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add single quotes and modify parameters

This commit is contained in:
李嘉琪 2020-07-24 16:41:25 +08:00
parent a051d7c5dc
commit 8feb9450f2
1 changed files with 8 additions and 9 deletions
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17
mindspore/nn/learning_rate_schedule.py
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@ -59,7 +59,7 @@ class ExponentialDecayLR(LearningRateSchedule):
For the i-th step, the formula of computing decayed_learning_rate[i] is:
.. math::
decayed\_learning\_rate[i] = learning\_rate * decay\_rate^{p}}
decayed\_learning\_rate[i] = learning\_rate * decay\_rate^{p}
Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, The formula
is :math:`p = floor(\frac{current\_step}{decay\_steps})`.
@ -158,7 +158,7 @@ class InverseDecayLR(LearningRateSchedule):
For the i-th step, the formula of computing decayed_learning_rate[i] is:
.. math::
decayed\_learning\_rate[i] = learning\_rate / (1 + decay\_rate * p}
decayed\_learning\_rate[i] = learning\_rate / (1 + decay\_rate * p)
Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, The formula
is :math:`p = floor(\frac{current\_step}{decay\_steps})`.
@ -166,7 +166,7 @@ class InverseDecayLR(LearningRateSchedule):
Args:
learning_rate (float): The initial value of learning rate.
decay_rate (float): The decay rate.
decay_epoch (int): A value used to calculate decayed learning rate.
decay_steps (int): A value used to calculate decayed learning rate.
is_stair (bool): If true, learning rate decay once every `decay_steps` times. Default: False.
Inputs:
@ -207,9 +207,8 @@ class CosineDecayLR(LearningRateSchedule):
.. math::
decayed\_learning\_rate[i] = min\_learning\_rate + 0.5 * (max\_learning\_rate - min\_learning\_rate) *
(1 + cos(\frac{current\_epoch}{decay\_epoch}\pi))
(1 + cos(\frac{current\_step}{decay\_steps}\pi))
Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
Args:
min_lr (float): The minimum value of learning rate.
@ -262,11 +261,11 @@ class PolynomialDecayLR(LearningRateSchedule):
.. math::
decayed\_learning\_rate[i] = (learning\_rate - end\_learning\_rate) *
(1 - tmp\_step / tmp\_decay\_step)^{power} + end\_learning\_rate
(1 - tmp\_step / tmp\_decay\_steps)^{power} + end\_learning\_rate
Where :math:`tmp\_step=min(global\_step, decay\_step).
Where :math:`tmp\_step=min(current\_step, decay\_steps).
If `update_decay_steps` is true, update the value of `tmp_decay_step` every `decay_steps`. The formula
is :math:`tmp\_decay\_step = decay\_step * ceil(global\_step / decay\_steps)`
is :math:`tmp\_decay\_steps = decay\_steps * ceil(current\_step / decay\_steps)`
Args:
learning_rate (float): The initial value of learning rate.
@ -335,7 +334,7 @@ class WarmUpLR(LearningRateSchedule):
.. math::
warmup\_learning\_rate[i] = learning\_rate * tmp\_step / warmup\_steps
Where :math:`tmp\_step=min(global\_step, warmup\_steps).
Where :math:`tmp\_step=min(current\_step, warmup\_steps)`.
Args:
learning_rate (float): The initial value of learning rate.