Source code for mindspore.nn.dynamic_lr

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"""Dynamic Learning Rate"""
from __future__ import absolute_import

import math

from mindspore import _checkparam as validator


[docs]def piecewise_constant_lr(milestone, learning_rates): r""" Get piecewise constant learning rate. The learning rate for each step will be stored in a list. Calculate learning rate by the given `milestone` and `learning_rates`. Let the value of `milestone` be :math:`(M_1, M_2, ..., M_t, ..., M_N)` and the value of `learning_rates` be :math:`(x_1, x_2, ..., x_t, ..., x_N)`. N is the length of `milestone`. Let the output learning rate be :math:`y[i]`, then for the :math:`i`-th step, the formula of computing decayed_learning_rate[i] is: .. math:: y[i] = x_t,\ for\ i \in [M_{t-1}, M_t) Args: milestone (Union[list[int], tuple[int]]): A list of milestone. When the specified step is reached, use the corresponding `learning_rates`. This list is a monotone increasing list. Every element in the list must be greater than 0. learning_rates (Union[list[float], tuple[float]]): A list of learning rates. Returns: list[float]. The size of list is :math:`M_N`. Raises: TypeError: If `milestone` or `learning_rates` is neither a tuple nor a list. ValueError: If the length of `milestone` and `learning_rates` is not same. ValueError: If the value in `milestone` is not monotonically decreasing. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.nn as nn >>> >>> milestone = [2, 5, 10] >>> learning_rates = [0.1, 0.05, 0.01] >>> lr = nn.piecewise_constant_lr(milestone, learning_rates) >>> # learning_rates = 0.1 if step <= 2 >>> # learning_rates = 0.05 if 2 < step <= 5 >>> # learning_rates = 0.01 if 5 < step <= 10 >>> net = nn.Dense(2, 3) >>> optim = nn.SGD(net.trainable_params(), learning_rate=lr) """ validator.check_value_type('milestone', milestone, (tuple, list)) validator.check_value_type('learning_rates', learning_rates, (tuple, list)) if len(milestone) != len(learning_rates): raise ValueError(f"For 'piecewise_constant_lr', " f"the size of 'milestone' must be same with the size of 'learning_rates', " f"but got 'milestone' size: {len(milestone)}, 'learning_rates' size: {len(learning_rates)}.") lr = [] last_item = 0 for i, item in enumerate(milestone): validator.check_positive_int(item, f'milestone[{i}]') validator.check_is_float(learning_rates[i], f'learning_rates[{i}]') if item < last_item: raise ValueError(f"For 'piecewise_constant_lr', " f"the value of milestone[{i}] must be greater than milestone[{i - 1}], " f"but got milestone[{i}]: {milestone[i]}, " f"milestone[{i - 1}]: {milestone[i - 1]}.") lr += [learning_rates[i]] * (item - last_item) last_item = item return lr
def _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair): 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_positive_float(learning_rate, 'learning_rate') validator.check_is_float(learning_rate, 'learning_rate') validator.check_positive_float(decay_rate, 'decay_rate') validator.check_is_float(decay_rate, 'decay_rate') validator.check_value_type('is_stair', is_stair, [bool])
[docs]def exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False): r""" Calculates learning rate base on exponential decay function. The learning rate for each step will be stored in a list. For the i-th step, the formula of computing decayed_learning_rate[i] is: .. math:: decayed\_learning\_rate[i] = learning\_rate * decay\_rate^{\frac{current\_epoch}{decay\_epoch}} Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`. Args: learning_rate (float): The initial value of learning rate. decay_rate (float): The decay rate. total_step (int): The total number of steps. step_per_epoch (int): The number of steps in per epoch. decay_epoch (int): Number of epochs to decay over. is_stair (bool): If true, learning rate is decayed once every `decay_epoch` times. Default: ``False`` . Returns: list[float]. The size of list is `total_step`. Raises: TypeError: If `total_step` or `step_per_epoch` or `decay_epoch` is not an int. TypeError: If `is_stair` is not a bool. TypeError: If `learning_rate` or `decay_rate` is not a float. ValueError: If `learning_rate` or `decay_rate` is less than or equal to 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.nn as nn >>> >>> learning_rate = 0.1 >>> decay_rate = 0.9 >>> total_step = 6 >>> step_per_epoch = 2 >>> decay_epoch = 1 >>> lr = nn.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch) >>> net = nn.Dense(2, 3) >>> optim = nn.SGD(net.trainable_params(), learning_rate=lr) """ _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair) lr = [] for i in range(total_step): if is_stair: lr.append(learning_rate * decay_rate ** math.floor(math.floor(i / step_per_epoch) / decay_epoch)) else: lr.append(learning_rate * decay_rate ** (math.floor(i / step_per_epoch) / decay_epoch)) return lr
