Ejemplos de get_classification_data_loaders en Python

Ejemplo n.º 1

def test_greedy_searcher_sp(_, _1, _2, _3):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)
    searcher = GreedySearcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
                        loss=classification_loss, generators=[CnnGenerator, CnnGenerator])
    for _ in range(2):
        searcher.search(train_data, test_data)
    clean_dir(TEST_TEMP_DIR)
    assert len(searcher.history) == 2

Ejemplo n.º 2

Archivo: test_search.py Proyecto: Saiuz/autokeras

def test_greedy_searcher_sp(_, _1, _2, _3):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)
    searcher = GreedySearcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
                        loss=classification_loss, generators=[CnnGenerator, CnnGenerator])
    for _ in range(2):
        searcher.search(train_data, test_data)
    clean_dir(TEST_TEMP_DIR)
    assert len(searcher.history) == 2

Ejemplo n.º 3

def test_model_trainer_classification():
    model = CnnGenerator(3, (28, 28, 3)).generate().produce_model()
    train_data, test_data = get_classification_data_loaders()
    ModelTrainer(model,
                 train_data=train_data,
                 test_data=test_data,
                 metric=Accuracy,
                 loss_function=classification_loss,
                 verbose=True).train_model(max_iter_num=3)

Ejemplo n.º 4

Archivo: test_search.py Proyecto: karolmajek/autokeras

def test_bayesian_searcher(_, _1):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(default_test_path)
    generator = Searcher(3, (28, 28, 3), verbose=False, path=default_test_path, metric=Accuracy,
                         loss=classification_loss)
    Constant.N_NEIGHBOURS = 1
    Constant.T_MIN = 0.8
    for _ in range(2):
        generator.search(train_data, test_data)
    clean_dir(default_test_path)
    assert len(generator.history) == 2

Ejemplo n.º 5

Archivo: test_search.py Proyecto: Saiuz/autokeras

def test_bayesian_searcher_sp(_, _1, _2, _3):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)
    searcher = BayesianSearcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
                        loss=classification_loss, generators=[CnnGenerator, CnnGenerator])
    Constant.N_NEIGHBOURS = 1
    Constant.T_MIN = 0.8
    for _ in range(2):
        searcher.search(train_data, test_data)
    clean_dir(TEST_TEMP_DIR)
    assert len(searcher.history) == 2

Ejemplo n.º 6

Archivo: test_search.py Proyecto: tl-yang/autokeras

def test_bayesian_searcher_sp(_, _1, _2, _3):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)
    searcher = Searcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
                        loss=classification_loss, generators=[CnnGenerator, CnnGenerator])
    Constant.N_NEIGHBOURS = 1
    Constant.T_MIN = 0.8
    for _ in range(2):
        searcher.search(train_data, test_data)
    clean_dir(TEST_TEMP_DIR)
    assert len(searcher.history) == 2

Ejemplo n.º 7

Archivo: test_model_trainer.py Proyecto: Saiuz/autokeras

def test_model_trainer_classification():
    model = CnnGenerator(3, (28, 28, 3)).generate().produce_model()
    train_data, test_data = get_classification_data_loaders()
    ModelTrainer(model,
                 train_data=train_data,
                 test_data=test_data,
                 metric=Accuracy,
                 loss_function=classification_loss,
                 verbose=True,
                 path=TEST_TEMP_DIR).train_model(max_iter_num=3)
    clean_dir(TEST_TEMP_DIR)

Ejemplo n.º 8

Archivo: test_search.py Proyecto: hanhanshangying/autokeras

def test_out_of_memory(_, _1):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)
    searcher = Searcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
                        loss=classification_loss)
    Constant.N_NEIGHBOURS = 1
    Constant.T_MIN = 0.8
    for _ in range(4):
        searcher.search(train_data, test_data)
    clean_dir(TEST_TEMP_DIR)
    assert len(searcher.history) == 0

Ejemplo n.º 9

def test_grid_searcher(_, _1, _2):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)
    searcher = GridSearcher(3, (28, 28, 3), verbose=True, path=TEST_TEMP_DIR, metric=Accuracy,
                        loss=classification_loss, generators=[CnnGenerator, CnnGenerator])
    Constant.N_NEIGHBOURS = 1
    Constant.T_MIN = 0.8
    print(len(searcher.get_search_dimensions()))
    for _ in range(len(searcher.get_search_dimensions())):
        searcher.search(train_data, test_data)
    clean_dir(TEST_TEMP_DIR)
    assert len(searcher.history) == len(searcher.search_dimensions)

