我在 pytorch 中有一个基本的神经网络模型,如下所示:
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(Net, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.sigmoid = nn.Sigmoid()
self.fc2 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
out = self.fc1(x)
out = self.sigmoid(out)
out = self.fc2(out)
return out
net = Net(400, 512,10)
如何从 net.parameters() 中提取偏差/截距项? 这个模型是否等同于使用equential()?
net = nn.Sequential(nn.Linear(input_dim, hidden_dim[0]),
nn.Sigmoid(),
nn.Linear(hidden_dim[0], hidden_dim[1]),
nn.Sigmoid(),
nn.Linear(hidden_dim[1], output_dim))
对于多类分类,nn.Softmax() 在任一模型的末尾是可选的吗?如果我理解正确的话,使用软件它会输出某个类别的概率,但如果不使用它,它会返回预测输出?
预先感谢您回答我的新手问题。
最佳答案
我们来一一解答问题。 这个模型等价于使用equential()
简短回答:不。您可以看到添加了两个 Sigmoid 层和两个线性层。您可以打印网络并查看结果:
net = Net(400, 512,10)
print(net.parameters())
print(net)
input_dim = 400
hidden_dim = 512
output_dim = 10
model = Net(400, 512,10)
net = nn.Sequential(nn.Linear(input_dim, hidden_dim),
nn.Sigmoid(),
nn.Linear(hidden_dim, hidden_dim),
nn.Sigmoid(),
nn.Linear(hidden_dim, output_dim))
print(net)
输出为:
Net(
(fc1): Linear(in_features=400, out_features=512, bias=True)
(sigmoid): Sigmoid()
(fc2): Linear(in_features=512, out_features=10, bias=True)
)
Sequential(
(0): Linear(in_features=400, out_features=512, bias=True)
(1): Sigmoid()
(2): Linear(in_features=512, out_features=512, bias=True)
(3): Sigmoid()
(4): Linear(in_features=512, out_features=10, bias=True)
)
我希望您能看到它们的不同之处。
您的第一个问题:如何从 net.parameters() 中提取偏差/截距项
答案:
model = Net(400, 512,10)
bias = model.fc1.bias
print(bias)
输出是:
tensor([ 3.4078e-02, 3.1537e-02, 3.0819e-02, 2.6163e-03, 2.1002e-03,
4.6842e-05, -1.6454e-02, -2.9456e-02, 2.0646e-02, -3.7626e-02,
3.5531e-02, 4.7748e-02, -4.6566e-02, -1.3317e-02, -4.6593e-02,
-8.9996e-03, -2.6568e-02, -2.8191e-02, -1.9806e-02, 4.9720e-02,
---------------------------------------------------------------
-4.6214e-02, -3.2799e-02, -3.3605e-02, -4.9720e-02, -1.0293e-02,
3.2559e-03, -6.6590e-03, -1.2456e-02, -4.4547e-02, 4.2101e-02,
-2.4981e-02, -3.6840e-03], requires_grad=True)
关于machine-learning - pytorch问题: how to add bias term and extract its value? 类与顺序模型?和softmax,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/57863907/