# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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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
[文档]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:
>>> import numpy as np
>>> from mindspore import Model
>>> from mindspore.train.callback import LearningRateScheduler
>>> import mindspore.nn as nn
>>> from mindspore import dataset as ds
...
>>> 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 = nn.Dense(10, 5)
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
>>> optim = nn.Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum)
>>> model = Model(net, loss_fn=loss, optimizer=optim)
...
>>> data = {"x": np.float32(np.random.rand(64, 10)), "y": np.random.randint(0, 5, (64,))}
>>> dataset = ds.NumpySlicesDataset(data=data).batch(32)
>>> 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
[文档] def step_end(self, run_context):
"""
Change the learning_rate at the end of step.
Args:
run_context (RunContext): Include some information of the model.
"""
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}')