mindspore.nn.warmup_lr
- mindspore.nn.warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)[source]
Gets learning rate warming up. The learning rate for each step will be stored in a list.
For the i-th step, the formula of computing warmup_learning_rate[i] is:
\[warmup\_learning\_rate[i] = learning\_rate * tmp\_epoch / warmup\_epoch\]Where \(tmp\_epoch= \min(current\_epoch, warmup\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})\)
- Parameters
- Returns
list[float]. The size of list is total_step.
- Raises
TypeError – If learning_rate is not a float.
TypeError – If total_step or step_per_epoch or decay_epoch is not an int.
ValueError – If learning_rate is less than 0.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore.nn as nn >>> >>> learning_rate = 0.1 >>> total_step = 6 >>> step_per_epoch = 2 >>> warmup_epoch = 2 >>> lr = nn.warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch) >>> net = nn.Dense(2, 3) >>> optim = nn.SGD(net.trainable_params(), learning_rate=lr)