DescriptionNext generation HPC schedulers will rely heavily on accurate information about resource usage of submitted jobs. The information provided by users is often inaccurate and previous prediction models, which rely on parsed job script features, fail to accurately predict for all HPC jobs. We propose a new representation of job scripts and inclusion of application input decks for resource usage predictions with a neural network. Our contributions are a method for representing job scripts as image-like data, an automated method for predicting job resource usage from job script images and input deck features, and validation of our methods with real HPC data. We demonstrate that when job scripts for an application are very similar, our method performs better than other methods. We observe an average decrease in error of 2 node-hours compared to state of the art methods.