Neural Architecture Search Framework
NORD is a NAS research framework that aims to provide tools in order to make the implementation and comparison of neural architecture design methods fair and straightforward.
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Design Algorithms: Designing neural network specifications
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Descriptors: Making the programmatical generation of nodes and connections more straigth-forward.
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Evaluators: Evaluating the quality of descriptors' networks.
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Environments: Managing the distributed execution of Evaluators.
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PyTorch and Torchvision (https://pytorch.org/)
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NetworkX
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Tensorflow 1.15 for the NASBench-101 benchmark dataset
- descriptor_example.py Example, usage of NeuralDescriptor and NeuralNet classes
- local_evaluator_example.py, Example usage of LocalEvaluator, which evaluates the given architecture on various datasets.
- nasbench_evaluator_example.py, Example usage of BenchmarkEvaluator,which evaluates the given architecture in NASBench-101.
- reproducible_genetic_algorithm_example_cifar10.py, Example of a reproducible simple genetic algorithm based on DeepNEAT.
- custom_dataset_example.py, Example of evaluating on a custom dataset