Ejemplo n.º 10

Archivo: test_search.py Proyecto: Saiuz/autokeras

def test_grid_searcher(_, _1, _2):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)
    searcher = GridSearcher(3, (28, 28, 3), verbose=True, path=TEST_TEMP_DIR, metric=Accuracy,
                        loss=classification_loss, generators=[CnnGenerator, CnnGenerator])
    Constant.N_NEIGHBOURS = 1
    Constant.T_MIN = 0.8
    print(len(searcher.get_search_dimensions()))
    for _ in range(len(searcher.get_search_dimensions())):
        searcher.search(train_data, test_data)
    clean_dir(TEST_TEMP_DIR)
    assert len(searcher.history) == len(searcher.search_dimensions)

Ejemplo n.º 11

Archivo: test_search.py Proyecto: dangviethung1998/autokeras

def test_exception_handling(_, _2):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)
    Constant.N_NEIGHBOURS = 2
    Constant.T_MIN = 0.8
    Constant.BETA = 1
    generator = BayesianSearcher(3, (28, 28, 3), verbose=True, path=TEST_TEMP_DIR, metric=Accuracy,
                                 loss=classification_loss, generators=[CnnGenerator, ResNetGenerator])
    for _ in range(3):
        generator.search(train_data, test_data)
    clean_dir(TEST_TEMP_DIR)
    assert len(generator.history) == 0

Ejemplo n.º 12

Archivo: test_model_trainer.py Proyecto: PaulGureghian1/Auto_Keras

def test_model_trainer_timout():
    model = CnnGenerator(3, (28, 28, 3)).generate().produce_model()
    timeout = 1
    train_data, test_data = get_classification_data_loaders()
    with pytest.raises(TimeoutError):
        ModelTrainer(model,
                     train_data=train_data,
                     test_data=test_data,
                     metric=Accuracy,
                     loss_function=classification_loss,
                     verbose=True,
                     path=TEST_TEMP_DIR).train_model(max_iter_num=300, timeout=timeout)
    clean_dir(TEST_TEMP_DIR)

Ejemplo n.º 13

Archivo: test_search.py Proyecto: Saiuz/autokeras

def test_out_of_memory(_, _2):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)
    Constant.N_NEIGHBOURS = 2
    Constant.SEARCH_MAX_ITER = 0
    Constant.T_MIN = 0.8
    Constant.BETA = 1
    generator = BayesianSearcher(3, (28, 28, 3), verbose=True, path=TEST_TEMP_DIR, metric=Accuracy,
                         loss=classification_loss, generators=[CnnGenerator, ResNetGenerator])
    for _ in range(3):
        generator.search(train_data, test_data)
    clean_dir(TEST_TEMP_DIR)
    assert len(generator.history) == 0

Ejemplo n.º 14

Archivo: test_backend.py Proyecto: zwcdp/autokeras

def test_model_trainer_classification():
    Backend.backend = tensorflow
    model = CnnGenerator(3, (28, 28, 3)).generate().produce_model()
    train_data, test_data = get_classification_data_loaders()
    ModelTrainer(model,
                 train_data=train_data,
                 test_data=test_data,
                 metric=Accuracy,
                 loss_function=Backend.classification_loss,
                 verbose=True,
                 path=TEST_TEMP_DIR).train_model(max_iter_num=3)
    Backend.backend = torch
    clean_dir(TEST_TEMP_DIR)

Ejemplo n.º 15

def test_bayesian_searcher(_, _1):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(default_test_path)
    generator = Searcher(3, (28, 28, 3),
                         verbose=False,
                         path=default_test_path,
                         metric=Accuracy,
                         loss=classification_loss)
    Constant.N_NEIGHBOURS = 1
    Constant.T_MIN = 0.8
    for _ in range(2):
        generator.search(train_data, test_data)
    clean_dir(default_test_path)
    assert len(generator.history) == 2

Ejemplo n.º 16

Archivo: test_search.py Proyecto: tl-yang/autokeras

def test_max_acq(_, _2):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)
    Constant.N_NEIGHBOURS = 2
    Constant.SEARCH_MAX_ITER = 0
    Constant.T_MIN = 0.8
    Constant.BETA = 1
    generator = Searcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
                         loss=classification_loss, generators=[CnnGenerator, ResNetGenerator])
    for _ in range(3):
        generator.search(train_data, test_data)
    for index1, descriptor1 in enumerate(generator.descriptors):
        for descriptor2 in generator.descriptors[index1 + 1:]:
            assert edit_distance(descriptor1, descriptor2) != 0.0

    clean_dir(TEST_TEMP_DIR)

Ejemplo n.º 17

Archivo: test_search.py Proyecto: Saiuz/autokeras

def test_max_acq(_, _2):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)
    Constant.N_NEIGHBOURS = 2
    Constant.SEARCH_MAX_ITER = 0
    Constant.T_MIN = 0.8
    Constant.BETA = 1
    generator = BayesianSearcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
                         loss=classification_loss, generators=[CnnGenerator, ResNetGenerator])
    for _ in range(3):
        generator.search(train_data, test_data)
    for index1, descriptor1 in enumerate(generator.descriptors):
        for descriptor2 in generator.descriptors[index1 + 1:]:
            assert edit_distance(descriptor1, descriptor2) != 0.0

    clean_dir(TEST_TEMP_DIR)

