python - PyTorch - 运行时错误 : Assertion 'cur_target >= 0 && cur_target < n_classes' failed

Question

我正在尝试使用 PyTorch 制作一个神经网络来预测学生的期末考试成绩。我是这样做的 -

# Hyper Parameters
input_size = 2
hidden_size = 50
num_classes =21
num_epochs = 500
batch_size = 5
learning_rate = 0.1

# define a customise torch dataset
class DataFrameDataset(torch.utils.data.Dataset):
def __init__(self, df):
    self.data_tensor = torch.Tensor(df.as_matrix())

# a function to get items by index
def __getitem__(self, index):
    obj = self.data_tensor[index]
    input = self.data_tensor[index][0:-1]
    target = self.data_tensor[index][-1] - 1
    return input, target

# a function to count samples
def __len__(self):
    n, _ = self.data_tensor.shape
    return n

# load all data
data_i = pd.read_csv('dataset/student-mat.csv', header=None,delimiter=";")
data = data_i.iloc[:,30:33]

# normalise input data
for column in data:
# the last column is target
if column != data.shape[1] - 1:
    data[column] = data.loc[:, [column]].apply(lambda x: (x - x.mean()) / x.std())

# randomly split data into training set (80%) and testing set (20%)
msk = np.random.rand(len(data)) < 0.8
train_data = data[msk]
test_data = data[~msk]

# define train dataset and a data loader
train_dataset = DataFrameDataset(df=train_data)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# Neural Network
class Net(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
    super(Net, self).__init__()
    self.fc1 = nn.Linear(input_size, hidden_size)
    self.sigmoid = nn.Sigmoid()
    self.fc2 = nn.Linear(hidden_size, num_classes)

def forward(self, x):
    out = self.fc1(x)
    out = self.sigmoid(out)
    out = self.fc2(out)
    return out


net = Net(input_size, hidden_size, num_classes)

# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Rprop(net.parameters(), lr=learning_rate)

# store all losses for visualisation
all_losses = []

# train the model by batch
for epoch in range(num_epochs):
    for step, (batch_x, batch_y) in enumerate(train_loader):
        # convert torch tensor to Variable
        X = Variable(batch_x)
        Y = Variable(batch_y.long())

        # Forward + Backward + Optimize
        optimizer.zero_grad()  # zero the gradient buffer
        outputs = net(X)
        loss = criterion(outputs, Y)
        all_losses.append(loss.data[0])
        loss.backward()
        optimizer.step()

        if epoch % 50 == 0:
            _, predicted = torch.max(outputs, 1)
            # calculate and print accuracy
            total = predicted.size(0)
            correct = predicted.data.numpy() == Y.data.numpy()

            print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f, Accuracy: %.2f %%'
                  % (epoch + 1, num_epochs, step + 1,
                      len(train_data) // batch_size + 1,
                      loss.data[0], 100 * sum(correct)/total))

我在 loss = criterion(outputs, Y) 行遇到错误上面写着—— RuntimeError: Assertion 'cur_target >= 0 && cur_target < n_classes' failed. at /pytorch/torch/lib/THNN/generic/ClassNLLCriterion.c:62

我无法弄清楚我做错了什么，因为我对此很陌生，并且我已经检查过这里的其他帖子，但是它们似乎没有帮助。data数据框看起来像 -

     30  31  32

0     5   6   6
1     5   5   6
2     7   8  10
3    15  14  15
4     6  10  10
5    15  15  15

谁能告诉我我做错了什么以及如何纠正。任何帮助表示赞赏!谢谢! :)

Answer 1

我的程序也有同样的错误我刚刚意识到问题出在网络中的输出节点数量上在我的程序中，模型的输出节点数不等于数据的标签数输出数量为1，目标标签数量为10。然后我将输出数量改为10，没有错误