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)