def compile(problem, need_grad, need_hess):
# Conditionally load autodiff backend if needed
if (problem.cost is None or
(need_grad and problem.grad is None and problem.egrad is None) or
(need_hess and problem.hess is None and problem.ehess is None)):
if type(problem.ad_cost).__name__ == 'TensorVariable':
from pymanopt.tools.autodiff import _theano as ad
elif type(problem.ad_cost).__name__ == 'function':
from pymanopt.tools.autodiff import _autograd as ad
else:
warn('Cannot identify autodiff backend from '
'ad_cost variable type.')
if problem.cost is None:
problem.cost = ad.compile(problem.ad_cost, problem.ad_arg)
if need_grad and problem.egrad is None and problem.grad is None:
problem.egrad = ad.gradient(problem.ad_cost, problem.ad_arg)
# Assume if hessian is needed gradient is as well
if need_hess and problem.ehess is None and problem.hess is None:
problem.ehess = ad.hessian(problem.ad_cost, problem.ad_arg)
def test_grad(self):
grad = tf.gradient(self.cost, self.X)
np_testing.assert_allclose(self.correct_grad, grad(self.Y))
def test_grad(self):
grad = tf.gradient(self.cost, self.X)
np_testing.assert_allclose(self.correct_grad, grad(self.Y))
