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Customized architectures are supported through the `--arch` flag once specified in `genotypes.py`.
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The CIFAR-10 result at the end of training is subject to variance due to the non-determinism of cuDNN back-prop kernels. _It would be misleading to report the result of only a single run_. By training our best cell from scratch, one should expect the average test accuracy of 10 independent runs to fall in the range of 2.76 +/- 0.09% with high probability.
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The CIFAR-10 result at the end of training is subject to variance due to the non-determinism of cuDNN back-prop kernels. _It would be misleading to report the result of only a single run_. By training our best cell from scratch, one should expect the average test error of 10 independent runs to fall in the range of 2.76 +/- 0.09% with high probability.
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