Question

我正在尝试通过加载我自己的数据来扩充发现here的MNIST。为此，我有numpy数组training_x，training_y，我将其变换如下：

training_X = torch.from_numpy(training_X)
training_y = torch.from_numpy(training_y)

[请注意原始training_y是scikit-learn的LabelEncoder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float，double和long但似乎我仍然缺少一些关键的东西。

如何让模型接受我的输入？

Answer 1

您提供的第四个参数似乎是torch.FloatTensor但它应该是torch.cuda.FloatTensor。

您是否尝试在该论点上调用.cuda()？

请参阅torch.Tensor.cuda()

pytorch forward方法中的类型不匹配

1 个答案: