model.fit 早停
时间: 2024-03-11 12:25:00 浏览: 29
Model.fit的早停是指在训练神经网络模型时,使用早停法来避免过度训练。早停法是通过监控模型的损失函数的梯度是否下降来判断是否停止训练。当损失函数的梯度不再下降时,早停法会停止训练,从而保持最后一次下降时的最优参数。具体实现时,可以使用Keras中的callbacks模块中的EarlyStopping类来设置早停法。在model.fit函数中,通过callbacks参数传入EarlyStopping对象即可实现早停。例如,可以使用EarlyStopping(monitor='loss', patience=3)来设置监控损失函数的变化,并在连续3次epochs中损失函数没有下降时停止训练。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [在keras中model.fit_generator()和model.fit()的区别说明](https://download.csdn.net/download/weixin_38738977/12850766)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
- *2* [keras的EarlyStopping使用与技巧](https://blog.csdn.net/wuruivv/article/details/109372233)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
- *3* [机器学习的早停法(EarlyStopping) sklearn实现](https://blog.csdn.net/weixin_41115760/article/details/122063958)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
[ .reference_list ]