我正在尝试通过加载我自己的数据来增加此处找到的 MNIST。为此，我有 numpy arrays training_x，training_y我将其转换如下：
training_X = torch.from_numpy(training_X)
training_y = torch.from_numpy(training_y)
【注意原文training_y是scikit-learn的LabelEncoder的输出】
并通过执行以下操作添加到 DataLoader：
torch.utils.data.TensorDataset(training_X, training_y)
训练时我收到以下错误：
TypeError: addmm_ received an invalid combination of arguments - got (int, int, torch.cuda.FloatTensor, torch.FloatTensor), but expected one of:
* (torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2)
* (torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2)
* (float beta, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2)
* (float alpha, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2)
* (float beta, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2)
* (float alpha, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2)
* (float beta, float alpha, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2)
didn't match because some of the arguments have invalid types: (int, int, torch.cuda.FloatTensor, !torch.FloatTensor!)
v * (float beta, float alpha, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2)
didn't match because some of the arguments have invalid types: (int, int, !torch.cuda.FloatTensor!, !torch.FloatTensor!)
我尝试将输入张量更改为浮动、双倍和长，但似乎我仍然缺少一些重要的东西。
如何让模型接受我的输入？