def compute(self, simulation, closest_rsu):
neural_net = Neural_Network()
X = self.training_data.pop()
y = self.training_label.pop()
# print(X)
# print(y)
with autograd.record():
output = self.net(X)
if cfg['attack'] == 'label' and len(
closest_rsu.accumulative_gradients
) < cfg['num_faulty_grads']:
loss = neural_net.loss(output, 9 - y)
else:
loss = neural_net.loss(output, y)
loss.backward()
grad_collect = []
for param in self.net.collect_params().values():
if param.grad_req != 'null':
grad_collect.append(param.grad().copy())
self.gradients = grad_collect
