Solver可理解為模型舵手。因為它控制著模型的訓練和測試階段，并規定了學習的方法和學習的參數。
The solver orchestrates （安排）model optimization by coordinating（協調） the network’s forward inference and backward gradients to form（產生） parameter updates that attempt to improve the loss.
The Solver optimizes a model by：
（1）calls network forward to compute the output and loss（前向）
（2）calls network backward to compute the gradients（反向）
（3）incorporates the gradients into parameter updates according to the solver method （權重更新）
（4）updates the solver state according to learning rate, history, and method（分離解耦）

The Caffe solvers mathod are:

Stochastic Gradient Descent (type: "SGD"),#隨機梯度下降 AdaDelta (type: "AdaDelta"), Adaptive Gradient (type: "AdaGrad"), Adam (type: "Adam"), Nesterov’s Accelerated Gradient (type: "Nesterov") and RMSprop (type: "RMSProp")

The solver 作用
1. 參數字典
2. 迭代優化參數
3. 周期性的得到測試結果
4. snapshots the model and solver state throughout the optimization（？）

Snapshotting and Resuming
The weight snapshots export the learned model while the solver snapshots allow training to be resumed from a given point.

Solver::Snapshot()和Solver::SnapshotSolverState()保存指定迭代次數中的參數并停止，相當于斷點，由Solver::Restore()和Solver::RestoreSolverState()繼續運行。
參見博文http://blog.csdn.net/u013508213/article/details/53208434#solver

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