title: keras model not supporting for lime
body: <p>I am using lime to get the model interpretations using keras model. When I am trying to use the keras model with lime I am getting the mentioned erros. Can anyone tell me how to resolve this?</p>
  <p>type of model</p>
  <pre><code>&lt;keras.engine.sequential.Sequential at 0x7fb89525f128&gt;
  </code></pre>
  <p>Below is the code</p>
  <pre><code>import lime
  from lime import lime_tabular
  
  explainer = lime_tabular.LimeTabularExplainer(
      training_data=np.array(X_train),
      feature_names=X_train.columns,
      class_names=['Completed', 'Failed'],
      mode='classification'
  )
  
  exp = explainer.explain_instance(
      data_row=X_test.iloc[1], 
      predict_fn=model.predict
  )
  
  exp.show_in_notebook(show_table=True)
  </code></pre>
  <p>Below is the error that I got</p>
  <pre><code>---------------------------------------------------------------------------
  IndexError                                Traceback (most recent call last)
  &lt;ipython-input-66-2ad24f676142&gt; in &lt;module&gt;
        1 exp = explainer.explain_instance(
        2     data_row=X_test.iloc[1],
  ----&gt; 3     predict_fn=model.predict
        4 )
        5 
  
  /libraries/test_api/lib/python3.6/site-packages/lime/lime_tabular.py in explain_instance(self, data_row, predict_fn, labels, top_labels, num_features, num_samples, distance_metric, model_regressor)
      457                     num_features,
      458                     model_regressor=model_regressor,
  --&gt; 459                     feature_selection=self.feature_selection)
      460 
      461         if self.mode == &quot;regression&quot;:
  
  /libraries/test_api/lib/python3.6/site-packages/lime/lime_base.py in explain_instance_with_data(self, neighborhood_data, neighborhood_labels, distances, label, num_features, feature_selection, model_regressor)
      180 
      181         weights = self.kernel_fn(distances)
  --&gt; 182         labels_column = neighborhood_labels[:, label]
      183         used_features = self.feature_selection(neighborhood_data,
      184                                                labels_column,
  
  IndexError: index 1 is out of bounds for axis 1 with size 1
  </code></pre>