def _preprocess_weight_decay(self, weight_decay):
"""Check weight decay, and convert int to float."""
if isinstance(weight_decay, (float, int)):
weight_decay = float(weight_decay)
validator.check_non_negative_float(weight_decay, "weight_decay", self.cls_name)
return weight_decay
raise TypeError("Weight decay should be int or float.")
def _preprocess_single_lr(self, learning_rate):
"""Check lr value, and convert lr to a float, a Tensor or a LearningRateSchedule."""
if isinstance(learning_rate, (float, int)):
learning_rate = float(learning_rate)
validator.check_non_negative_float(learning_rate, "learning rate",
self.cls_name)
return learning_rate
if isinstance(learning_rate, Tensor) and learning_rate.ndim == 0:
return learning_rate
self.dynamic_lr = True
if isinstance(learning_rate, Iterable):
return Tensor(np.array(list(learning_rate)).astype(np.float32))
if isinstance(learning_rate, Tensor):
if learning_rate.ndim > 1:
raise ValueError(
"The dim of `Tensor` type Learning rate should be a 0 or 1,"
f"but got {learning_rate.ndim}.")
if learning_rate.ndim == 1 and learning_rate.size < 2:
logger.warning(
"If use `Tensor` type dynamic learning rate, please make sure that the number"
"of elements in the tensor passed is greater than 1.")
return learning_rate
if isinstance(learning_rate, LearningRateSchedule):
return learning_rate
raise TypeError(
"Learning rate should be int, float, Tensor, Iterable or LearningRateSchedule."
)
def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
use_locking=False, centered=False, loss_scale=1.0, weight_decay=0.0):
super(RMSProp, self).__init__(learning_rate, params, weight_decay, loss_scale)
validator.check_value_type("decay", decay, [float], self.cls_name)
validator.check_non_negative_float(decay, "decay", self.cls_name)
validator.check_value_type("momentum", momentum, [float], self.cls_name)
validator.check_non_negative_float(momentum, "momentum", self.cls_name)
validator.check_value_type("epsilon", epsilon, [float], self.cls_name)
validator.check_positive_float(epsilon, "epsilon", self.cls_name)
validator.check_value_type("use_locking", use_locking, [bool], self.cls_name)
validator.check_value_type("centered", centered, [bool], self.cls_name)
self.centered = centered
if centered:
self.opt = P.ApplyCenteredRMSProp(use_locking)
self.mg = self.parameters.clone(prefix="mean_grad", init='zeros')
else:
self.opt = P.ApplyRMSProp(use_locking)
self.momentum = momentum
self.ms = self.parameters.clone(prefix="mean_square", init='ones')
self.moment = self.parameters.clone(prefix="moment", init='zeros')
self.hyper_map = C.HyperMap()
self.epsilon = epsilon
self.decay = decay
def _check_param_value(beta1, beta2, eps, weight_decay, prim_name):
"""Check the type of inputs."""
validator.check_value_type("beta1", beta1, [float], prim_name)
validator.check_value_type("beta2", beta2, [float], prim_name)
validator.check_value_type("eps", eps, [float], prim_name)
validator.check_value_type("weight_dacay", weight_decay, [float], prim_name)
validator.check_float_range(beta1, 0.0, 1.0, Rel.INC_NEITHER, "beta1", prim_name)
validator.check_float_range(beta2, 0.0, 1.0, Rel.INC_NEITHER, "beta2", prim_name)
validator.check_positive_float(eps, "eps", prim_name)
validator.check_non_negative_float(weight_decay, "weight_decay", prim_name)
def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch):
r"""
Calculate learning rate base on cosine decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
.. math::
decayed\_learning\_rate[i] = min\_learning\_rate + 0.5 * (max\_learning\_rate - min\_learning\_rate) *
(1 + cos(\frac{current\_epoch}{decay\_epoch}\pi))
Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
Args:
min_lr (float): The minimum value of learning rate.
max_lr (float): The maximum value of learning rate.
total_step (int): The total number of steps.
step_per_epoch (int): The number of steps in per epoch.
decay_epoch (int): A value used to calculate decayed learning rate.
Returns:
list[float]. The size of list is `total_step`.
Examples:
>>> min_lr = 0.01
>>> max_lr = 0.1
>>> total_step = 6
>>> step_per_epoch = 2
>>> decay_epoch = 2
>>> output = cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
>>> print(output)
[0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01]
"""
if not isinstance(min_lr, float):
raise TypeError("min_lr must be float.")
validator.check_non_negative_float(min_lr, "min_lr", None)
validator.check_positive_float(max_lr, 'max_lr')
validator.check_is_float(max_lr, 'max_lr')
validator.check_positive_int(total_step, 'total_step')
validator.check_positive_int(step_per_epoch, 'step_per_epoch')
validator.check_positive_int(decay_epoch, 'decay_epoch')
if min_lr >= max_lr:
raise ValueError('`max_lr` should be greater than `min_lr`.')
delta = 0.5 * (max_lr - min_lr)
lr = []
for i in range(total_step):
tmp_epoch = min(math.floor(i / step_per_epoch), decay_epoch)
lr.append(min_lr + delta *
(1 + math.cos(math.pi * tmp_epoch / decay_epoch)))
return lr
def warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch):
r"""
Get learning rate warming up.
