class TestNeuralNetwork(unittest.TestCase):
def setUp(self):
self.nnw = NeuralNetwork()
def test_sigmoid(self):
x = np.arange(-5.0, 5.0, 0.1)
y = self.nnw._sigmoid(x)
assert_almost_equal(
np.array([
0.00669285, 0.00739154, 0.00816257, 0.0090133, 0.0099518,
0.01098694, 0.01212843, 0.01338692, 0.01477403, 0.0163025,
0.01798621, 0.01984031, 0.02188127, 0.02412702, 0.02659699,
0.02931223, 0.03229546, 0.03557119, 0.03916572, 0.04310725,
0.04742587, 0.05215356, 0.05732418, 0.06297336, 0.06913842,
0.07585818, 0.0831727, 0.09112296, 0.09975049, 0.10909682,
0.11920292, 0.13010847, 0.14185106, 0.15446527, 0.16798161,
0.18242552, 0.19781611, 0.21416502, 0.23147522, 0.24973989,
0.26894142, 0.2890505, 0.31002552, 0.33181223, 0.35434369,
0.37754067, 0.40131234, 0.42555748, 0.450166, 0.47502081, 0.5,
0.52497919, 0.549834, 0.57444252, 0.59868766, 0.62245933,
0.64565631, 0.66818777, 0.68997448, 0.7109495, 0.73105858,
0.75026011, 0.76852478, 0.78583498, 0.80218389, 0.81757448,
0.83201839, 0.84553473, 0.85814894, 0.86989153, 0.88079708,
0.89090318, 0.90024951, 0.90887704, 0.9168273, 0.92414182,
0.93086158, 0.93702664, 0.94267582, 0.94784644, 0.95257413,
0.95689275, 0.96083428, 0.96442881, 0.96770454, 0.97068777,
0.97340301, 0.97587298, 0.97811873, 0.98015969, 0.98201379,
0.9836975, 0.98522597, 0.98661308, 0.98787157, 0.98901306,
0.9900482, 0.9909867, 0.99183743, 0.99260846
]), y)
# self.assertEqual(True, path.exists('../img/sigmoid.png'))
def test_softmax(self):
y = self.nnw._softmax(np.array([0.3, 2.9, 4.0]))
assert_almost_equal(np.array([0.01821127, 0.24519181, 0.73659691]), y)
def test_show_image(self):
x_train, t_train = self.nnw._get_test_data()
img, label, reshaped_img = self.nnw._process_image(x_train, t_train)
self.assertEqual(7, label)
self.assertEqual((784, ), img.shape)
self.assertEqual((28, 28), reshaped_img.shape)
# self.nnw._show_image(reshaped_img)
def test_step_func(self):
x = np.arange(-5.0, 5.0, 0.1)
y = self.nnw.step_func(x)
assert_array_equal(
np.array([
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
]), y)
# self.assertEqual(True, path.exists('../img/step_func.png'))
def test_relu(self):
x = np.arange(-5.0, 5.0, 0.1)
y = self.nnw.relu(x)
assert_almost_equal(
np.array([
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.,
1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2., 2.1, 2.2, 2.3,
2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6,
3.7, 3.8, 3.9, 4., 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9
]), y)
# self.assertEqual(True, path.exists('../img/relu.png'))
def test_matrix_product_1(self):
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
product = self.nnw.matrix_product(a, b)
self.assertEqual((2, 2), a.shape)
self.assertEqual((2, 2), b.shape)
assert_array_equal(np.array([[19, 22], [43, 50]]), product)
def test_matrix_product_2(self):
a = np.array([[1, 2, 3], [4, 5, 6]])
b = np.array([[1, 2], [3, 4], [5, 6]])
product = self.nnw.matrix_product(a, b)
self.assertEqual((2, 3), a.shape)
self.assertEqual((3, 2), b.shape)
assert_array_equal(np.array([[22, 28], [49, 64]]), product)
def test_matrix_product_3(self):
a = np.array([[1, 2], [3, 4], [5, 6]])
b = np.array([7, 8])
product = self.nnw.matrix_product(a, b)
self.assertEqual((3, 2), a.shape)
self.assertEqual((2, ), b.shape)
assert_array_equal(np.array([23, 53, 83]), product)
def test_evaluate(self):
self.assertEqual('92.07%', self.nnw.evaluate())
