mindspore.nn.natural_exp_decay_lr

mindspore.nn.natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False)[source]

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:

\[decayed\_learning\_rate[i] = learning\_rate * e^{-decay\_rate * current\_epoch}\]

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) – 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)