Ejemplo n.º 18

Archivo: test_search.py Proyecto: karolmajek/autokeras

def test_max_acq(_, _1):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(default_test_path)
    Constant.N_NEIGHBOURS = 2
    Constant.SEARCH_MAX_ITER = 0
    Constant.T_MIN = 0.8
    Constant.BETA = 1
    generator = Searcher(3, (28, 28, 3), verbose=False, path=default_test_path, metric=Accuracy,
                         loss=classification_loss)
    for _ in range(3):
        generator.search(train_data, test_data)
    for index1, descriptor1 in enumerate(generator.descriptors):
        for descriptor2 in generator.descriptors[index1 + 1:]:
            assert edit_distance(descriptor1, descriptor2, 1) != 0

    clean_dir(default_test_path)

Ejemplo n.º 19

Archivo: test_search.py Proyecto: karolmajek/autokeras

def test_export_json(_, _1):
    train_data, test_data = get_classification_data_loaders()

    clean_dir(default_test_path)
    generator = Searcher(3, (28, 28, 3), verbose=False, path=default_test_path, metric=Accuracy,
                         loss=classification_loss)
    Constant.N_NEIGHBOURS = 1
    Constant.T_MIN = 0.8
    for _ in range(3):
        generator.search(train_data, test_data)
    file_path = os.path.join(default_test_path, 'test.json')
    generator.export_json(file_path)
    import json
    data = json.load(open(file_path, 'r'))
    assert len(data['networks']) == 3
    assert len(data['tree']['children']) == 2
    clean_dir(default_test_path)
    assert len(generator.history) == 3

Ejemplo n.º 20

Archivo: test_search.py Proyecto: ychjiang/autokeras

def test_export_json(_, _1, _2):
    train_data, test_data = get_classification_data_loaders()

    clean_dir(TEST_TEMP_DIR)
    generator = Searcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
                         loss=classification_loss, generators=[CnnGenerator])
    Constant.N_NEIGHBOURS = 1
    Constant.T_MIN = 0.8
    for _ in range(3):
        generator.search(train_data, test_data)
    file_path = os.path.join(TEST_TEMP_DIR, 'test.json')
    generator.export_json(file_path)
    import json
    data = json.load(open(file_path, 'r'))
    assert len(data['networks']) == 3
    assert len(data['tree']['children']) == 2
    clean_dir(TEST_TEMP_DIR)
    assert len(generator.history) == 3

Ejemplo n.º 21

def test_max_acq(_, _1):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(default_test_path)
    Constant.N_NEIGHBOURS = 2
    Constant.SEARCH_MAX_ITER = 0
    Constant.T_MIN = 0.8
    Constant.BETA = 1
    generator = Searcher(3, (28, 28, 3),
                         verbose=False,
                         path=default_test_path,
                         metric=Accuracy,
                         loss=classification_loss)
    for _ in range(3):
        generator.search(train_data, test_data)
    for index1, descriptor1 in enumerate(generator.descriptors):
        for descriptor2 in generator.descriptors[index1 + 1:]:
            assert edit_distance(descriptor1, descriptor2, 1) != 0

    clean_dir(default_test_path)

Ejemplo n.º 22

def test_bayesian_searcher(_, _1, _2):
    train_data, test_data = get_classification_data_loaders()
    clean_dir(TEST_TEMP_DIR)

Ejemplo n.º 23

def test_bayesian_searcher_sp(_, _1, _2, _3):
    train_data, test_data = get_classification_data_loaders()

Ejemplo n.º 24

def test_greedy_searcher(_, _1, _2):
    train_data, test_data = get_classification_data_loaders()

Ejemplo n.º 25

def test_model_trainer_classification():
    model = CnnGenerator(3, (28, 28, 3)).generate().produce_model()
    train_data, test_data = get_classification_data_loaders()
    ModelTrainer(model, train_data, test_data, Accuracy, classification_loss,
                 True).train_model(max_iter_num=3)

Ejemplo n.º 26

Archivo: test_utils.py Proyecto: karolmajek/autokeras

def test_model_trainer_classification():
    model = CnnGenerator(3, (28, 28, 3)).generate().produce_model()
    train_data, test_data = get_classification_data_loaders()
    ModelTrainer(model, train_data, test_data, Accuracy, classification_loss, True).train_model(max_iter_num=3)

Ejemplo n.º 27

def test_out_of_memory(_, _2):
    train_data, test_data = get_classification_data_loaders()

Ejemplo n.º 28

def test_max_acq(_, _2):
    train_data, test_data = get_classification_data_loaders()