mindelec.common.get_poly_lr

mindelec.common.get_poly_lr(global_step, lr_init, lr_end, lr_max, warmup_steps, total_steps, poly_power)[source]

Generate polynomial decay learning rate array. The learning rate decays in a polynomial manner as training goes along.

Parameters

global_step (int) – current step number, non-negtive int value.
lr_init (float) – init learning rate, positive float value.
lr_end (float) – end learning rate, non-negtive float value.
lr_max (float) – max learning rate, positive float value.
warmup_steps (int) – number of warmup epochs, non-negtive int value.
total_steps (int) – total epoch of training, positive int value.
poly_power (float) – poly learning rate power, positive float value.

Returns

Numpy.array, learning rate array.

Supported Platforms:: Ascend

Examples

>>> from mindelec.common import get_poly_lr
>>> learning_rate = get_poly_lr(100, 0.001, 0.1, 0.0001, 1000, 10000, 0.5)
>>> print(learning_rate.shape)
(9900,)