python - SGDClassifier 类的数量必须大于 1；上了1节课

Question

因此，我已尽一切可能来解决此错误，这也是我巨大的挫败感。

from sklearn.linear_model import SGDClassifier

train_labels_9 = [(label == 9) for label in train_labels_9]
test_labels_9 = [(label == 9) for label in test_labels_9]

sgd = SGDClassifier(max_iter = 1000, tol = 1e-3)
sgd.fit(train_images,train_labels_9)

below is the error
ValueError                                Traceback (most recent call last)
<ipython-input-57-8ad0fdf39a29> in <module>
      6 
      7 sgd = SGDClassifier(max_iter = 1000, tol = 1e-3)
----> 8 sgd.fit(train_images,train_labels_9)

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in fit(self, X, y, coef_init, intercept_init, sample_weight)
    741                          loss=self.loss, learning_rate=self.learning_rate,
    742                          coef_init=coef_init, intercept_init=intercept_init,
--> 743                          sample_weight=sample_weight)
    744 
    745 

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in _fit(self, X, y, alpha, C, loss, learning_rate, coef_init, intercept_init, sample_weight)
    594 
    595         self._partial_fit(X, y, alpha, C, loss, learning_rate, self._max_iter,
--> 596                           classes, sample_weight, coef_init, intercept_init)
    597 
    598         if (self._tol is not None and self._tol > -np.inf

~\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py in _partial_fit(self, X, y, alpha, C, loss, learning_rate, max_iter, classes, sample_weight, coef_init, intercept_init)
    557             raise ValueError(
    558                 "The number of classes has to be greater than one;"
--> 559                 " got %d class" % n_classes)
    560 
    561         return self

ValueError: The number of classes has to be greater than one; got 1 class

Answer 1

我同意 Thremane D. Henry 的观点。
我昨天其实也遇到了类似的问题，你可以检查train_labels.shape，或者np.unique(train_labels)。

如果你打印出一些训练数据，比如train[5:10]，你就会发现问题。

它们是字符而不是int。

将代码更改为 (label == '9')。