Is the validation set used for anything besides monitoring progress on the Tensorflow for poets google code lab's project?
Very briefly, you have three sets of data: training, testing, and validation.  These inputs should be distinct; any duplication between data sets will reduce some desirable aspect of your final model.
Training data is used just the way you think: these are the inputs that are matched with the expected outputs (after filtering through your model).  The model weights are adjusted until you've converged at the greatest accuracy you can manage.
Testing data is used during training to determine when training is done.  If the testing accuracy is still dropping, then your model is still learning things; when it starts to rise, you're over-fitting.
Validation data is the final check on whether your training phase got to a useful point.  This data set is entirely independent of the previous two, as is representative of not-previously-seen, real-world input.
The short answer is no.  If you fold your testing and validation data into your training set (which is what I think you're trying to ask) and re-train the model, you've changed the training conditions, and you have no evidence that the resulting training is still valid.  How do you know ho many epochs you need to train with this larger data set?  When you're ready to deploy the trained model, how do you know your new accuracy?
I would like to know if the validation set influences in any way the training of the network, or if it's sole purpose is to monitor the training progress (with the tensorflow for poets project in question).
If doesn't, is it okay/good-practice to reduce the validation and training sets to zero once I know the accuracy for my dataset? So in that way I can get a model trained with the most amount of data I can provide.