def test_dataset_tool_instance_training_flag_true_data_label_different( self): result = tests.read_from_output(lambda: dataset_tool.DatasetTool( training_flag=True, data=self.dataset, label=numpy.array([1, 2, 3, 5, 6]))) self.assertIn("学習データサイズが異なる, 入力データサイズ = 3 ラベルサイズ = 5", result)
def prepare_train_dataset(): (x_train, y_train), (_, _) = mnist.load_data() x_train = x_train.reshape(60000, 784) / 255. return dataset_tool.DatasetTool(data=x_train, label=y_train, training_flag=True, repeat=False, one_hot_classes=10)
def prepare_test_dataset(): (_, _), (x_test, y_test) = mnist.load_data() x_test = x_test.reshape(10000, 784) / 255. return dataset_tool.DatasetTool(data=x_test, label=y_test, training_flag=True, repeat=False, one_hot_classes=10)
def test_dataset_tool_instance_training_flag_true_no_label(self): self.assertRaises( AttributeError, lambda: dataset_tool.DatasetTool( training_flag=True, data=self.dataset, label=None))
def test_dataset_tool_instance_training_flag_false(self): res = dataset_tool.DatasetTool(training_flag=False, data=self.dataset) self.assertIs(type(res), dataset_tool.DatasetTool)
def setUpClass(cls) -> None: cls.dataset = numpy.array([1, 2, 3]) cls.label = numpy.array([1, 2, 3]) cls.dataset_tool = dataset_tool.DatasetTool(training_flag=True, data=cls.dataset, label=cls.label)