RuntimeError: input.size(-1) must be equal to input_size. Expected 1, got 2

这个错误是由于输入的尺寸不匹配导致的。根据错误信息，它指出输入的维度（size）与期望的维度（input_size）不一致。

在这种情况下，输入的最后一个维度（-1）应该等于1，但实际上是2。你可以检查一下你的输入数据的维度是否正确，并确保最后一个维度的大小与期望的一致。如果你能提供更多的代码和上下文，我可以给出更具体的建议。

相关问题

RuntimeError: Expected 4-dimensional input for 4-dimensional weight [64, 1, 4, 4], but got 2-dimensional input of size [64, 240] instead

This error is caused when the input to a convolutional neural network (CNN) has the wrong dimensions. CNNs require input data to be in 4 dimensions: (batch_size, channels, height, width).

In this specific case, the error message indicates that the expected weight tensor has 4 dimensions with shape [64, 1, 4, 4]. However, the actual input tensor has 2 dimensions with shape [64, 240]. This means that the input tensor is missing the height and width dimensions required by the CNN.

To fix this error, you need to reshape the input tensor to have the required dimensions. You can do this using the `reshape` method in NumPy or PyTorch. For example, if your input tensor has shape [64, 240], you can reshape it to [64, 1, 4, 4] by calling `input_tensor.reshape(64, 1, 4, 4)`.

Make sure to double-check that your input data has the correct shape before passing it to your CNN.