Recent advances in deep neural networks have improved performance of several types of tasks, although some of them cannot be modeled by machine learning schemes. Neural Programmer-Interpreters (NPI) can learn algorithm when exam- ple traces of the task are given as the training input. Our previous study proposed a machine code generation scheme using NPI: scratch-pad, subprograms, and immediate-subprograms were modified to treat instructions included in RISC-V architecture appropriately. Experimental results showed that the NPI model trained using several examples that executes soft-multiplication can learn methods to perform it using RISC-V instruction sets; the accuracy reached 100% in the previous research. This study attempts to construct a transcoder that generates machine code for an architecture using training data about a task represented with machine codes for another architecture. However, there are some technical limitations to the application of the NPI model to construct a transcoder. To address these problems, this study proposed a novel training scheme for the NPI model and tried to generate programs composed of RISC-V instructions from the NPI model trained using machine code for x86 64 instructions. Evaluating the trained model by diagnosing the output sequence from the NPI model, the RISC-V instructions were correctly replaced with x86 64 instructions. However, the arguments were not completely generated.