mindspore.nn.PolynomialDecayLR

class mindspore.nn.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power, update_decay_steps=False)

Calculates learning rate base on polynomial decay function.

For the i-th step, the formula of computing decayed_learning_rate[i] is:

$$decayed\mathrm{\_}learning\mathrm{\_}rate[i]=(learning\mathrm{\_}rate-end\mathrm{\_}learning\mathrm{\_}rate)\ast (1-tmp\mathrm{\_}step/tmp\mathrm{\_}decay\mathrm{\_}steps{)}^{power}+end\mathrm{\_}learning\mathrm{\_}rate$$

Where :

$$tmp\mathrm{\_}step=min(current\mathrm{\_}step,decay\mathrm{\_}steps)$$

If update_decay_steps is true, update the value of tmp_decay_step every decay_steps. The formula is :

$$tmp\mathrm{\_}decay\mathrm{\_}steps=decay\mathrm{\_}steps\ast ceil(current\mathrm{\_}step/decay\mathrm{\_}steps)$$

Parameters

• 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 must be greater than 0.

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

Inputs:

Tensor. The current step number.

Outputs:

Tensor. The learning rate value for the current step.

Examples

>>> learning_rate = 0.1
>>> end_learning_rate = 0.01
>>> decay_steps = 4
>>> power = 0.5
>>> global_step = Tensor(2, mstype.int32)
>>> polynomial_decay_lr = nn.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
>>> result = polynomial_decay_lr(global_step)
>>> print(result)
0.07363961