title: How can I use Lime to classify my time series
body: <p>I have my simplified model that looks like this:</p>
  
  <pre><code>model = Sequential()
  model.add(LSTM(12, input_shape=(1000,12)))
  model.add(Dense(9, activation='sigmoid'))
  </code></pre>
  
  <p>My training data has the shape:</p>
  
  <pre><code>(900,1000,12)
  </code></pre>
  
  <p>As you can see from the output layer I have 9 outputs, so every signal (of length 1000) will be classified into one or more of this outputs (it is a multilabel classification)</p>
  
  <p>I train my model like this:</p>
  
  <pre><code>history = model.fit(X_train,y_train, batch_size=32, epochs=10,validation_data=(X_val,y_val),verbose=2)
  </code></pre>
  
  <p>So everything is ok so far, but now I want to use Lime to explain the classification</p>
  
  <pre><code>explainer = lime_tabular.RecurrentTabularExplainer(X_train, training_labels=y_train,feature_names=['1','2','3','4','5','6','7','8','9','10','11','12'],
                                                     discretize_continuous=True,
                                                     class_names=['a','b','c','d','e','f','g','h','i'],
                                                     discretizer='decile')
  </code></pre>
  
  <p>I am not getting any error when I define my explainer, but when I try to run the code below it runs for a long time befor giving me an error</p>
  
  <pre><code>exp=explainer.explain_instance(data_row=X[0].reshape(1,1000,12),classifier_fn= model)
  exp.show_in_notebook()
  </code></pre>
  
  <pre><code>NotImplementedError: LIME does not currently support classifier models without probability scores. 
  If this conflicts with your use case, please let us know: https://github.com/datascienceinc/lime/issues/16
  </code></pre>
  
  <p>Can anyone recognize this error or see whats wrong?</p>