def test_numeric_gradient(self):
nll = NegativeLogLikelihoodLoss()
y = np.random.rand(2)
t = np.random.rand(2)
nll.loss(y, t)
gradient = nll.numeric_gradient(y)
dJdy = nll.dJdy_gradient(y, t)
assert_almost_equal(np.diag(gradient), dJdy, decimal=5)
def test_calc_delta(self):
l1 = SoftMaxLayer()
n = Sequential([l1])
x = np.array([15.0, 10.0, 2.0])
y = n.forward(x)
self.assertEqual(y.shape, (3, ))
nll = NegativeLogLikelihoodLoss()
t = np.array([0.0, 0.0, 1.0])
self.assertEqual(y.shape, t.shape)
J1 = nll.loss(y, t)
self.assertEqual(J1.shape, (3, ))
assert_almost_equal(J1, [0.0, 0.0, 13.0067176], decimal=5)
cel = CrossEntropyLoss()
t = np.array([0.0, 0.0, 1.0])
J2 = cel.loss(x, t)
self.assertEqual(J2.shape, (3, ))
assert_almost_equal(J2, [0.0, 0.0, 13.0067176], decimal=5)
delta_in = -nll.dJdy_gradient(y, t)
assert_almost_equal(delta_in, [0.0, 0.0, 445395.349996])
delta_out1 = n.backward(delta_in)
assert_almost_equal(delta_out1, [-0.9933049, -0.0066928, 0.9999978],
decimal=5)
#
delta_out2 = -cel.dJdy_gradient(x, t)
assert_almost_equal(delta_out2, [-0.9933049, -0.0066928, 0.9999978],
decimal=5)
def test_calc_loss(self):
l1 = SoftMaxLayer()
n = Sequential([l1])
x = np.array([15.0, 10.0, 2.0])
y = n.forward(x)
self.assertEqual(y.shape, (3, ))
nll = NegativeLogLikelihoodLoss()
t = np.array([0.0, 0.0, 1.0])
self.assertEqual(y.shape, t.shape)
J = nll.loss(y, t)
self.assertEqual(J.shape, (3, ))
assert_almost_equal(J, [0.0, 0.0, 13.0067176], decimal=5)