https://blog.csdn.net/xiaotao_1/article/details/78874336
如果learning rate很大，算法会在局部最优点附近来回跳动，不会收敛；
如果learning rate太小，算法每步的移动距离很短，就会导致算法收敛速度很慢。
所以我们可以先设置一个比较大的学习率，随着迭代次数的增加慢慢降低它。mxnet中有现成的类class，我们可以直接引用。
这里有三种mxnet.lr_scheduler。
第一种是：
mxnet.lr_scheduler.FactorScheduler(step, factor=1, stop_factor_lr=1e-08)
# Reduce the learning rate by a factor for every n steps.
# It returns a new learning rate by:
base_lr * pow(factor, floor(num_update/step))
# Parameters:
step (int) – Changes the learning rate for every n updates.
factor (float, optional) – The factor to change the learning rate.
stop_factor_lr (float, optional) – Stop updating the learning rate if it is less than this value.
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例如：
lr_sch = mxnet.lr_scheduler.FactorScheduler(step=500, factor=0.9)
model.fit(
train_iter,
eval_data=val_iter,
optimizer='sgd',
optimizer_params={'learning_rate': 0.1, 'lr_scheduler': lr_sch},
eval_metric=metric,
num_epoch=num_epoch,
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这里就表示：初始学习率是0.1 。经过500次参数更新后，学习率变为0.1×0.90.1×0.9。经过1000次参数更新之后，学习率变为0.1×0.9×0.90.1×0.9×0.9
第二种是：
class mxnet.lr_scheduler.LRScheduler(base_lr=0.01)
# Base class of a learning rate scheduler.
# A scheduler returns a new learning rate based on the number of updates that have been performed.
Parameters: base_lr (float, optional) – The initial learning rate.
__call__(num_update)
# Return a new learning rate.
# The num_update is the upper bound of the number of updates applied to every weight.
# Assume the optimizer has updated i-th weight by k_i times, namely optimizer.update(i, weight_i) is called by k_i times. Then:
num_update = max([k_i for all i])
Parameters: num_update (int) – the maximal number of updates applied to a weight.
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第三种是：
class mxnet.lr_scheduler.MultiFactorScheduler(step, factor=1)
# Reduce the learning rate by given a list of steps.
# Assume there exists k such that:
step[k] <= num_update and num_update < step[k+1]
# Then calculate the new learning rate by:
base_lr * pow(factor, k+1)
# Parameters:
step (list of int) – The list of steps to schedule a change
factor (float) – The factor to change the learning rate.
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参考：https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.lr_scheduler.LRScheduler
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作者：xiaotao_1
来源：CSDN
原文：https://blog.csdn.net/xiaotao_1/article/details/78874336
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