neural_network.py 文件源码

def check_gradient(self, X, T, eps=1e-10):
        thetas = self.flatten()

        grad1 = self.numerical_gradient(thetas, X, T)
        _, grad2 = self.compute_cost(thetas, X, T)

        diff = linalg.norm(grad1 - grad2) / linalg.norm(grad1 + grad2)
        print(np.c_[grad1, grad2, np.abs(grad1 - grad2)])
        print('diff = {0}'.format(diff))

        return diff < eps