Dynamic MR image reconstruction from incomplete k-space data is an important technique for reducing its scan time. Deep learning has shown great potential in assisting this task. Nevertheless, most frameworks only adopt a final loss for network training and the intermediate results generated during the network forward pass haven't been considered for the network training. This work proposes a multi-supervised learning strategy, which constrains the frequency domain information and reconstruction results at different levels. Improved reconstruction results have been achieved with the proposed strategy.