def construct(self, x, rewards):
"""compute the perturbed gradients for parameters."""
# Choose optimal action
x_transpose = self.transpose(x, (1, 0))
scores_a = self.squeeze(
self.matmul(x, self.expand_dims(self._theta, 1)))
scores_b = x_transpose * self.matmul(self._Vc_inv, x_transpose)
scores_b = self.reduce_sum(scores_b, 0)
scores = scores_a + self._beta * scores_b
max_a = self.argmax(scores)
xa = x[max_a]
xaxat = self.matmul(self.expand_dims(xa, -1), self.expand_dims(xa, 0))
y = rewards[max_a]
y_max = self.reduce_max(rewards)
y_diff = y_max - y
self._current_regret = float(y_diff.asnumpy())
self._regret += self._current_regret
# Prepare noise
B = np.random.normal(0, self._sigma, size=xaxat.shape)
B = np.triu(B)
B += B.transpose() - np.diag(B.diagonal())
B = Tensor(B.astype(np.float32))
Xi = np.random.normal(0, self._sigma, size=xa.shape)
Xi = Tensor(Xi.astype(np.float32))
# Add noise and update parameters
return xaxat + B, xa * y + Xi, max_a
def to_tensor(obj, dtype=None):
if dtype is None:
res = Tensor(obj)
if res.dtype == mnp.float64:
res = res.astype(mnp.float32)
if res.dtype == mnp.int64:
res = res.astype(mnp.int32)
else:
res = Tensor(obj, dtype)
return res
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.value = Tensor([[1, 2, 3], [4, 5, 6]], dtype=mstype.float32)
def construct(self):
return self.value.astype("float88")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.value = Tensor([[1, 2, 3], [4, 5, 6]], dtype=mstype.int64)
def construct(self):
return self.value.astype(mstype.bool_)
def create_network(name, thres_filename, *args, **kwargs):
if name == 'resnet20':
thres = np.load(thres_filename)
thres = Tensor(thres.astype(np.float32))
return resnet20(thres=thres)
raise NotImplementedError(f"{name} is not implemented in the repo")
