text: Title: Demonstrate how to use the trained model with new data in examples
  Body: This is more of a feature request around examples. 
  
  It'd be very useful to extend the examples to demonstrate how one might use the trained models on new data. This is already done for the generative models such as the NLP City Name Generator but when it comes to the classifiers the examples are currently only concerned with creating networks and training. They never show how the model can be used on new data.
  
  For example, the `lstm.py` currently finishes with the line where `mode.fit` is called. What I'm suggesting is to extend the example code to include a case where the model is used on new data. 
  ````python
  .....
  # Training
  model = tflearn.DNN(net, tensorboard_verbose=0)
  model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True,
            batch_size=32)
  
  # Use
  new_sentence = 'this is a new sentence to be analysed using the trained model`
  # code to prepare the new string ....
  predictions = model.predict(new_sentence)
  print(predictions)
  ```` 
  
  Same goes for the computer vision examples. 
  This can be particularly useful for people (like myself) who are new to machine learning.