For the i-th step, the formula of computing warmup_learning_rate[i] is:
.. math::
warmup\_learning\_rate[i] = learning\_rate * tmp\_epoch / tmp\_warmup\_epoch
Where :math:`tmp\_epoch=min(current\_epoch, warmup\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})`
Args:
learning_rate (float): The initial value of learning rate.
warmup_steps (int): The warm up steps of learning rate.
Inputs:
Tensor. The current step number.
Returns:
Tensor. The learning rate value for the current step.
Examples:
>>> learning_rate = 0.1
>>> total_step = 6
>>> step_per_epoch = 2
>>> warmup_epoch = 2
>>> output = warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)
>>> print(output)
[0.0, 0.0, 0.05, 0.05, 0.1, 0.1]
"""
if not isinstance(learning_rate, float):
raise TypeError("learning_rate must be float.")
validator.check_non_negative_float(learning_rate, "learning_rate", None)
validator.check_positive_int(warmup_epoch, 'warmup_epoch')
validator.check_positive_int(total_step, 'total_step')
validator.check_positive_int(step_per_epoch, 'step_per_epoch')
function = lambda x, y: (x, min(x, y))
lr = []
for i in range(total_step):
current_epoch = math.floor(i / step_per_epoch)
warmup_epoch, tmp_epoch = function(warmup_epoch, current_epoch)
lr.append(learning_rate * tmp_epoch / warmup_epoch)
return lr
def _check_param_value(accum, l1, l2, use_locking, prim_name=None):
"""Check inputs param."""
validator.check_value_type("accum", accum, [float], prim_name)
validator.check_value_type("l1", l1, [float], prim_name)
validator.check_value_type("l2", l2, [float], prim_name)
validator.check_value_type("use_locking", use_locking, [bool], prim_name)
validator.check_non_negative_float(accum, "accum", prim_name)
validator.check_non_negative_float(l1, "l1", prim_name)
validator.check_non_negative_float(l2, "l2", prim_name)
def polynomial_decay_lr(learning_rate,
end_learning_rate,
total_step,
step_per_epoch,
decay_epoch,
power,
update_decay_epoch=False):
r"""
Calculate learning rate base on polynomial decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
.. math::
decayed\_learning\_rate[i] = (learning\_rate - end\_learning\_rate) *
(1 - tmp\_epoch / tmp\_decay\_epoch)^{power} + end\_learning\_rate
Where:
.. math::
tmp\_epoch = min(current\_epoch, decay\_epoch)
.. math::
current\_epoch=floor(\frac{i}{step\_per\_epoch})
.. math::
tmp\_decay\_epoch = decay\_epoch
If `update_decay_epoch` is true, update the value of `tmp_decay_epoch` every epoch. The formula is:
.. math::
tmp\_decay\_epoch = decay\_epoch * ceil(current\_epoch / decay\_epoch)
Args:
learning_rate (float): The initial value of learning rate.
end_learning_rate (float): The end value of learning rate.
total_step (int): The total number of steps.
step_per_epoch (int): The number of steps in per epoch.
decay_epoch (int): A value used to calculate decayed learning rate.
power (float): A value used to calculate decayed learning rate. This parameter must be greater than 0.
update_decay_epoch (bool): If true, update `decay_epoch`. Default: False.
Returns:
list[float]. The size of list is `total_step`.
Examples:
>>> learning_rate = 0.1
>>> end_learning_rate = 0.01
>>> total_step = 6
>>> step_per_epoch = 2
>>> decay_epoch = 2
>>> power = 0.5
>>> r = polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
>>> print(r)
[0.1, 0.1, 0.07363961030678928, 0.07363961030678928, 0.01, 0.01]
"""
validator.check_positive_float(learning_rate, 'learning_rate')
validator.check_is_float(learning_rate, 'learning_rate')
if not isinstance(end_learning_rate, float):
raise TypeError("end_learning_rate must be float.")
validator.check_non_negative_float(end_learning_rate, "end_learning_rate",
None)
validator.check_positive_float(power, 'power')
validator.check_is_float(power, 'power')
validator.check_positive_int(total_step, 'total_step')
validator.check_positive_int(step_per_epoch, 'step_per_epoch')
validator.check_positive_int(decay_epoch, 'decay_epoch')
validator.check_value_type('update_decay_epoch', update_decay_epoch,
[bool])
origin_decay_epoch = decay_epoch
function = lambda x, y: (x, min(x, y))
if update_decay_epoch:
function = lambda x, y: (origin_decay_epoch * max(
math.ceil(y / origin_decay_epoch), 1), y)
lr = []
delta = learning_rate - end_learning_rate
for i in range(total_step):
current_epoch = math.floor(i / step_per_epoch)
decay_epoch, tmp_epoch = function(decay_epoch, current_epoch)
lr.append(delta * (1 - tmp_epoch / decay_epoch)**power +
end_learning_rate)
return lr
def _check_param_value(accum, update_slots, prim_name=None):
"""Check inputs param."""
validator.check_value_type("accum", accum, [float], prim_name)
validator.check_value_type("update_slots", update_slots, [bool], prim_name)
validator.check_non_negative_float(accum, "accum", prim_name)
