text: Title: Multi-label classification with two labels 
  Body: ### Bug Description
  <!---
  A clear and concise description of what the bug is.
  -->
  
  ImageClassifier classification head treats the multi-label classification with 2 labels as multi-class classification one-hot encoded labels.
  
  ### Bug Reproduction
  Code for reproducing the bug:
  
  ----
  
  from sklearn.datasets import make_multilabel_classification
  X, Y = make_multilabel_classification(n_samples=100, n_features = 64,
                                        n_classes=2, n_labels=1,
                                        allow_unlabeled=False,
                                        random_state=1)
  X = X.reshape((100, 8, 8))
  
  clf = ak.ImageClassifier(max_trials=2, multi_label=True)
  clf.fit(X, Y, epochs=3, verbose=2)
  
  ----
  
  Data used by the code:
  synthetic data created with scikit-learn
  
  
  ### Setup Details
  Include the details about the versions of:
   - OS type and version:
   - Python: 3.6
   - autokeras: 1.0.2
   - keras-tuner:
   - scikit-learn:
   - numpy:
   - pandas:
   - tensorflow: 2.1.0
  
  ### Additional context
  <!---
  If applicable, add any other context about the problem.
  -->
  