Pytorch, Torch, Torchvision, Deep Learning, Python.
loss_1 = self.loss(self.fit(w1, self.x, self.b)) loss_2 = self.loss(self.fit(w2, self.x, self.b)) rate = loss_1 - loss_2 return self.w - lr * rate def update(self): return self.w+self.eta, self.w-self.eta x = torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1)) y =