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Deep Neural Networks (DNNs) are intensively used to solve a wide variety of complex problems. Although powerful, such systems re- quire manual configuration and tuning. To this end, we view DNNs as configurable systems and propose an end-to-end framework that allows the configuration, evaluation and automated search for DNN architectures. Our FM…
yamizi/FeatureNet
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# Neural Architecture Search with Feature Models ## Prerequisite ### Python The code should be run using python 3.5, Tensorflow 1.12.0, Keras 2.2.4, PIL, Validators ### Tensorflow ```bash sudo pip install tensorflow ``` if you have gpu, ```bash pip install tensorflow-gpu ``` ### Keras Keras is included in the requirements. Install all the requirements of the file To set Keras backend to be tensorflow (two options): ```bash 1. Modify ~/.keras/keras.json by setting "backend": "tensorflow" 2. KERAS_BACKEND=tensorflow python gen_diff.py ``` ## First run run the example ```bash python ./full.py ``` It will load the base product of a feature model (for instance lenet5.json) This file is generated with Feature model product parser. ## Building a .json product Use the script featuremode_to_json.py to convert a product generated by PLEDGE (https://github.com/christopherhenard/pledge) into a json that can be parsed by the generator
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Deep Neural Networks (DNNs) are intensively used to solve a wide variety of complex problems. Although powerful, such systems re- quire manual configuration and tuning. To this end, we view DNNs as configurable systems and propose an end-to-end framework that allows the configuration, evaluation and automated search for DNN architectures. Our FM…
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