Geological facies modeling is a key component in exploration and characterization of subsurface reservoirs. While traditional geostatistical approaches are still commonly used nowadays, deep learning is gaining a lot of attention within geoscientific community for generating subsurface models, as a result of recent advance of computing powers and increasing availability of training data sets. This work presents a deep learning approach for geological facies modeling based on generative adversarial networks (GANs) combined with training-image-based simulation. In a typical application of learned networks, all neural network parameters are fixed after training, and the uncertainty in the trained model cannot be analyzed. To address this problem, a Bayesian GANs (BGANs) approach is proposed to create facies models. In this approach, a probability distribution is assigned to the neural parameters of the BGANs. Only neural parameters of the generator in BGANs are assigned with a probability function, and the ones in the discriminator are treated as fixed. Random samples are then drawn from the posterior distribution of neural parameters to simulate subsurface facies models. The proposed approach is applied to the two different geological depositional scenarios, fluvial channels and carbonate mounds, and the generated models reasonably capture the variability of the training/testing data. Meanwhile, the model uncertainty of learned networks is readily accessible. To fully sample the spatial distribution in the training image set, a large collection of samples of network parameters is required to be drawn from the posterior distribution, thus significantly increasing computational cost.