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modify lr api

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pull/6087/head
Jiaqi 5 years ago
parent 4781021c1d
commit 806c21854a
2 changed files with 30 additions and 19 deletions
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21
mindspore/nn/dynamic_lr.py
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@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""dynamic learning rate"""
"""Dynamic Learning Rate"""
import math
from mindspore._checkparam import Validator as validator
@ -90,7 +90,7 @@ def exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch,
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 decay once every `decay_epoch` times. Default: False.
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`.
@ -132,7 +132,7 @@ def natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch,
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 decay once every `decay_epoch` times. Default: False.
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`.
@ -175,7 +175,7 @@ def inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, deca
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 decay once every `decay_epoch` times. Default: False.
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`.
@ -261,9 +261,16 @@ def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_e
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),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})`, and
: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)`
Where:
.. math::
`tmp\_epoch = min(current\_epoch, decay\_epoch),
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.

28
mindspore/nn/learning_rate_schedule.py
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@ -61,14 +61,14 @@ class ExponentialDecayLR(LearningRateSchedule):
.. math::
decayed\_learning\_rate[i] = learning\_rate * decay\_rate^{p}
Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, The formula
Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, the formula
is :math:`p = floor(\frac{current\_step}{decay\_steps})`.
Args:
learning_rate (float): The initial value of learning rate.
decay_rate (float): The decay 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.
is_stair (bool): If True, learning rate is decayed once every `decay_steps` time. Default: False.
Inputs:
Tensor. The current step number.
@ -80,7 +80,7 @@ class ExponentialDecayLR(LearningRateSchedule):
>>> learning_rate = 0.1
>>> decay_rate = 0.9
>>> decay_steps = 4
>>> global_step = Tenosr(2, mstype.int32)
>>> global_step = Tensor(2, mstype.int32)
>>> exponential_decay_lr = ExponentialDecayLR(learning_rate, decay_rate, decay_steps)
>>> exponential_decay_lr(global_step)
"""
@ -110,14 +110,14 @@ class NaturalExpDecayLR(LearningRateSchedule):
.. math::
decayed\_learning\_rate[i] = learning\_rate * e^{-decay\_rate * p}
Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, The formula
Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, the formula
is :math:`p = floor(\frac{current\_step}{decay\_steps})`.
Args:
learning_rate (float): The initial value of learning rate.
decay_rate (float): The decay 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.
is_stair (bool): If True, learning rate is decayed once every `decay_steps` time. Default: False.
Inputs:
Tensor. The current step number.
@ -129,7 +129,7 @@ class NaturalExpDecayLR(LearningRateSchedule):
>>> learning_rate = 0.1
>>> decay_rate = 0.9
>>> decay_steps = 4
>>> global_step = Tenosr(2, mstype.int32)
>>> global_step = Tensor(2, mstype.int32)
>>> natural_exp_decay_lr = NaturalExpDecayLR(learning_rate, decay_rate, decay_steps, True)
>>> natural_exp_decay_lr(global_step)
"""
@ -225,7 +225,7 @@ class CosineDecayLR(LearningRateSchedule):
>>> min_lr = 0.01
>>> max_lr = 0.1
>>> decay_steps = 4
>>> global_step = Tenosr(2, mstype.int32)
>>> global_step = Tensor(2, mstype.int32)
>>> cosine_decay_lr = CosineDecayLR(min_lr, max_lr, decay_steps)
>>> cosine_decay_lr(global_steps)
"""
@ -263,16 +263,20 @@ class PolynomialDecayLR(LearningRateSchedule):
decayed\_learning\_rate[i] = (learning\_rate - end\_learning\_rate) *
(1 - tmp\_step / tmp\_decay\_steps)^{power} + end\_learning\_rate
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\_steps = decay\_steps * ceil(current\_step / decay\_steps)`
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\_steps = decay\_steps * ceil(current\_step / decay\_steps)`
Args:
learning_rate (float): The initial value of learning rate.
end_learning_rate (float): The end value of learning rate.
decay_steps (int): A value used to calculate decayed learning rate.
power (float): A value used to calculate decayed learning rate. This parameter should be greater than 0.
update_decay_steps (bool): If true, learning rate decay once every `decay_steps` times. Default: False.
update_decay_steps (bool): If True, learning rate is decayed once every `decay_steps` time. Default: False.
Inputs:
Tensor. The current step number.
@ -285,7 +289,7 @@ class PolynomialDecayLR(LearningRateSchedule):
>>> end_learning_rate = 0.01
>>> decay_steps = 4
>>> power = 0.5
>>> global_step = Tenosr(2, mstype.int32)
>>> global_step = Tensor(2, mstype.int32)
>>> polynomial_decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
>>> polynomial_decay_lr(global_step)
"""

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