[docs]def natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False): r""" Calculates learning rate base on natural exponential decay function. The learning rate for each step will be stored in a list. For the i-th step, the formula of computing decayed_learning_rate[i] is: .. math:: decayed\_learning\_rate[i] = learning\_rate * e^{-decay\_rate * current\_epoch} Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`. Args: learning_rate (float): The initial value of learning rate. decay_rate (float): The decay rate. total_step (int): The total number of steps. step_per_epoch (int): The number of steps in per epoch. decay_epoch (int): Number of epochs to decay over. is_stair (bool): If true, learning rate is decayed once every `decay_epoch` times. Default: ``False`` . Returns: list[float]. The size of list is `total_step`. Raises: TypeError: If `total_step` or `step_per_epoch` or `decay_epoch` is not an int. TypeError: If `is_stair` is not a bool. TypeError: If `learning_rate` or `decay_rate` is not a float. ValueError: If `learning_rate` or `decay_rate` is less than or equal to 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.nn as nn >>> >>> learning_rate = 0.1 >>> decay_rate = 0.9 >>> total_step = 6 >>> step_per_epoch = 2 >>> decay_epoch = 2 >>> lr = nn.natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) >>> net = nn.Dense(2, 3) >>> optim = nn.SGD(net.trainable_params(), learning_rate=lr) """ _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair) function = lambda x, y: x if is_stair: function = lambda x, y: math.floor(x / y) * y lr = [] for i in range(total_step): lr.append(learning_rate * math.e ** (-decay_rate * function(math.floor(i / step_per_epoch), decay_epoch))) return lr
[docs]def inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False): r""" Calculates learning rate base on inverse-time decay function. The learning rate for each step will be stored in a list. For the i-th step, the formula of computing decayed_learning_rate[i] is: .. math:: decayed\_learning\_rate[i] = learning\_rate / (1 + decay\_rate * current\_epoch / decay\_epoch) Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`. Args: learning_rate (float): The initial value of learning rate. decay_rate (float): The decay rate. total_step (int): The total number of steps. step_per_epoch (int): The number of steps in per epoch. decay_epoch (int): Number of epochs to decay over. is_stair (bool): If true, learning rate is decayed once every `decay_epoch` times. Default: ``False`` . Returns: list[float]. The size of list is `total_step`. Raises: TypeError: If `total_step` or `step_per_epoch` or `decay_epoch` is not an int. TypeError: If `is_stair` is not a bool. TypeError: If `learning_rate` or `decay_rate` is not a float. ValueError: If `learning_rate` or `decay_rate` is less than or equal to 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.nn as nn >>> >>> learning_rate = 0.1 >>> decay_rate = 0.5 >>> total_step = 6 >>> step_per_epoch = 1 >>> decay_epoch = 1 >>> lr = nn.inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) >>> net = nn.Dense(2, 3) >>> optim = nn.SGD(net.trainable_params(), learning_rate=lr) """ _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair) lr = [] for i in range(total_step): if is_stair: lr.append(learning_rate / (1 + decay_rate * math.floor(math.floor(i / step_per_epoch) / decay_epoch))) else: lr.append(learning_rate / (1 + decay_rate * math.floor(i / step_per_epoch) / decay_epoch)) return lr
