Question

我试图与PyTorch建立神经网络，以预测学生的期末考试成绩。我已经这样做了 -

# 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))

我在第loss = criterion(outputs, Y)行收到错误，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，而且我已经在这里检查了其他帖子，但是，他们不会这样做。似乎有所帮助。 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

任何人都可以告诉我我做错了什么以及如何纠正它。任何帮助表示赞赏！谢谢！：）

Answer 1

我的程序中有同样的错误我只是意识到问题出在我网络中的输出节点数上在我的程序中，我模型的输出节点数不等于数据标签数输出的数量为1，目标标签的数量为10。然后我将输出的数量更改为10，没有错误

Answer 2

假设您的代码是正确的，当类标签不在0到MAX范围内（在您的情况下为20）时会出现此问题。

PyTorch - RuntimeError：Assertion＆＃39; cur_target＆gt; = 0＆amp;＆amp; cur_target＆lt; n_classes＆＃39;失败

2 个答案: