def test_nnCostFunction(self):
cost = nnCostFunction(unrolltheta(self.theta1, self.theta2),
self.s_1, self.s_2, self.K,
self.X, self.y, lamda=0)
self.assertAlmostEqual(cost, 0.287629, places=6)
cost_reg = nnCostFunction(unrolltheta(self.theta1, self.theta2),
self.s_1, self.s_2, self.K,
self.X, self.y, lamda=1.0)
self.assertAlmostEqual(cost_reg, 0.383770, places=6)
def test_neuralnetwork(self):
lamda = 1.0
epsilon = 0.12
initial_theta1 = generate_theta(self.theta1.shape, epsilon)
initial_theta2 = generate_theta(self.theta2.shape, epsilon)
initial_theta = unrolltheta(initial_theta1, initial_theta2)
optimized_theta1, optimized_theta2 = nnTrain(initial_theta, self.s_1, self.s_2, self.K, self.X, self.y, lamda)
predictions = nnPredict(optimized_theta1, optimized_theta2, self.X)
expected = (self.orig_y - 1).reshape(-1)
acc = accuracy(predictions,expected)
print "Accuracy: {} (Should be around 95%, plus or minus 1% due to random initialization)".format(acc)
self.assertGreater(acc, 93)
