After the fumbling around last time, I found a more basic introduction to TensorFlow. This time, instead of embarking blindly on a machine learning task, I simply got more comfortable with the library, learning a few things along the way.

One of the cool things I learned worth repeating here is that you can substitute in your own values for arbitrary variables inside a TensorFlow graph. Normally, you use tf.placeholder type variables for your inputs, but you can supply (via feed_dict) input to change tf.Varable and tf.constant values as well. This, combined with interactive sessions allows for better debugging, sanity checking, and simple model exploration when building up a model. I still have plenty of training to go to beat into my head that most TF lines define part of the graph, then sess.run actually executes it. But it’s starting to make sense.