mindspore.nn.dynamic_lr
Dynamic Learning Rate
- mindspore.nn.dynamic_lr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)[source]
Calculate learning rate base on cosine decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
\[decayed\_learning\_rate[i] = min\_learning\_rate + 0.5 * (max\_learning\_rate - min\_learning\_rate) * (1 + cos(\frac{current\_epoch}{decay\_epoch}\pi))\]Where \(current\_epoch=floor(\frac{i}{step\_per\_epoch})\).
- Parameters
- 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 >>> cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch) [0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01]
- mindspore.nn.dynamic_lr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False)[source]
Calculate learning rate base on exponential decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
\[decayed\_learning\_rate[i] = learning\_rate * decay\_rate^{\frac{current\_epoch}{decay\_epoch}}\]Where \(current\_epoch=floor(\frac{i}{step\_per\_epoch})\).
- Parameters
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) – A value used to calculate decayed learning rate.
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.
Examples
>>> learning_rate = 0.1 >>> decay_rate = 0.9 >>> total_step = 6 >>> step_per_epoch = 2 >>> decay_epoch = 1 >>> exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch) [0.1, 0.1, 0.09000000000000001, 0.09000000000000001, 0.08100000000000002, 0.08100000000000002]
- mindspore.nn.dynamic_lr.inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False)[source]
Calculate learning rate base on inverse-time decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
\[decayed\_learning\_rate[i] = learning\_rate / (1 + decay\_rate * current\_epoch / decay\_epoch)\]Where \(current\_epoch=floor(\frac{i}{step\_per\_epoch})\).
- Parameters
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) – A value used to calculate decayed learning rate.
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.
Examples
>>> learning_rate = 0.1 >>> decay_rate = 0.5 >>> total_step = 6 >>> step_per_epoch = 1 >>> decay_epoch = 1 >>> inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) [0.1, 0.06666666666666667, 0.05, 0.04, 0.03333333333333333, 0.028571428571428574]
- mindspore.nn.dynamic_lr.natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False)[source]
Calculate learning rate base on natural exponential decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
\[decayed\_learning\_rate[i] = learning\_rate * e^{-decay\_rate * current\_epoch}\]Where \(current\_epoch=floor(\frac{i}{step\_per\_epoch})\).
- Parameters
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) – A value used to calculate decayed learning rate.
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.
Examples
>>> learning_rate = 0.1 >>> decay_rate = 0.9 >>> total_step = 6 >>> step_per_epoch = 2 >>> decay_epoch = 2 >>> natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) [0.1, 0.1, 0.1, 0.1, 0.016529888822158657, 0.016529888822158657]
- mindspore.nn.dynamic_lr.piecewise_constant_lr(milestone, learning_rates)[source]
Get piecewise constant learning rate.
Calculate learning rate by given milestone and learning_rates. Let the value of milestone be \((M_1, M_2, ..., M_N)\) and the value of learning_rates be \((x_1, x_2, ..., x_N)\). N is the length of milestone. Let the output learning rate be y.
\[y[i] = x_t,\ for\ i \in [M_{t-1}, M_t)\]- Parameters
- Returns
list[float]. The size of list is \(M_N\).
Examples
>>> milestone = [2, 5, 10] >>> learning_rates = [0.1, 0.05, 0.01] >>> piecewise_constant_lr(milestone, learning_rates) [0.1, 0.1, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01]
- mindspore.nn.dynamic_lr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power, update_decay_epoch=False)[source]
Calculate learning rate base on polynomial decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
\[decayed\_learning\_rate[i] = (learning\_rate - end\_learning\_rate) * (1 - tmp\_epoch / tmp\_decay\_epoch)^{power} + end\_learning\_rate\]Where:
\[tmp\_epoch = min(current\_epoch, decay\_epoch)\]\[current\_epoch=floor(\frac{i}{step\_per\_epoch})\]\[tmp\_decay\_epoch = decay\_epoch\]If update_decay_epoch is true, update the value of tmp_decay_epoch every epoch. The formula is:
\[tmp\_decay\_epoch = decay\_epoch * ceil(current\_epoch / decay\_epoch)\]- Parameters
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 >>> polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power) [0.1, 0.1, 0.07363961030678928, 0.07363961030678928, 0.01, 0.01]
- mindspore.nn.dynamic_lr.warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)[source]
Get learning rate warming up.
For the i-th step, the formula of computing warmup_learning_rate[i] is:
\[warmup\_learning\_rate[i] = learning\_rate * tmp\_epoch / tmp\_warmup\_epoch\]Where \(tmp\_epoch=min(current\_epoch, warmup\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})\)
- Parameters
- 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 >>> warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch) [0.0, 0.0, 0.05, 0.05, 0.1, 0.1]