caffe训练模型中生成的caffemodel和solverstate的区别

以下答案来自StackOverflow，原最佳答案是个鬼，简直无智。为了方便后来人特转载，就不翻译了，没什么难理解的。
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.

下面是一些定义及作用： Training set: A set of examples used for learning, which is to fit the parameters [i.e., weights] of the classifier. 训练集是用来学习的样本集，通过匹配一些参数来建立一个分类器 Validation set: A set of exa。