[docs]def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch): r""" Calculates learning rate base on cosine decay function. The learning rate for each step will be stored in a list. For the i-th step, the formula of computing decayed_learning_rate[i] is: .. math:: decayed\_learning\_rate[i] = min\_lr + 0.5 * (max\_lr - min\_lr) * (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): Number of epochs to decay over. Returns: list[float]. The size of list is `total_step`. Raises: TypeError: If `min_lr` or `max_lr` is not a float. TypeError: If `total_step` or `step_per_epoch` or `decay_epoch` is not an int. ValueError: If `max_lr` is not greater than 0 or `min_lr` is less than 0. ValueError: If `total_step` or `step_per_epoch` or `decay_epoch` is less than 0. ValueError: If `min_lr` is greater than or equal to `max_lr`. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.nn as nn >>> >>> min_lr = 0.01 >>> max_lr = 0.1 >>> total_step = 6 >>> step_per_epoch = 2 >>> decay_epoch = 2 >>> lr = nn.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch) >>> net = nn.Dense(2, 3) >>> optim = nn.SGD(net.trainable_params(), learning_rate=lr) """ if not isinstance(min_lr, float): raise TypeError(f"For 'cosine_decay_lr', the argument 'min_lr' must be type of float, " f"but got 'min_lr' type: {type(min_lr)}.") 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(f"For 'cosine_decay_lr', the 'max_lr' must be greater than the 'min_lr', " f"but got 'max_lr' value: {max_lr}, 'min_lr' value: {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
[docs]def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power, update_decay_epoch=False): r""" Calculates learning rate base on polynomial decay function. The learning rate for each step will be stored in a list. 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 :math:`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): Number of epochs to decay over. power (float): The power of polynomial. It 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`. Raises: TypeError: If `learning_rate` or `end_learning_rate` or `power` is not a float. TypeError: If `total_step` or `step_per_epoch` or `decay_epoch` is not an int. TypeError: If `update_decay_epoch` is not a bool. ValueError: If `learning_rate` or `power` is not greater than 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.nn as nn >>> >>> lr = 0.1 >>> end_learning_rate = 0.01 >>> total_step = 6 >>> step_per_epoch = 2 >>> decay_epoch = 2 >>> power = 0.5 >>> lr = nn.polynomial_decay_lr(lr, end_learning_rate, total_step, step_per_epoch, decay_epoch, power) >>> net = nn.Dense(2, 3) >>> optim = nn.SGD(net.trainable_params(), learning_rate=lr) """ 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(f"For 'polynomial_decay_lr', the argument 'end_learning_rate' must be type of float, " f"but got 'end_learning_rate' type: {type(end_learning_rate)}.") 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
[docs]def warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch): r""" Gets learning rate warming up. The learning rate for each step will be stored in a list. For the i-th step, the formula of computing warmup_learning_rate[i] is: .. math:: warmup\_learning\_rate[i] = learning\_rate * tmp\_epoch / 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. total_step (int): The total number of steps. step_per_epoch (int): The number of steps in per epoch. warmup_epoch (int): A value that determines the epochs of the learning rate is warmed up. Returns: list[float]. The size of list is `total_step`. Raises: TypeError: If `learning_rate` is not a float. TypeError: If `total_step` or `step_per_epoch` or `decay_epoch` is not an int. ValueError: If `learning_rate` is less than 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.nn as nn >>> >>> learning_rate = 0.1 >>> total_step = 6 >>> step_per_epoch = 2 >>> warmup_epoch = 2 >>> lr = nn.warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch) >>> net = nn.Dense(2, 3) >>> optim = nn.SGD(net.trainable_params(), learning_rate=lr) """ if not isinstance(learning_rate, float): raise TypeError(f"For 'warmup_lr', the argument 'learning_rate' must be type of float, " f"but got 'learning_rate' type: {type(learning_rate)}.") 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
__all__ = [ 'piecewise_constant_lr', 'exponential_decay_lr', 'natural_exp_decay_lr', 'inverse_decay_lr', 'cosine_decay_lr', 'polynomial_decay_lr', 'warmup_lr' ]