写了一个简单的二分类模型，但是训练了 N 次模型参数都没有动静

训练代码如下：

import numpy as np import torch # 1.prepare dataset xy = np.loadtxt("redPacket_2.csv", skiprows=1, delimiter",", dtype=np.float32) x_data = torch.from_numpy(xy[:, :-1]) y_data = torch.from_numpy(xy[:, [-1]]) # 2.design model using class class Model(torch.nn.Module): def __init__(self): super(Model, self).__init__() self.linear1 = torch.nn.Linear(4, 2) self.linear2 = torch.nn.Linear(2, 1) self.activate = torch.nn.ReLU() self.sigmoid = torch.nn.Sigmoid() def forward(self, x): x = self.activate(self.linear1(x)) x = self.sigmoid(self.linear2(x)) return x device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = Model().to(device) x_data = x_data.to(device) y_data = y_data.to(device) # 3.construct loss and optimizer criterion = torch.nn.BCELoss(reduction="mean") optimizer = torch.optim.SGD(model.parameters(), lr=0.01) # 4.training cycle forward, backward, update for epoch in range(10000): y_pred = model(x_data) loss = criterion(y_pred, y_data) if epoch % 100 == 0: print( "epoch %9d loss %.3f" % (epoch, loss.item()), model.linear2.weight.data, model.linear2.bias.data, ) optimizer.zero_grad() loss.backward() optimizer.step() 

训练集下载在这里，我每隔 100 周期打印模型的损失值和模型参数，结果如下：

epoch 0 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 100 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 200 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 300 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 400 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 500 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 600 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 700 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 800 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 900 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 1000 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 1100 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 1200 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') epoch 1300 loss 50.000 tensor([[0.0944, 0.1484]], device='cuda:0') tensor([0.4391], device='cuda:0') 

不知道为什么会不收敛，是哪里需要改进吗？