| abstract |
Improved training of optical neural networks is provided. In one example: 1) we choose input and target vectors, we program those into an input vector generator and a measurement unit, respectively, we turn on the optical input source power, and we monitor the electrical signal representing the cost function. 2) we can then modulate two or more controllable elements inside the optical network at different frequencies and look for the size and sign of the corresponding distinct AC variations in the measured cost function, simultaneously giving us the gradients with respect to each element. |