GedankenNet is a self-supervised AI model that does not require real data or experimental subjects for feeding. Instead, it learns through thought experiments and physical laws, exhibiting excellent external generalization capabilities. This model holds immense potential in the field of holographic microscopy reconstruction, providing new opportunities to address problems in holography, microscopy, and computational imaging inversion. GedankenNet is trained via physical consistency loss, eliminating the need for step-by-step iterations, leading to faster reconstruction speeds and more accurate results. Research shows that GedankenNet excels in image reconstruction quality and external generalization ability.