from deephyper.problem import NaProblem from deepspace.tabular import OneLayerFactory def create_search_space(input_shape, output_shape, **kwargs): return OneLayerFactory()(input_shape, output_shape, **kwargs) Problem = NaProblem(seed=2019) Problem.load_data(load_data) Problem.search_space(create_search_space) Problem.hyperparameters(batch_size=100, learning_rate=0.1, optimizer="adam", num_epochs=1) Problem.loss("mse") Problem.metrics(["r2"]) Problem.objective("val_r2") # Just to print your problem, to test its definition and imports in the current python environment. if __name__ == "__main__": print(Problem) model = Problem.get_keras_model([4 for _ in range(20)])
from deephyper.benchmark.nas.linearReg.load_data import load_data from deephyper.problem import NaProblem from deepspace.tabular import OneLayerSpace Problem = NaProblem() Problem.load_data(load_data) Problem.search_space(OneLayerSpace) Problem.hyperparameters(batch_size=100, learning_rate=0.1, optimizer="adam", num_epochs=1) Problem.loss("mse") Problem.metrics(["r2"]) Problem.objective("val_r2") # Just to print your problem, to test its definition and imports in the current python environment. if __name__ == "__main__": print(Problem) model = Problem.get_keras_model([1])