external_optimizer_test.py 文件源码

def test_nonlinear_programming(self):
    vector_initial_value = [7., 7.]
    vector = variables.Variable(vector_initial_value, 'vector')

    # Make norm as small as possible.
    loss = math_ops.reduce_sum(math_ops.square(vector))
    # Ensure y = 1.
    equalities = [vector[1] - 1.]
    # Ensure x >= 1. Thus optimum should be at (1, 1).
    inequalities = [vector[0] - 1.]

    optimizer = external_optimizer.ScipyOptimizerInterface(
        loss, equalities=equalities, inequalities=inequalities, method='SLSQP')

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())
      optimizer.minimize(sess)
      self.assertAllClose(np.ones(2), sess.run(vector))