Question

我正在使用Keras和scikit-learn包装器。特别是，我想使用GridSearchCV进行超参数优化。

这是一个多类问题，即目标变量只能在一组n个类中选择一个标签。例如，目标变量可以是'Class1'，'Class2'......'Classn'。

# self._arch creates my model
nn = KerasClassifier(build_fn=self._arch, verbose=0)
clf = GridSearchCV(
  nn,
  param_grid={ ... },
  # I use f1 score macro averaged
  scoring='f1_macro',
  n_jobs=-1)

# self.fX is the data matrix
# self.fy_enc is the target variable encoded with one-hot format
clf.fit(self.fX.values, self.fy_enc.values)

问题在于，当在交叉验证期间计算得分时，验证样本的真实标签是一次性编码的，而由于某种原因预测会折叠为二进制标签（当目标变量只有两个类时）。例如，这是堆栈跟踪的最后一部分：

...........................................................................
/Users/fbrundu/.pyenv/versions/3.6.0/lib/python3.6/site-packages/sklearn/metrics/classification.py in _check_targets(y_true=array([[ 0.,  1.],
       [ 0.,  1.],
       [ 0... 0.,  1.],
       [ 0.,  1.],
       [ 0.,  1.]]), y_pred=array([1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1,...0, 1, 0, 0, 1, 0, 0, 0,
       0, 0, 0, 0, 1, 1]))
     77     if y_type == set(["binary", "multiclass"]):
     78         y_type = set(["multiclass"])
     79
     80     if len(y_type) > 1:
     81         raise ValueError("Can't handle mix of {0} and {1}"
---> 82                          "".format(type_true, type_pred))
        type_true = 'multilabel-indicator'
        type_pred = 'binary'
     83
     84     # We can't have more than one value on y_type => The set is no more needed
     85     y_type = y_type.pop()
     86

ValueError: Can't handle mix of multilabel-indicator and binary

如何指示Keras / sklearn以单热编码方式回馈预测？

Answer 1

根据Vivek的评论，我使用了原始（非单热编码）目标数组，并根据the comments to this issue配置（在我的Keras模型中，参见代码）损失sparse_categorical_crossentropy。

arch.compile(
  optimizer='sgd',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy'])

ValueError：无法处理multilabel-indicator和binary的混合

1 个答案: