def testProblemGradients(self):
param_shapes = [(1, 1)]
random_seed = 200
noise_stdev = 1.0
problem = pg.Problem(param_shapes, random_seed, noise_stdev)
x = tf.constant(2.)
y = tf.constant(20.)
parameters = [x, y]
objective = x**2 + y**2
grads = problem.gradients(objective, parameters)
self.assertLen(grads, len(parameters))
self.assertNotEqual(grads[0], grads[1])
def testProblem(self):
param_shapes = [(5, 1), (2, 2)]
random_seed = 200
noise_stdev = 1.0
problem = pg.Problem(param_shapes, random_seed, noise_stdev)
init = problem.init_tensors()
self.assertLen(init, len(param_shapes))
for i, ps in enumerate(param_shapes):
self.assertEqual(list(ps), init[i].get_shape().as_list())
init = problem.init_variables()
self.assertLen(init, len(param_shapes))
for i, ps in enumerate(param_shapes):
self.assertSequenceEqual(ps, init[i].get_shape())
self.assertIsInstance(init[i], tf.Variable)