def test_sigmoidPrime(self):
NN = Neural_Network()
s = np.array([0.2, 3.4, 7.4])
d_sig_1 = NN.sigmoidPrime(s)
d_sig_2 = s * (1 - s)
self.assertEqual(d_sig_1[0], d_sig_2[0])
self.assertEqual(d_sig_1[1], d_sig_2[1])
self.assertEqual(d_sig_1[2], d_sig_2[2])
def test_d_loss_o(self):
NN = Neural_Network()
gt = np.array([0.2, 3.4, 7.4])
o = np.array([2.2, 5.4, 9.4])
d_o_1 = NN.d_loss_o(gt, o)
d_o_2 = np.array([2., 2., 2.])
self.assertAlmostEqual(d_o_1[0], d_o_2[0])
self.assertAlmostEqual(d_o_1[1], d_o_2[1])
self.assertAlmostEqual(d_o_1[2], d_o_2[2])
def test_forward(self):
NN = Neural_Network()
X = np.random.randint(0, high=10, size=[3, 2], dtype='l')
X = X / np.amax(X, axis=0)
sol_1 = NN.forward(X)
z = np.dot(X, NN.U) + NN.e
b = NN.sigmoid(z)
h = np.dot(b, NN.W) + NN.f
sol_2 = NN.sigmoid(h)
self.assertEqual(sol_1[0][0], sol_2[0][0])
self.assertEqual(sol_1[1][0], sol_2[1][0])
self.assertEqual(sol_1[2][0], sol_2[2][0])