Manages the environment for executing ops in a graph
Specifies which device executes part of all of a graph
Each session has one graph
Each graph may be run in multiple sessions
Building a Graph
Done in some "front-end" language
Python
C++ (Yeah, right)
Running a Graph
Model inputs typically "fed" in via placeholder ops
Model outputs specified by "fetching" certain ops
Trained values as Variable ops which must be initialized before execution
Demonstration
MNIST
Dataset containing images of handwritten digits. The classification problem of converting images to digits is a common benchmark for machine learning models.
Softmax Regression
1. Aggregate evidence in support of each class ("digit")
2. Convert the evidence into a likelihood for each given class
3. Train a set of weights to optimize the output probabilities.
