Update plots for NATS-Bench
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@ -33,7 +33,7 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
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alg2name['REA'] = 'R-EA-SS3'
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alg2name['REINFORCE'] = 'REINFORCE-0.01'
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alg2name['RANDOM'] = 'RANDOM'
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alg2name['BOHB'] = 'BOHB'
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# alg2name['BOHB'] = 'BOHB'
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for alg, name in alg2name.items():
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alg2path[alg] = os.path.join(ss_dir, dataset, name, 'results.pth')
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assert os.path.isfile(alg2path[alg]), 'invalid path : {:}'.format(alg2path[alg])
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@ -76,11 +76,18 @@ y_max_s = {('cifar10', 'tss'): 94.5,
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('ImageNet16-120', 'tss'): 44,
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('ImageNet16-120', 'sss'): 46}
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x_axis_s = {('cifar10', 'tss'): 200,
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('cifar10', 'sss'): 200,
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('cifar100', 'tss'): 400,
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('cifar100', 'sss'): 400,
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('ImageNet16-120', 'tss'): 1200,
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('ImageNet16-120', 'sss'): 600}
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name2label = {'cifar10': 'CIFAR-10',
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'cifar100': 'CIFAR-100',
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'ImageNet16-120': 'ImageNet-16-120'}
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def visualize_curve(api, vis_save_dir, search_space, max_time):
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def visualize_curve(api, vis_save_dir, search_space):
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vis_save_dir = vis_save_dir.resolve()
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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@ -89,28 +96,36 @@ def visualize_curve(api, vis_save_dir, search_space, max_time):
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LabelSize, LegendFontsize = 16, 16
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def sub_plot_fn(ax, dataset):
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xdataset, max_time = dataset.split('-T')
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alg2data = fetch_data(search_space=search_space, dataset=dataset)
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alg2accuracies = OrderedDict()
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total_tickets = 150
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time_tickets = [float(i) / total_tickets * max_time for i in range(total_tickets)]
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time_tickets = [float(i) / total_tickets * int(max_time) for i in range(total_tickets)]
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colors = ['b', 'g', 'c', 'm', 'y']
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ax.set_xlim(0, 200)
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ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])
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ax.set_xlim(0, x_axis_s[(xdataset, search_space)])
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ax.set_ylim(y_min_s[(xdataset, search_space)],
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y_max_s[(xdataset, search_space)])
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for idx, (alg, data) in enumerate(alg2data.items()):
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print('plot alg : {:}'.format(alg))
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print('{:} plot alg : {:}'.format(time_string(), alg))
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accuracies = []
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for ticket in time_tickets:
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accuracy = query_performance(api, data, dataset, ticket)
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accuracy = query_performance(api, data, xdataset, ticket)
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accuracies.append(accuracy)
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alg2accuracies[alg] = accuracies
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ax.plot([x/100 for x in time_tickets], accuracies, c=colors[idx], label='{:}'.format(alg))
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ax.set_xlabel('Estimated wall-clock time (1e2 seconds)', fontsize=LabelSize)
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ax.set_ylabel('Test accuracy on {:}'.format(name2label[dataset]), fontsize=LabelSize)
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ax.set_title('Searching results on {:}'.format(name2label[dataset]), fontsize=LabelSize+4)
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ax.set_ylabel('Test accuracy on {:}'.format(name2label[xdataset]), fontsize=LabelSize)
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ax.set_title('Searching results on {:}'.format(name2label[xdataset]), fontsize=LabelSize+4)
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ax.legend(loc=4, fontsize=LegendFontsize)
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fig, axs = plt.subplots(1, 3, figsize=figsize)
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datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
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# datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
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if search_space == 'tss':
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datasets = ['cifar10-T20000', 'cifar100-T40000', 'ImageNet16-120-T120000']
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elif search_space == 'sss':
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datasets = ['cifar10-T20000', 'cifar100-T40000', 'ImageNet16-120-T60000']
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else:
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raise ValueError('Unknown search space: {:}'.format(search_space))
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for dataset, ax in zip(datasets, axs):
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sub_plot_fn(ax, dataset)
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print('sub-plot {:} on {:} done.'.format(dataset, search_space))
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@ -121,13 +136,12 @@ def visualize_curve(api, vis_save_dir, search_space, max_time):
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='NATS-Bench', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser = argparse.ArgumentParser(description='NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/nas-algos', help='Folder to save checkpoints and log.')
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parser.add_argument('--search_space', type=str, choices=['tss', 'sss'], help='Choose the search space.')
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parser.add_argument('--max_time', type=float, default=20000, help='The maximum time budget.')
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args = parser.parse_args()
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save_dir = Path(args.save_dir)
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api = create(None, args.search_space, fast_mode=True, verbose=False)
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visualize_curve(api, save_dir, args.search_space, args.max_time)
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visualize_curve(api, save_dir, args.search_space)
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@ -167,7 +167,7 @@ if __name__ == '__main__':
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parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
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args = parser.parse_args()
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api = create(None, args.search_space, fast_mode=True, verbose=False)
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api = create(None, args.search_space, fast_mode=False, verbose=False)
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args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space),
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'{:}-T{:}'.format(args.dataset, args.time_budget), 'BOHB')
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@ -14,55 +14,24 @@ alg_type=$1
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if [ "$alg_type" == "mul" ]; then
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# datasets="cifar10 cifar100 ImageNet16-120"
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run_four_algorithms(){
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dataset=$1
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search_space=$2
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time_budget=$3
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python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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python ./exps/NATS-algos/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
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python ./exps/NATS-algos/bohb.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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}
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# The topology search space
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dataset="cifar10"
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search_space="tss"
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time_budget="20000"
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python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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python ./exps/NATS-algos/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
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python ./exps/NATS-algos/bohb.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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dataset="cifar100"
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search_space="tss"
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time_budget="40000"
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python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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python ./exps/NATS-algos/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
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python ./exps/NATS-algos/bohb.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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dataset="ImageNet16-120"
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search_space="tss"
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time_budget="120000"
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python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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python ./exps/NATS-algos/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
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python ./exps/NATS-algos/bohb.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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run_four_algorithms "cifar10" "tss" "20000"
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run_four_algorithms "cifar100" "tss" "40000"
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run_four_algorithms "ImageNet16-120" "tss" "120000"
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# The size search space
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dataset="cifar10"
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search_space="sss"
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time_budget="20000"
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python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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python ./exps/NATS-algos/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
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python ./exps/NATS-algos/bohb.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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dataset="cifar100"
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search_space="sss"
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time_budget="40000"
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python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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python ./exps/NATS-algos/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
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python ./exps/NATS-algos/bohb.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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dataset="ImageNet16-120"
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search_space="tss"
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time_budget="60000"
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python ./exps/NATS-algos/reinforce.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --learning_rate 0.01
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python ./exps/NATS-algos/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/NATS-algos/random_wo_share.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget}
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python ./exps/NATS-algos/bohb.py --dataset ${dataset} --search_space ${search_space} --time_budget ${time_budget} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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run_four_algorithms "cifar10" "sss" "20000"
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run_four_algorithms "cifar100" "sss" "40000"
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run_four_algorithms "ImageNet16-120" "sss" "60000"
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# python exps/experimental/vis-bench-algos.py --search_space tss
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# python exps/experimental/vis-bench-algos.py --search_space sss
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else
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