Source code for mindspore.train.callback._lr_scheduler_callback

# Copyright 2020 Huawei Technologies Co., Ltd
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"""LearningRateScheduler Callback class."""

import math
import numpy as np

from mindspore import log as logger
import mindspore.common.dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.train.callback._callback import Callback
from mindspore.ops import functional as F


[docs]class LearningRateScheduler(Callback): """ Change the learning_rate during training. Args: learning_rate_function (Function): The function about how to change the learning rate during training. Examples: >>> from mindspore.train.callback import LearningRateScheduler >>> import mindspore.nn as nn >>> from mindspore.train import Model ... >>> def learning_rate_function(lr, cur_step_num): ... if cur_step_num%1000 == 0: ... lr = lr*0.1 ... return lr ... >>> lr = 0.1 >>> momentum = 0.9 >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> optim = nn.Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum) >>> model = Model(net, loss_fn=loss, optimizer=optim) ... >>> dataset = create_custom_dataset("custom_dataset_path") >>> model.train(1, dataset, callbacks=[LearningRateScheduler(learning_rate_function)], ... dataset_sink_mode=False) """ def __init__(self, learning_rate_function): super(LearningRateScheduler, self).__init__() self.learning_rate_function = learning_rate_function def step_end(self, run_context): cb_params = run_context.original_args() arr_lr = cb_params.optimizer.learning_rate.asnumpy() lr = float(np.array2string(arr_lr)) new_lr = self.learning_rate_function(lr, cb_params.cur_step_num) if not math.isclose(lr, new_lr, rel_tol=1e-10): F.assign(cb_params.optimizer.learning_rate, Tensor(new_lr, mstype.float32)) logger.info(f'At step {cb_params.cur_step_num}, learning_rate change to {new_lr}')