_box) |
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Operator updated using te Proximal ADAM algorithm
Uses multiple variants of adaptive quasi-Newton gradient descent
* Adam (Kingma & Ba 2015)
* NAdam (Dozat 2016)
* AMSGrad (Reddi, Kale & Kumar 2018)
* PAdam (Chen & Gu 2018)
* AdamX (Phuong & Phong 2019)
* RAdam (Liu et al. 2019)
See details of the algorithms in the respective papers.