n_init=[10], epsilon=[1e-05], patience=[10], ) params_architecture: Mapping[str, Sequence[Any]] = dict( size_hidden_layers=[(2, )], activation=[tanh_classification], activation_hidden=[relu], eta=[0.1, 0.01, 0.001], alpha=[0, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], alambd=[0, 0.0001, 0.001, 0.01], eta_decay=[0], ) cv_params: Mapping[str, Any] = dict( cv=3, error_calculator=ErrorCalculator.MSE, to_shuffle=True, ) grid_search_results = grid_search( train_data, params_nn=params_nn, params_architecture=params_architecture, cv_params=cv_params, n_jobs=8, ) write_on_file(grid_search_results, 'results/monk1-mse-low_eta')
learning_algorithm=[batch], epochs_limit=[1000], n_init=[1], epsilon=[1e-05], patience=[10], ) params_architecture: Mapping[str, Sequence[Any]] = dict( size_hidden_layers=[(2, )], activation=[tanh_classification], activation_hidden=[relu], eta=[0.1], alpha=[0.7], alambd=[0.0001], eta_decay=[0], ) validation_params: Mapping[str, Any] = dict( validation_set=validation_set, error_calculator=ErrorCalculator.MSE, ) grid_search_results = grid_search( train_set, params_nn=params_nn, params_architecture=params_architecture, validation_params=validation_params, n_jobs=8, ) write_on_file(grid_search_results, 'results/monk1_choose_seed')