ValueError：预期的二维数组，而是得到标量数组：array=1.0

如何解决ValueError：预期的二维数组，而是得到标量数组：array=1.0

我刚刚修复了一个错误，指出“只有一个元素张量可以转换为 Python 标量”，现在我收到了这个错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-130-1555e7bdb655> in <module>
      4 
      5 pca = PCA(n_components=0.95)
----> 6 pca.fit(X)
      7 cov_matrix = np.dot(X.T,X) / len(X)
      8 

~\anaconda3\lib\site-packages\sklearn\decomposition\_pca.py in fit(self,X,y)
    349             Returns the instance itself.
    350         """
--> 351         self._fit(X)
    352         return self
    353 

~\anaconda3\lib\site-packages\sklearn\decomposition\_pca.py in _fit(self,X)
    395                             'TruncatedSVD for a possible alternative.')
    396 
--> 397         X = self._validate_data(X,dtype=[np.float64,np.float32],398                                 ensure_2d=True,copy=self.copy)
    399 

~\anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self,y,reset,validate_separately,**check_params)
    418                     f"requires y to be passed,but the target y is None."
    419                 )
--> 420             X = check_array(X,**check_params)
    421             out = X
    422         else:

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args,**kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k,arg in zip(sig.parameters,args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array,accept_sparse,accept_large_sparse,dtype,order,copy,force_all_finite,ensure_2d,allow_nd,ensure_min_samples,ensure_min_features,estimator)
    611             # If input is scalar raise error
    612             if array.ndim == 0:
--> 613                 raise ValueError(
    614                     "Expected 2D array,got scalar array instead:\narray={}.\n"
    615                     "Reshape your data either using array.reshape(-1,1) if "

ValueError: Expected 2D array,got scalar array instead:
array=1.0.
Reshape your data either using array.reshape(-1,1) if your data has a single feature or array.reshape(1,-1) if it contains a single sample.

我不知道如何修复它！ 我使用 .reshape(1,-1) 重塑了我的目标张量，这就是我收到的错误。

可以看出，我正在尝试将 PCA 应用于 FashionMNIST 的 2 个类上的张量。

我也尝试重塑张量，

reshaped = torch.reshape(target,[16,2])

带输出

reshaped

tensor([[1,1],[1,7],[7,1]])

但我仍然面临完全相同的错误！请帮帮我