神经网络中训练数据集、验证数据集和测试数据集的区别
2015-08-26 09:32
621 查看
whats the difference between train, validation and test set, in neural networks?
Answer:
The training and validation sets are used during training.
for each epoch
for each training data instance
propagate error through the network
adjust the weights
calculate the accuracy over training data
for each validation data instance
calculate the accuracy over the validation data
if the threshold validation accuracy is met
exit training
else
continue training
Once you're finished training, then you run against your testing set and verify that the accuracy is sufficient.
Training Set: this data
set is used to adjust the weights on the neural network.
Validation Set: this data
set is used to minimize overfitting. You're not adjusting the weights of the network with this data set, you're just verifying that any increase in accuracy over the training data set actually yields an increase in accuracy over a data set that has not been
shown to the network before, or at least the network hasn't trained on it (i.e. validation data set). If the accuracy over the training data set increases, but the accuracy over then validation data set stays the same or decreases, then you're overfitting
your neural network and you should stop training.
Testing Set: this data
set is used only for testing the final solution in order to confirm the actual predictive power of the network.
转自:http://stackoverflow.com/questions/2976452/whats-the-diference-between-train-validation-and-test-set-in-neural-networks
Answer:
The training and validation sets are used during training.
for each epoch
for each training data instance
propagate error through the network
adjust the weights
calculate the accuracy over training data
for each validation data instance
calculate the accuracy over the validation data
if the threshold validation accuracy is met
exit training
else
continue training
Once you're finished training, then you run against your testing set and verify that the accuracy is sufficient.
Training Set: this data
set is used to adjust the weights on the neural network.
Validation Set: this data
set is used to minimize overfitting. You're not adjusting the weights of the network with this data set, you're just verifying that any increase in accuracy over the training data set actually yields an increase in accuracy over a data set that has not been
shown to the network before, or at least the network hasn't trained on it (i.e. validation data set). If the accuracy over the training data set increases, but the accuracy over then validation data set stays the same or decreases, then you're overfitting
your neural network and you should stop training.
Testing Set: this data
set is used only for testing the final solution in order to confirm the actual predictive power of the network.
转自:http://stackoverflow.com/questions/2976452/whats-the-diference-between-train-validation-and-test-set-in-neural-networks
相关文章推荐
- 用Python从零实现贝叶斯分类器的机器学习的教程
- bp神经网络及matlab实现
- 也谈 机器学习到底有没有用 ?
- 量子计算机编程原理简介 和 机器学习
- 基于神经网络的预测模型
- 10个关于人工智能和机器学习的有趣开源项目
- 机器学习实践中应避免的7种常见错误
- 机器学习书单
- 北美常用的机器学习/自然语言处理/语音处理经典书籍
- 如何提升COBOL系统代码分析效率
- 支持向量机(SVM)算法概述
- 神经网络初步学习手记
- 开始spark之旅
- spark的几点备忘
- 关于机器学习的学习笔记(一):机器学习概念
- 关于机器学习的学习笔记(二):决策树算法
- 关于机器学习的学习笔记(三):k近邻算法
- 长期招聘:自然语言处理工程师
- 长期招聘:个性化推荐
- 人工智能扫盲漫谈篇 & 2018年1月新课资源推荐