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mindspore.nn.NaturalExpDecayLR

class mindspore.nn.NaturalExpDecayLR(learning_rate, decay_rate, decay_steps, is_stair=False)

Calculates learning rate base on natural exponential decay function.

For current step, the formula of computing decayed learning rate is:

$$decayed\mathrm{\_}learning\mathrm{\_}rate=learning\mathrm{\_}rate\ast e^{-decay\mathrm{\_}rate\ast p}$$

Where :

$$p=\frac{current\mathrm{\_}step}{decay\mathrm{\_}steps}$$

If is_stair is True, the formula is :

$$p=floor(\frac{current\mathrm{\_}step}{decay\mathrm{\_}steps})$$

Parameters

• learning_rate (float) – The initial value of learning rate.

• decay_rate (float) – The decay rate.

• decay_steps (int) – Number of steps to decay over.

• is_stair (bool) – If true , learning rate is decayed once every decay_steps time. Default: False .

Inputs:

• global_step (Tensor) - The current step number.

Outputs:

Tensor. The learning rate value for the current step with shape $()$.

Raises

• TypeError – If learning_rate or decay_rate is not a float.

• TypeError – If decay_steps is not an int or is_stair is not a bool.

• ValueError – If decay_steps is less than 1.

• ValueError – If learning_rate or decay_rate is less than or equal to 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>>
>>> learning_rate = 0.1
>>> decay_rate = 0.9
>>> decay_steps = 4
>>> global_step = Tensor(2, mindspore.int32)
>>> natural_exp_decay_lr = nn.NaturalExpDecayLR(learning_rate, decay_rate, decay_steps, True)
>>> lr = natural_exp_decay_lr(global_step)
>>> net = nn.Dense(2, 3)
>>> optim = nn.SGD(net.trainable_params(), learning_rate=lr)