forked from huawei/mindspore2022
69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""LearningRateScheduler Callback class."""
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import math
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import numpy as np
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from mindspore import log as logger
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import mindspore.common.dtype as mstype
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from mindspore.common.tensor import Tensor
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from mindspore.train.callback._callback import Callback
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from mindspore.ops import functional as F
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class LearningRateScheduler(Callback):
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"""
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Change the learning_rate during training.
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Args:
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learning_rate_function (Function): The function about how to change the learning rate during training.
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Examples:
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>>> from mindspore.train.callback import LearningRateScheduler
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>>> import mindspore.nn as nn
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>>> from mindspore.train import Model
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...
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>>> def learning_rate_function(lr, cur_step_num):
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... if cur_step_num%1000 == 0:
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... lr = lr*0.1
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... return lr
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...
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>>> lr = 0.1
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>>> momentum = 0.9
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>>> net = Net()
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>>> loss = nn.SoftmaxCrossEntropyWithLogits()
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>>> optim = nn.Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum)
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>>> model = Model(net, loss_fn=loss, optimizer=optim)
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...
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>>> dataset = create_custom_dataset("custom_dataset_path")
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>>> model.train(1, dataset, callbacks=[LearningRateScheduler(learning_rate_function)],
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... dataset_sink_mode=False)
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"""
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def __init__(self, learning_rate_function):
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super(LearningRateScheduler, self).__init__()
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self.learning_rate_function = learning_rate_function
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def step_end(self, run_context):
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cb_params = run_context.original_args()
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arr_lr = cb_params.optimizer.learning_rate.asnumpy()
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lr = float(np.array2string(arr_lr))
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new_lr = self.learning_rate_function(lr, cb_params.cur_step_num)
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if not math.isclose(lr, new_lr, rel_tol=1e-10):
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F.assign(cb_params.optimizer.learning_rate, Tensor(new_lr, mstype.float32))
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logger.info(f'At step {cb_params.cur_step_num}, learning_rate change to {new_lr}')
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