http://blog.csdn.net/wang4959520/article/details/51831637

我們在使用caffe訓練過程中會生成.caffemodel和.solverstate文件，一個是模型文件，一個是中間狀態文件（生成多少個取決于你自己設定的snapshot）。當訓練過程中斷，你想繼續運行數據學習，此時只需要調用.solverstate文件即可。

使用方式代碼，我使用的是.sh直接運行，配置和官方給的文件train_caffenet.sh差不多，稍微添加點內容就可以了。

[plain]?view plain?copy ?

./build/tools/caffe?train?\??

?????--solver=examples/test/solver.prototxt?--snapshot=examples/test/test_100000.solverstate??

上述配置根據個人文件路徑實際情況相應修改即可。

The solverstate file, as its name conveys, stores the state of the solver and not any information related to classification results. The model is saved as caffemodel file, which you can use to obtain classification results for your data. If you want to fine-tune your network you may use a pre-trained caffemodel file. This will save time as your network does not need to learn from scratch. But, in case your present training needs to be halted, due to a power cut or an unexpected reboot, you may resume your training form the previous snapshot of the solverstate. The difference between using the solverstate and the caffemodel files is that the former allows you to complete your training in the pre-determined manner while the latter may require changes in certain training parameters such as the maximum number of iterations.

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