Image registration is a crucial preprocessing step for many downstream analysis tasks. Existing iterative methods for affine registration are accurate but time consuming. We propose a deep learning (DL) based unsupervised affine registration algorithm that executes orders of magnitude faster when compared to conventional registration toolkits. The proposed algorithm aligns 3D volumes from the same modality (e.g. T1 vs T1-CE) as well as different modalities (e.g. T1 vs T2). We train the model and perform quantitative evaluation using a pre-registered brain MRI public dataset.