Tensorflow LSTM model testing

I'm new to LSTM and Tensorflow, and I'm trying to use an LSTM model to learn and then classify some huge data set that I have. (I'm not worried about the accuracy my intention is to learn). I tried to implement the model in a similar way as in the PTB word prediction tutorial that uses LSTM. The code in the tutorial (http://ift.tt/1SUgZSz) uses the below line to run the session using the model

 cost, state, _ = session.run([m.cost, m.final_state, eval_op],
                                 {m.input_data: x,
                                  m.targets: y,
                                  m.initial_state: state})

I modified this for my example as below (to get the logits and work with it):

  cost, state, _,output,logits = session.run([m.cost, m.final_state, eval_op, m.output,m.logits],
                                 {m.input_data: x,
                                  m.targets: y,
                                  m.initial_state: state})

So my questions if someone could help are as below:

• How can the model built while training be used for testing? What exactly is happening when 3 models are being used by the tutorial one for each test, train and validation?
• What about the targets while testing(if I don't know them, say in a classification problem). What changes in the run_epoch () can be done in a way to use the model built during training.
• Just another question: It's difficult to debug tensorflow graphs ( and I found it difficult to understand the tensorboard visualizer too) And I didn't find good resource for learning tensorflow (the website seems to be lacking structure/ documentation) What other resources/ debugging methods are there?

Thanks.