class TestSoftmaxLayer(TestCase):
def setUp(self):
self.layer = SoftmaxLayer()
self.layer.set_input_shape((4, ))
self.predicted = np.array([0.2, 0.4, 0.4], dtype=np.float64)
self.true = np.array([0, 1, 0], dtype=np.float64)
def test_forward_prop_two_equal(self):
input = np.array([43265, 0, 0, 43265], dtype=np.float64)
output = self.layer.forward_prop(input)
expected_output = np.array([0.5, 0, 0, 0.5], dtype=np.float64)
numpy.testing.assert_array_equal(output, expected_output)
def test_forward_prop_one_large(self):
input = np.array([43265, 2, 54, 21], dtype=np.float64)
output = self.layer.forward_prop(input)
expected_output = np.array([1, 0, 0, 0], dtype=np.float64)
numpy.testing.assert_array_equal(output, expected_output)
def test_back_prop(self):
out_grad = np.array([3, 3, 3, 3], dtype=np.float64)
with self.assertRaises(NotImplementedError):
self.layer.back_prop(out_grad)
def test_initial_gradient(self):
expected_gradient = np.array([0.2, -0.6, 0.4], dtype=np.float64)
gradient = self.layer.initial_gradient(self.predicted, self.true)
numpy.testing.assert_array_equal(expected_gradient, gradient)
def test_loss(self):
expected_loss = 0.9162907
loss = self.layer.loss(self.predicted, self.true)
self.assertAlmostEqual(expected_loss, loss)
def test_get_output_shape(self):
self.layer.set_input_shape((5, ))
self.assertEqual(self.layer.get_output_shape(), (5, ))
