test_iaf.py 文件源码

def _test_jacobian(self, input_dim, hidden_dim, multiplier):
        jacobian = torch.zeros(input_dim, input_dim)
        arn = AutoRegressiveNN(input_dim, hidden_dim, multiplier)

        def nonzero(x):
            return torch.sign(torch.abs(x))

        for output_index in range(multiplier):
            for j in range(input_dim):
                for k in range(input_dim):
                    x = Variable(torch.randn(1, input_dim))
                    epsilon_vector = torch.zeros(1, input_dim)
                    epsilon_vector[0, j] = self.epsilon
                    delta = (arn(x + Variable(epsilon_vector)) - arn(x)) / self.epsilon
                    jacobian[j, k] = float(delta[0, k + output_index * input_dim].data.cpu().numpy()[0])

            permutation = arn.get_permutation()
            permuted_jacobian = jacobian.clone()
            for j in range(input_dim):
                for k in range(input_dim):
                    permuted_jacobian[j, k] = jacobian[permutation[j], permutation[k]]

            lower_sum = torch.sum(torch.tril(nonzero(permuted_jacobian), diagonal=0))
            self.assertTrue(lower_sum == float(0.0))