def test_greedy_searcher_mlp(_, _1, _2): train_data, test_data = get_classification_data_loaders_mlp() clean_dir(TEST_TEMP_DIR) generator = GreedySearcher(3, (28,), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy, loss=classification_loss, generators=[MlpGenerator, MlpGenerator]) for _ in range(2): generator.search(train_data, test_data) clean_dir(TEST_TEMP_DIR) assert len(generator.history) == 2
def test_greedy_searcher_mlp(_, _1, _2): train_data, test_data = get_classification_data_loaders_mlp() clean_dir(TEST_TEMP_DIR) generator = GreedySearcher(3, (28,), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy, loss=classification_loss, generators=[MlpGenerator, MlpGenerator]) for _ in range(2): generator.search(train_data, test_data) clean_dir(TEST_TEMP_DIR) assert len(generator.history) == 2
def test_bayesian_searcher_mlp(_, _1, _2): train_data, test_data = get_classification_data_loaders_mlp() clean_dir(TEST_TEMP_DIR) generator = BayesianSearcher(3, (28,), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy, loss=classification_loss, generators=[MlpGenerator, MlpGenerator]) Constant.N_NEIGHBOURS = 1 Constant.T_MIN = 0.8 for _ in range(2): generator.search(train_data, test_data) clean_dir(TEST_TEMP_DIR) assert len(generator.history) == 2
def test_bayesian_searcher_mlp(_, _1, _2): train_data, test_data = get_classification_data_loaders_mlp() clean_dir(TEST_TEMP_DIR) generator = Searcher(3, (28,), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy, loss=classification_loss, generators=[MlpGenerator, MlpGenerator]) Constant.N_NEIGHBOURS = 1 Constant.T_MIN = 0.8 for _ in range(2): generator.search(train_data, test_data) clean_dir(TEST_TEMP_DIR) assert len(generator.history) == 2
def test_bayesian_searcher_mlp(_, _1, _2): train_data, test_data = get_classification_data_loaders_mlp()