def get_updates(self, loss, params):
lr_multiplier = piecewise_linear(self.iterations, self.lr_schedule)
old_update = K.update
def new_update(x, new_x):
if is_one_of(x, params):
new_x = x + (new_x - x) * lr_multiplier
return old_update(x, new_x)
K.update = new_update
updates = super(new_optimizer, self).get_updates(loss, params)
K.update = old_update
return updates
def _decayed_lr(self, var_dtype):
lr_multiplier = piecewise_linear(self.iterations, self.lr_schedule)
lr_t = super(new_optimizer, self)._decayed_lr(var_dtype)
return lr_t * K.cast(lr_multiplier, var_dtype)
def __init__(self, lr_schedule, *args, **kwargs):
super(new_optimizer, self).__init__(*args, **kwargs)
self.lr_schedule = {int(i): j for i, j in lr_schedule.items()}
self.lr_multiplier = piecewise_linear(self.iterations,
self.lr_schedule)
def __init__(self, optimizer, schedule=None, **kwargs):
super(PiecewiseLinearLearningRate, self).__init__(optimizer, **kwargs)
self.schedule = {int(i): j for i, j in schedule.items()}
factor = piecewise_linear(self.iterations, self.schedule)
self.learning_rate = self.learning_rate * factor
def __call__(self, step):
with ops.name_scope_v2(self.name or "PiecewiseLinear") as name:
return piecewise_linear(step, self.schedule)
