def test_bernoulli(self):
for size in [(10,), (10, 10), (10, 10, 10)]:
probs = torch.rand(size)
randvec = crypten.bernoulli(probs)
self.assertTrue(randvec.size() == size, "Incorrect size")
tensor = randvec.get_plain_text()
self.assertTrue(((tensor == 0) + (tensor == 1)).all(), "Invalid values")
probs = torch.Tensor(int(1e4)).fill_(0.2)
randvec = crypten.bernoulli(probs).get_plain_text()
frac_zero = float((randvec == 0).sum()) / randvec.nelement()
self.assertTrue(math.isclose(frac_zero, 0.8, rel_tol=1e-1, abs_tol=1e-1))
def _process_targets(self, targets):
"""Encrypts targets and RR to targets if necessary"""
if self.rr_prob is not None:
flip_probs = torch.tensor(self.rr_prob).expand(targets.size())
# Apply appropriate RR-protocol and encrypt targets if necessary
if self.rr_prob is not None:
flip_probs = torch.tensor(self.rr_prob).expand(targets.size())
if crypten.is_encrypted_tensor(targets):
if self.rr_prob is not None:
flip_mask = crypten.bernoulli(flip_probs)
targets = targets + flip_probs - 2 * flip_mask * targets
targets_enc = targets
else:
# Label provider adds RR label flips if they are plaintext
if self.rr_prob is not None and self.is_label_src():
flip_mask = flip_probs.bernoulli()
targets += flip_mask - 2 * targets * flip_mask
# Encrypt targets:
targets_enc = crypten.cryptensor(targets, src=self.label_src)
return targets_enc
def score_func(scores, A_inv, b, context):
explore = crypten.bernoulli(torch.tensor([epsilon]))
rand_scores = crypten.rand(*scores.size())
scores.mul_(1 - explore).add_(rand_scores.mul(explore))