'powerful', 'relaxing', 'romantic', 'sad', 'sexy', 'slow', 'soft', 'upbeat', 'uplifting', ], ), ] pipeline = Pipeline([ ('model', Ensemble( base_estimator=CRNNModel(dataloader=dataloader), label_splits=label_splits, epochs=20, )), ]) evaluator = ModelCallbackWrapper( FixedSplitEvaluator(**common.fixed_split_params()), lambda model: common.store_prediction(model, dataloader), ) result_handlers = [ dbispipeline.result_handlers.print_gridsearch_results, ]
np.arange(0, 28, 1), np.arange(28, 56, 1), ] pipeline = Pipeline([ ('model', Ensemble( base_estimator=CNNModel( dataloader=dataloader, block_sizes=[ 32, 32, 64, 64, 64, ], ), label_splits=label_splits, epochs=20, )), ]) evaluator = ModelCallbackWrapper( FixedSplitEvaluator(**common.fixed_split_params()), lambda model: common.store_prediction(model, dataloader, 'final_'), ) result_handlers = [ dbispipeline.result_handlers.print_gridsearch_results, ]