title: ValueError: Can't handle mix of multilabel-indicator and binary
body: <p>I am using Keras with the scikit-learn wrapper. In particular, I want to use GridSearchCV for hyper-parameters optimisation.</p>
  
  <p>This is a multi-class problem, i.e. the target variable can have only one label chosen on a set of n classes. For instance, the target variable can be 'Class1', 'Class2' ... 'Classn'.</p>
  
  <pre><code># 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)
  </code></pre>
  
  <p>The problem is that, when score is computed during cross-validation, the true label for validation samples is encoded one-hot, while the prediction for some reason collapses to binary label (when the target variable has only two classes). For instance, this is the last part of the stack trace:</p>
  
  <pre><code>...........................................................................
  /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) &gt; 1:
       81         raise ValueError("Can't handle mix of {0} and {1}"
  ---&gt; 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 =&gt; The set is no more needed
       85     y_type = y_type.pop()
       86
  
  ValueError: Can't handle mix of multilabel-indicator and binary
  </code></pre>
  
  <p>How can I instruct Keras/sklearn to give back predictions in one-hot encoding?</p>