Tensorflow Learning

Tensorflow Intro

1. Tensorflow core concept

Tensorflow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. A computing system based on graph.

Two steps using Tensorflow:

(1) Graph Built (operation + tensor)

Graph consisted of operations; Each operations of a graph connected together with Tensor, therefore the computing process in Tensorflow is a flow of Tensor.

(2) Graph implementation

Graph must be calulated adn implemented using Session(), session offers the env for operation implementation and tensor calucation.

code example:

import tensorflow as tf

# Build a graph.

a = tf.constant([1.0, 2.0])

b = tf.constant([3.0, 4.0])

c = a * b

# Launch the graph in a session.

sess = tf.Session()

# Evaluate the tensor 'c'.

print sess.run(c)

sess.close()

# result: [3., 8.]

2. Tensorflow详解

2.1 Tensorflow structure：

(1) Tensor

(2) Variable

(3) op