Extended Data Fig. 5: Neural network’s architecture.
The Actor and Model networks are comprised of two sub-networks, (Areal, Aimag) and (Mreal, Mimag) respectively, to cope with the real and imaginary parts of input-output fields. All sub-networks are fully-connected. Thereby, the input images to the Actor network is first flattened out (from size 200×200 pixels to 40000×1 vectors) and then fed to the sub-networks Areal and Aimag (input nodes 40000, output nodes 2601). In the training step, the output vectors (size 2601×1) of Areal and Aimag are passed on to Mreal and Mimag (input nodes 2601, output nodes 40000), respectively. The virtual neural network output image of the target image (size 200×200) is then produced at the output of the Model. Once trained, the output vectors of the Actor network can be directly reshaped to produce the real and imaginary parts of the SLM images (reshaping from size 2601×1 to 51×51 pixels). If these SLM images are uploaded to the SLM and sent through the fiber, they produce projected images on the camera that are similar to the target images.
