Deep learning has great potential to be useful for ocean acoustics problems. However, uncertainty in an ocean environment must be taken into account, because it affects the ability of trained machine learning models to generalize. Because ocean measurements are costly, we are using a laboratory water tank to help develop our deep learning neural networks. In addition, using a water tank in a lab increases some level of control over the amount of uncertainty in data used to train neural networks. A residual convolutional neural network architecture is trained to identify source-receiver range in an underwater tank environment at room temperature. The model is then tested on data taken with variable water temperature. For comparison, two different source signals are used (sine waves and linear chirps) to analyze the usefulness of broadband sounds. These tests explore how environmental uncertainty impacts source localization and will lead to insights on how to develop robust machine learning for ocean acoustics problems. [Work supported by the Office of Naval Research.]