def __init__(self, params, steppers, **defaults):
self.steppers = listify(steppers)
# if '_defaults' attribute of every stepper is not in defaults, add it
maybe_update(self.steppers, defaults, get_defaults)
# might be a generator
self.param_groups = list(params)
# ensure params is a list of lists
if not isinstance(self.param_groups[0], list):
self.param_groups = [self.param_groups]
self.hypers = [{**defaults} for p in self.param_groups]
def adam_opt(xtra_step=None, **kwargs):
"adam optimizer"
return partial(StatefulOptimizer,
steppers=[adam_step, weight_decay] + listify(xtra_step),
stateupdaters=[
AverageGrad(dampening=True),
AverageSqrGrad(),
StepCount()
],
**kwargs)
def compose(x, funcs, *args, order_key="_order", **kwargs):
"return steppers' result in the ascending way of steppers' order"
# key = lambda o: getattr(o, order_key, 0)
def key(obj):
"get steppers' order"
return getattr(obj, order_key, 0)
for f in sorted(listify(funcs), key=key):
x = f(x, **kwargs)
return x
def __init__(self,
model,
data,
loss_func,
opt_func,
lr=1e-2,
cbs=None,
cb_funcs=None):
self.model = model
self.data = data
self.loss_func = loss_func
self.opt_func = opt_func
self.lr = lr
self.in_train = False
self.logger = print
self.opt = None
# NB: Things marked "NEW" are covered in lesson 12
# NEW: avoid need for set_runner
self.cbs = []
# self.add_cb(TrainEvalCallback())
self.add_cbs(cbs)
self.add_cbs(cbf() for cbf in listify(cb_funcs))
def __init__(self, metrics, in_train):
self.metrics = listify(metrics)
self.in_train = in_train
def remove_cbs(self, cbs):
for cb in listify(cbs):
self.cbs.remove(cb)
def add_cbs(self, cbs):
for cb in listify(cbs):
self.add_cb(cb)
def __init__(self, params, steppers, stateupdaters=None, **defaults):
self.stateupdaters = listify(stateupdaters)
maybe_update(self.stateupdaters, defaults, get_defaults)
super().__init__(params, steppers, **defaults)
self.state = {}