def testRateOverrides(self, mocked_parametrized):
mocked_parametrized.side_effect = (
lambda x, rate: scale_gradient.scale_gradient(x, -rate))
rate = 7.3
cons = optimization_constraints.OptimizationConstraints()
lhs = tf.zeros_like(1.0)
rhs = tf.ones_like(1.0)
x = cons.add(lhs < rhs, rate=rate)()
v = tf.all_variables()[0]
dxdl = tf.gradients(x, v)
with tf.train.MonitoredSession() as sess:
grads = sess.run(dxdl)
self.assertAllClose(grads[0], rate)
def _parametrize(x, rate=1.0):
return scale_gradient.scale_gradient(_squared_softplus(x), -rate)