PyTorch：正确提取学习的权重

Question

我正在尝试从线性层中提取权重，但它们似乎没有改变，尽管误差单调下降（即正在进行训练）。打印权重总和，没有任何反应，因为它保持不变：

np.sum(model.fc2.weight.data.numpy())

以下是代码片段：

def train(epochs):
    model.train()
    for epoch in range(1, epochs+1):
        # Train on train set
        print(np.sum(model.fc2.weight.data.numpy()))
        for batch_idx, (data, target) in enumerate(train_loader):
            data, target = Variable(data), Variable(data)
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()

和

# Define model
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(100, 80, bias=False)
        init.normal(self.fc1.weight, mean=0, std=1)
        self.fc2 = nn.Linear(80, 87)
        self.fc3 = nn.Linear(87, 94)
        self.fc4 = nn.Linear(94, 100)

    def forward(self, x):
        x = self.fc1(x)
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = F.relu(self.fc4(x))
        return x

也许我查看了错误的参数，尽管我检查了文档。感谢您的帮助！

Answer 1

使用model.parameters() 获取任何模型或层的可训练权重。记得放在list()里面，不然打印不出来

以下代码片段有效

>>> import torch
>>> import torch.nn as nn
>>> l = nn.Linear(3,5)
>>> w = list(l.parameters())
>>> w

PyTorch：正确提取学习的权重

PyTorch: Extract learned weights correctly

python

pytorch