Model返回Nan值

Question

我试图构建一个具有4个输入节点/功能和一个输出功能（0/1）的神经网络。我编写了这段代码并运行但是在训练模型时返回NaN。我调试了，权重和偏差都很好，直到他们通过模型。

从我到目前为止的搜索结果来看，这可能是我传递数据的方式的问题。我的输入数据是：

tensor([[0.0000e+00, 0.0000e+00, 0.0000e+00, 1.5340e+00],
        [1.5000e+01, 1.0000e-01, 2.4210e+00, 3.0000e+01],
        [3.0000e+00, 2.2000e-01, 2.2000e-01, 4.5000e+01],
        ...,
        [1.0000e+00, 2.0000e-02, 2.0000e-02, 1.5000e+01],
        [6.0000e+00, 2.0000e-01, 2.0000e-01, 1.5000e+01],
        [1.7000e+01, 5.2400e-01, 5.2400e-01, 2.0000e+00]], dtype=torch.float64)

import torch
from torchvision import datasets, transforms
import pandas as pd
import numpy as np
from torch.autograd import Variable
# Import tensor dataset & data loader
from torch.utils.data import TensorDataset, DataLoader
from torch import nn, optim
import torch.nn.functional as F

file = pd.read_csv('ks-projects-201801.csv')
array = np.array(file.values)
result = np.empty(len(array))
input_data = np.empty((len(array), 4))
for i in range(len(array)):
    input_data[i] = np.array([array[i][10], array[i][12]/1000, array[i][13]/1000, array[i][14]/1000])
    if array[i][9] == 'successful':
        result[i] = 1
    else:
        result[i] = 0


input_node = Variable(torch.from_numpy(input_data))
output = torch.from_numpy(result)
print(input_node)
print(output)

train_ds = TensorDataset(input_node.squeeze(), output.squeeze())

batch_size = 5
train_dl = DataLoader(train_ds, batch_size, shuffle=True)

这是实际的模型和培训

model = nn.Linear(4, 1)
print(model.weight)
print(model.bias)
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.003)

epochs = 5
model = model.double()
for e in range(epochs):
    running_loss = 0
    for xb, yb in train_dl:
        optimizer.zero_grad()
        res = model(xb)
        loss = criterion(res, yb)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
    else:
        print(f"model : {loss}")

这打印出模型：每个时期的nan并终止。我对pytorch很新，我不知道如何处理这个问题。

Answer 1

如果你看到NaN的损失尝试渐变裁剪和数据规范化。归一化数据是必须的（即归一化输入数据，使得均值= 0和方差= 1）