Automated programming is one of the central challenges of intelligent software. Learning program by program execution traces or input output pairs are typical automatic programming research methods, but these methods can not bridge the gap between normal program elements and neural network components, can not absorb programing experience as input, and lack of programming control interface. This paper presents a hybrid programming model that seamlessly combines advanced programming language with neural network components. The program is composed of a mixture of elements from high-level programming language and neural network component, in which the language describes the sketch to provide experience information, with the key complex parts placed with undetermined and learnable neural network components. The program runs on differentiable abstraction machines to generate its continuous differentiable computational graph representation. Then, input-output pairs are used to train the graph by differentiable optimization method to learn to generate the complete program automaticly. This programming model provides an automatic program generation method which can combine programmer experience with neural network self-learning, bridges the gap between elements from programming language and neural network, which integrate the advantages of procedural and neural network programming, the complex details are automatically generated by neural networks to reduce the difficulty or workload of programming. Experience input is heuristic-helpful to the learning of undetermined parts and provides an input interface for reusing valuable programming experience accumulated over a long period of time.