Webclr_callback.pycontains the callback class CyclicLR(). This class includes 3 built-in CLR policies, 'triangular', 'triangular2', and 'exp_range', as detailed in the original paper. It also allows for custom amplitude scaling functions, enabling easy experimentation. Arguments for this class include:

Cosine decay with warmup和 周期性学习率（CLR）(学习率更新方 …

WebCyclic Learning Rate is a scheduling technique that varies the learning rate between the minimal and maximal thresholds. The learning rate values change in a cycle from … Web“triangular2”: A basic triangular cycle that scales initial amplitude by half each cycle. “exp_range”: A cycle that scales initial amplitude by \text {gamma}^ {\text {cycle iterations}} gammacycle iterations at each cycle iteration. This implementation was adapted from the … s0 pheasant\u0027s-eyes

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WebOct 1, 2024 · CyclicLR ( opt2, mode='triangular2' , base_lr=self. hparams. base_lr , max_lr=self. hparams. max_lr, step_size_up=step_size_up ) return ( { "optimizer": opt1, … WebJan 4, 2024 · An exemplary cyclicLR class is taken from these two repositories [ 4] [5] and is given below: def cyclical_lr (step_sz, min_lr=0.001, max_lr=1, mode='triangular', scale_func=None,... Web"triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. "exp_range": A cycle that scales initial amplitude by gamma** (cycle iterations) at each cycle iteration. For more detail, please see paper. # Example ```python clr = CyclicLR (base_lr=0.001, max_lr=0.006, step_size=2000., mode='triangular') s0 rabbit\u0027s-foot