Save and Restore a Model in TensorFlow
Tensorflow distinguishes between saving/restoring the current values of all the variables in a graph and saving/restoring the actual graph structure. To restore the graph, you are free to use either Tensorflow's functions or just call your piece of code again, that built the graph in the first place. When defining the graph, you should also think about which and how variables/ops should be retrievable once the graph has been saved and restored.
Various examples showing how Tensorflow supports indexing into tensors, highlighting differences and similarities to numpy-like indexing where possible.