TensorFlow environment

TensorFlow environment
Assuming that the reader is familiar with Python, here we present the building blocks of TensorFlow
framework:
The Data Flow Graph To leverage the parallel computational power of multi-core CPU, GPU
and even clusters of GPUs, the dynamic of the numerical computations has been conceived as a
directed graph, where each node represents a mathematical operation and the edges describe the
input/output relation between nodes.
Tensor It is a typed n-dimensional array that flows through the Data Flow Graph.
Variable Symbolic objects designed to represent parameters. They are exploited to compute the
derivatives at a symbolical level, but in general must be explicitly initialized in a session.
Optimizer It is the component which provides methods to compute gradients from the loss function and to apply back-propagation through all the variables. A collection is available in TensorFlow
to implement classic optimization algorithms.
Session A graph must be launched in a Session, which places the graph onto CPU or GPU and

provides methods to run computation.

https://arxiv.org/abs/1703.05298