TensorFlow 基本变量定义，基本操作，矩阵基本操作

使用 TensorFlow 进行基本操作的实例，这个实例主要是使用 TensorFlow 进行了加法运算。 包括使用 constant 常量进行加法运算和使用 placeholder 进行变量加法运算，以及扩展到矩阵的加法运算。 TensorFlow 变量定义，加法运算。

# -*- coding:utf-8 -*-from __future__ import print_function'''使用 TensorFlow 进行基本操作的实例，这个实例主要是使用 TensorFlow 进行了加法运算。包括使用 constant 常量进行加法运算和使用 placeholder 进行变量加法运算，以及扩展到矩阵的加法运算。TensorFlow 变量定义，加法运算。''''''Basic Operations example using TensorFlow library.Author: Aymeric DamienProject: https://github.com/aymericdamien/TensorFlow-Examples/'''import tensorflow as tf# Basic constant operations# The value returned by the constructor represents the output# of the Constant op.a = tf.constant(2)b = tf.constant(3)# Launch the default graph.with tf.Session() as sess:    print("a=2, b=3")    print("Addition with constants: %i" % sess.run(a+b))    print("Multiplication with constants: %i" % sess.run(a*b))# Basic Operations with variable as graph input# The value returned by the constructor represents the output# of the Variable op. (define as input when running session)# tf Graph inputa = tf.placeholder(tf.int16)b = tf.placeholder(tf.int16)# Define some operationsadd = tf.add(a, b)mul = tf.multiply(a, b)# Launch the default graph.with tf.Session() as sess:    # Run every operation with variable input    print("Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3}))    print("Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3}))# ----------------# More in details:# Matrix Multiplication from TensorFlow official tutorial# Create a Constant op that produces a 1x2 matrix.  The op is# added as a node to the default graph.## The value returned by the constructor represents the output# of the Constant op.matrix1 = tf.constant([[3., 3.]])# Create another Constant that produces a 2x1 matrix.matrix2 = tf.constant([[2.],[2.]])# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.# The returned value, 'product', represents the result of the matrix# multiplication.product = tf.matmul(matrix1, matrix2)# To run the matmul op we call the session 'run()' method, passing 'product'# which represents the output of the matmul op.  This indicates to the call# that we want to get the output of the matmul op back.## All inputs needed by the op are run automatically by the session.  They# typically are run in parallel.## The call 'run(product)' thus causes the execution of threes ops in the# graph: the two constants and matmul.## The output of the op is returned in 'result' as a numpy `ndarray` object.with tf.Session() as sess:    result = sess.run(product)    print(result)    # ==> [[ 12.]]

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