To carry out architecture search using 2nd-order approximation, run

cd cnn && python train_search.py --unrolled     # for conv cells on CIFAR-10

Note the validation performance in this step does not indicate the final performance of the architecture. One must train the obtained genotype/architecture from scratch using full-sized models, as described in the next section.

Also be aware that different runs would end up with different local minimum. To get the best result, it is crucial to repeat the search process with different seeds and select the best cell(s) based on validation performance (obtained by training the derived cell from scratch for a small number of epochs).

To evaluate our best cells by training from scratch, run

cd cnn && python train.py --auxiliary --cutout            # CIFAR-10

Customized architectures are supported through the --arch flag once specified in genotypes.py.

Package graphviz is required to visualize the learned cells

python visualize.py DARTS

where DARTS can be replaced by any customized architectures in genotypes.py.