I’m trying to tailor the tutorial 5 towards my particular need, but I am not getting predictable and consistent output. It’s my first time using neural networks so excuse the nature of my questions.
I have 3 labeled datasets, with a total size of 27,666 that I will train the model on (80% - set1=8000, set2=5821, set3=8312) and then evaluate (20%) to calculate the accuracy of prediction of each category.
The main parameters I am tweaking are:

n_iter
learning_rate
n_hidden
n_confusion

At the moment n_iter is the entire 80% of the data set and n_hidden, n_confusion are fixed to the value in the tutorial. I am changing the learning rate from 0.00005 to 0.001.
Questions:

What is the link between the train function (that uses the learning rate variable) and the evaluate function (that is used in the prediction)?
Out of the 4 parameters above, which one has the most effect on the accuracy of prediction?
How can I add more linear layers?