Please improve the documentation about prettytensor

[eiderman]

Thanks for such a long and well thought out critique. I will spend a bit of time working most of these into the code base.

As part of this, I will deemphasize templates. Their use case is narrow and is usually better served by doing the following pattern:

def my_model_fn(input_, labels, phase):
  ...

with tf.variable_scope('model'):
  train_result = my_model_fn(train_input, train_labels, pt.Phase.train)

with tf.variable_scope('model', reuse=True):
  test_result = my_model_fn(test_input, test_labels, pt.Phase.test)

[Hvass-Labs]

Thanks I'm looking forward to that!

I'm currently studying TFLearn and it suffers from the same lack of documentation. It is rather gruelling to learn and there is high risk of the user misunderstanding the API.

I saw a short and good video from one of the advocates of clean code, explaining why proper API documentation is very important: https://www.youtube.com/watch?v=Y-_oYR7e9cY

TFLearn seems to be more popular than PrettyTensor, but in some ways PrettyTensor appears more elegant, so I think there's room for both. TFLearn mimics scikit-learn in some ways, but it seems rather straight-forward to add similar functionality to PrettyTensor, and perhaps tailor it more to Neural Network learning.

The other day, I made a short video tutorial on PrettyTensor because that's how I initiallity like to learn things myself and none were available: https://www.youtube.com/watch?v=GCUfJQ_dec8