Lens-free inline holography contains zero-order image noise and twin image noise. Methods based on the Fresnel diffraction model can suppress these noise, but require many lens-free images. To resolve this problem, this paper proposes a lens-free imaging method based on generative adversarial networks (GAN). First, the defocusing distance of a lens-free image is calculated under partially coherent illumination, and the object plane image with zero-order image and twin image is reconstructed through back diffraction propagation according to the defocusing distance. Next, the object plane image is registered with commercial microscope images which are the gold standard. The registered images are taken as the training inputs of the GAN. Finally, the trained kernel function of the GAN is used for reconstructing the lens-free images, thus obtaining clear target images. The experimental results show that the proposed method can effectively suppress the zero-order image and twin image and significantly improve (up to 4×commercial microscope objective) the contrast and clarity of the image. Because the proposed method requires only a single lens-free image and omits Fourier transforms and other complex operations in the image reconstruction stage, it greatly shortens the imaging time. The proposed method requires fewer training data, better converges the loss function, and has higher processing efficiency than the method based on convolutional neural networks (CNN).