Symbolic PathFinder (SPF) is a tool that uses Java PathFinder at the back-end and can extract path conditions for a program by executing the program symbolically. Users can specify which variables they want to make symbolic and which methods to run. The Pre-GSoC implementation of Symbolic PathFinder allows only to run symbolic execution with fixed neural network architecture, i.e., MNIST0. We have added another implementation which is more generic and supports symbolic execution for all types of neural network architectures. We evaluated our implementation using a variety of architectures, namely MNIST0, MNIST1, MNIST2, and CIFAR. Evaluation results verified the correctness of the implementation. We believe that this GSoC implementation will make it easier for researchers to analyze a wide variety of neural networks. This will enable them to improve the neural network performance as well as help in identifying the vulnerabilities in already existing neural network architectures.