Tensorflow - notes on programmers guide. 1. Basic concepts: TensorFlow separates definition of compuations from their execution. It first assemble a graph (tf.graph) and then use a session to execute operations in the graph (tf.session). A graph can be separated into subgraphs and execution can be computed in distributed manner. Tensor: n-dimensional array (not mathematically rigorous). Nodes (graphs): operators, variables and constants. Edges (graphs): tensors. tf.Session(): it encapsulates the environment in which operation objects are executed, and tensor objects are evaluated. It also allocate memory to store the current values of varaibles. tf.Graph(): creates a graph. tf.get_default_graph() acquires the default graph. Mixing this user created graph with default graph is prone to errors. tf.constant(): sets a specific value and cannot be changed. Note that this makes loading graphs expensive when constants are big. tf.Variable() or tf.get_variable(): a class with man