mindspore.nn.InverseDecayLR

class mindspore.nn.InverseDecayLR(learning_rate, decay_rate, decay_steps, is_stair=False)[source]

Calculates learning rate base on inverse-time decay function.

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

\[decayed\_learning\_rate[i] = learning\_rate / (1 + decay\_rate * p)\]

Where :

\[p = \frac{current\_step}{decay\_steps}\]

If is_stair is True, The formula is :

\[p = floor(\frac{current\_step}{decay\_steps})\]
Parameters

  • 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.

Inputs:

Tensor. The current step number.

Outputs:

Tensor. The learning rate value for the current step.

Examples

>>> learning_rate = 0.1
>>> decay_rate = 0.9
>>> decay_steps = 4
>>> global_step = Tensor(2, mstype.int32)
>>> inverse_decay_lr = nn.InverseDecayLR(learning_rate, decay_rate, decay_steps, True)
>>> result = inverse_decay_lr(global_step)
>>> print(result)
0.1