dlpy.lr_scheduler.StepLR¶
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class
dlpy.lr_scheduler.StepLR(learning_rate=0.001, gamma=0.1, step_size=10)¶ Bases: dlpy.lr_scheduler._LRScheduler
Step learning rate scheduler The learning rate is reduced by a factor(gamma) at certain intervals(step_size) Example:
# reduce learning rate every 2 epochs lr_scheduler = StepLR(learning_rate=0.0001, gamma=0.1, step_size=2) solver = MomentumSolver(lr_scheduler = lr_scheduler, clip_grad_max = 100, clip_grad_min = -100)Parameters: - learning_rate : double, optional
Specifies the initial learning rate.
- gamma : double, optional
Specifies the gamma for the learning rate policy.
- step_size : int, optional
Specifies the step size when the learning rate policy is set to STEP.
Returns: -
__init__(learning_rate=0.001, gamma=0.1, step_size=10)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__([learning_rate, gamma, step_size]) Initialize self. clear() get(k[,d]) items() keys() pop(k[,d]) If key is not found, d is returned if given, otherwise KeyError is raised. popitem() as a 2-tuple; but raise KeyError if D is empty. setdefault(k[,d]) update([E, ]**F) If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v values()