我最近刚开始学习PyTorch。我尝试编写一个与我阅读过的论文中相同的模型以进行练习。
这是我参考的论文的PDF链接。https://dl.acm.org/doi/pdf/10.1145/3178876.3186066?download=true
这是我编写的代码。
class Tem(torch.nn.Module): def __init__(self, embedding_size, hidden_size): super(Tem, self).__init() self.embedding_size = embedding_size self.hidden_size = hidden_size self.leaf_size = 0 self.xgb_model = None self.vec_embedding = None self.multi_hot_Q = None self.user_embedding = torch.nn.Linear(1, embedding_size) self.item_embedding = torch.nn.Linear(1, embedding_size) def pretrain(self, ui_attributes, labels): print("Start XGBoost Training...") self.xgb_model = XGBoost(ui_attributes, labels) self.leaf_size = self.xgb_model.leaf_size self.vec_embedding = Variable(torch.rand(self.embedding_size, self.leaf_size, requires_grad=True)) self.h = Variable(torch.rand(self.hidden_size, 1, requires_grad=True)) self.att_w = Variable(torch.rand(2 * self.embedding_size, self.hidden_size, requires_grad=True)) self.att_b = Variable(torch.rand(self.leaf_size, self.hidden_size, requires_grad=True)) self.r_1 = Variable(torch.rand(self.embedding_size, 1, requires_grad=True)) self.r_2 = Variable(torch.rand(self.embedding_size, 1, requires_grad=True)) self.bias = Variable(torch.rand(1, 1, requires_grad=True)) def forward(self, ui_ids, ui_attributes): if self.xgb_model == None: raise Exception("Please run Tem.pretrain() to pre-train XGBoost model first.") n_data = len(ui_ids) att_input = torch.FloatTensor(ui_attributes) self.multi_hot_Q = torch.FloatTensor(self.xgb_model.multi_hot(att_input)).permute(0,2,1) vq = self.vec_embedding * self.multi_hot_Q id_input = torch.FloatTensor(ui_ids) user_embedded = self.user_embedding(id_input[:,0].reshape(n_data, 1)) item_embedded = self.item_embedding(id_input[:,1].reshape(n_data, 1)) ui = (user_embedded * item_embedded).reshape(n_data, self.embedding_size, 1) ui_repeat = ui.repeat(1, 1, self.leaf_size) cross = torch.cat([ui_repeat, vq], dim=1).permute(0,2,1) re_cross = corss.reshape(cross.shape[0] * cross.shape[1], cross.shape[2]) attention = torch.mm(re_cross, self.att_w) attention = F.leaky_relu(attention + self.att_b.repeat(n_data, 1)) attention = torch.mm(attention, self.h).reshape(n_data, self.leaf_size) attention = F.softmax(attention).reshape(n_data, self.leaf_size, 1) attention = self.vec_embedding.permute(1,0) * attention.repeat(1,1,20) pool = torch.max(attention, 1).values y_hat = self.bias.repeat(n_data, 1) + torch.mm(ui.reshape(n_data, self.embedding_size), self.r_1) + torch.mm(pool, self.r_2) y_hat = F.softmax(torch.nn.Linear(1, 2)(y_hat)) return y_hat我的问题是…似乎PyTorch不知道在反向传播中应该计算哪个张量的梯度。
print(tem)Tem((user_embedding): Linear(in_features=1, out_features=20, bias=True)(item_embedding): Linear(in_features=1, out_features=20, bias=True))我在谷歌上搜索了这个问题，有人说这些张量应该使用torch.autograd.Variable()，但这并没有解决我的问题。还有人说现在autograd直接支持张量，torch.autograd.Variable()不再是必要的。
loss_func = torch.nn.CrossEntropyLoss()optimizer = torch.Adagrad(tem.parameters(), lr=0.02)for t in range(20): prediction = tem(ids_train, att_train) loss = loss_func(prediction, y_train) optimizer.zero_grad() loss.backward() optimizer.step() if t % 5 == 0: print("loss: ", loss)loss: tensor(0.8133, grad_fn=<NllLossBackward>)loss: tensor(0.8133, grad_fn=<NllLossBackward>)loss: tensor(0.8133, grad_fn=<NllLossBackward>)loss: tensor(0.8133, grad_fn=<NllLossBackward>)回答：
你的问题与Variable无关。正如你所说，它现在不再是必要的。要计算在模型中（继承自nn.Module）声明的张量的梯度，你需要使用nn.Parameter()方法将它们包含在模型的参数中。例如，要包含self.h，你可以这样做：
self.h = nn.Parameter(torch.zeros(10,10)现在，当你调用loss.backward()时，它会为这个变量收集梯度（当然，loss必须依赖于self.h）。
