theano_Theano-结论
theano
Theano-结论 (Theano - Conclusion)
The Machine Learning model building involves intensive and repetitive computations involving tensors. These require intensive computing resources. As a regular compiler would provide the optimizations at the local level, it does not generally produce a fast execution code.
机器学习模型的建立涉及涉及张量的密集且重复的计算。 这些需要大量的计算资源。 由于常规编译器将在本地级别提供优化，因此通常不会生成快速执行代码。
Theano first builds a computational graph for the entire computation. As the whole picture of computation is available as a single image during compilation, several optimization techniques can be applied during pre-compilation and that’s what exactly Theano does. It restructures the computational graph, partly converts it into C, moves shared variables to GPU, and so on to generate a very fast executable code. The compiled code is then executed by a Theano function which just acts as a hook for injecting the compiled code into the runtime. Theano has proved its credentials and is widely accepted in both academics and industry.
Theano首先为整个计算构建一个计算图。 由于在编译过程中可以将整个计算图作为单个图像使用，因此在预编译期间可以应用多种优化技术，而Theano正是这样做的。 它重组计算图，部分将其转换为C，将共享变量移至GPU，等等，以生成非常快速的可执行代码。 然后由Theano 函数执行编译后的代码，该函数仅用作将编译后的代码注入运行时的挂钩。 Theano证明了其信誉，并在学者和行业中被广泛接受。
翻译自: https://www.tutorialspoint.com/theano/theano_conclusion.htm
theano