Proton density fat fraction (PDFF) and $$$R_2^*$$$, key biomarkers associated to liver disease, can be obtained by solving the water/fat separation problem. Convolutional Neural Networks (CNN) have been proposed for solving this problem. However, proposed solutions have not achieved accurate $$$R_2^*$$$ mapping. We introduce MDWF-Net, a CNN model for computing high quality water-fat, $$$R_2^*$$$ and field mapping from abdominal acquisitions. The results were evaluated considering error and structural similarity and compared against a U-Net. We also evaluated ROIs in the liver for PDFF and $$$R_2^*$$$. The proposed MDWF-Net overperforms the original U-Net, especially for $$$R_2^*$$$ maps, even with fewer echoes.