In MRI, automatic estimation of main (B0) and RF (B1) field maps from the scanned images will help daily quality assurance and field corrected reconstruction. In this paper, a novel approach based on deep learning technique is presented to estimate B0 and B1 maps from the scanned images. A modified version of stacked convolutional encoder with random skip connections deep learning network is constructed. Two separate networks are used to estimate B0 and B1 maps individually. The networks are trained and tested with phantom images. The results show that the estimated maps are comparable to the actual field maps. Automatic map estimation based on deep learning approach is the first step towards achieving daily quality assurance and field correction from the regular scanned images.