Ejemplo n.º 1
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 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.")
Ejemplo n.º 2
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    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."
        )
Ejemplo n.º 3
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    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
Ejemplo n.º 4
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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)
Ejemplo n.º 5
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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
Ejemplo n.º 6
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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
Ejemplo n.º 7
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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)
Ejemplo n.º 8
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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
Ejemplo n.º 9
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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)