def test_dataset_shuffle_setter(): try: new_dataset = dataset.Dataset(shuffle=1.5) except: new_dataset = dataset.Dataset() assert new_dataset.shuffle is True
def test_dataset_normalize_setter(): try: new_dataset = dataset.Dataset(normalize=1.5) except: new_dataset = dataset.Dataset() assert new_dataset.normalize == (0, 1)
def test_dataset_input_shape_setter(): try: new_dataset = dataset.Dataset(input_shape=1.5) except: new_dataset = dataset.Dataset() assert new_dataset.input_shape is None
def test_dataset_batch_size_setter(): try: new_dataset = dataset.Dataset(batch_size=1.5) except: new_dataset = dataset.Dataset() try: new_dataset = dataset.Dataset(batch_size=-1) except: new_dataset = dataset.Dataset() assert new_dataset.batch_size == 1
def test_dataset_preprocess(): new_dataset = dataset.Dataset(input_shape=(2, 3)) data = np.asarray([[1, 1, 1], [2, 2, 2]]) proc_data = new_dataset.preprocess(data) assert proc_data.numpy()[0][0] == 0 new_dataset = dataset.Dataset() proc_data = new_dataset.preprocess(data) assert proc_data.numpy()[0][0] == 0
def test_dataset_build(): new_dataset = dataset.Dataset() with pytest.raises(NotImplementedError): new_dataset._build()
def test_dataset_batch_size(): new_dataset = dataset.Dataset() assert new_dataset.batch_size == 1
def test_dataset_shuffle(): new_dataset = dataset.Dataset() assert new_dataset.shuffle is True
def test_dataset_normalize(): new_dataset = dataset.Dataset() assert new_dataset.normalize == (0, 1)
def test_dataset_input_shape(): new_dataset = dataset.Dataset() assert new_dataset.input_shape is None