TensorFlow learning: intro

tensor

A tensor consists of a set of primitive values shaped into an array of any number of dimensions.

TensorFlow uses numpy arrays to represent tensor values.

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A tf.Tensor has the following properties:

• a data type
• a shape

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• rank

The rank of a tf.Tensor object is its number of dimensions. Synonyms for rank include order or degree or n-dimension.

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• shape

The shape of a tensor is the number of elements in each dimension.

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• tensor dimensionality:
  rank, shape, and dimension number.

tf.png

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tensorflow core

You might think of TensorFlow Core programs as consisting of two discrete sections:

1, Building the computational graph
2, Running the computational graph

• Graph

A computational graph is a series of TensorFlow operations arranged into a graph. The graph is composed of two types of objects.

Operations (or "ops"): The nodes of the graph. Operations describe calculations that consume and produce tensors.

Tensors: The edges in the graph.

• Session

A session encapsulates the state of the TensorFlow runtime, and runs TensorFlow operations. If a tf.Graph is like a .py file, a tf.Session is like the python executable.

Reference

• tf documentation: introduction

• tf documentation: tensor