An increasing number of studies have utilized interactive deep learning as the analytic model of visual analytics systems for complex sensemaking tasks. In these systems, traditional interactive dimensionality reduction (DR) models are commonly utilized to build a bi-directional bridge between high-dimensional deep learning representations and low-dimensional visualizations. While these systems better capture analysts' intents in the context of human-in-the-loop interactive deep learning, traditional DR cannot support several desired properties for visual analytics, including out-of-sample extensions, stability, and real-time inference. To avoid this issue, we propose the neural design framework of semantic interaction for interactive deep learning. In our framework, we replace the traditional DR with a neural projection network and append it to the deep learning model as the task-specific output layer. Therefore, the analytic model (deep learning) and visualization method (interactive DR) form one integrated end-to-end trainable deep neural network. In order to understand the performance of the neural design in comparison to the state-of-the-art, we systematically performed two complementary studies, a human-centered qualitative case study and an algorithm-centered simulation-based quantitative experiment. The results of these studies indicate that the neural design can give semantic interaction systems substantial advantages while still keeping comparable inference ability compared to the state-of-the-art model.