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npy file and training images for phase mask optimization.
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Walk through the first sections of HybridNNMaskOpt.ipynb until "Extract optimized phase mask", making sure to save the tiled psf.For similar conditions as in paper results, use: There is much more code than necessary in this file from our experimenting. Train a network with hybrid_cifar10.py.Walk through ONNMaskOpt.ipynb from "Visualization of phase mask optimization" and plug in the checkpoint from onn_maskopt.py.Įxample to optimize a hybrid two-layer CNN for CIFAR-10 (rough outline):.You can use the sample psf in the assets folder or use the one you save from the ONNMaskOpt.ipynb walkthrough onn_maskopt.py: optimizes a phase mask to correspond to a pre-computed PSF.You can use the saved checkpoint folder we link below, or the checkpoint from running onn_quickdraw-16-tiled.py Walk through ONNMaskOpt.ipynb until the "Visualization of phase mask optimization" section.
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onn_quickdraw-16-tiled.py: optimizes a single-layer tiled kernel PSF model for the quickdraw-16 dataset.Download quickdraw-16 training dataset (see below) into assets folder.
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Our code was run with Python 3.5.5 and Tensorflow 1.4.0.Įxample to optimize a single-layer optical correlator for QuickDraw-16: Note 2: The Tensorflow fft2 function may also have changed in more recent updates, which has caused some differences in optimization results. Note 1: If you have a more up-to-date version of scipy, you may need to change the function to imageio.imwrite.
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