TensorFlow学习笔记2：基本运算

import tensorflow as tf

a = tf.constant(2)
b = tf.constant(3)

with tf.Session() as sess:
    print "a: %i" % sess.run(a), "b: %i" % sess.run(b)
    print "Addition with constants: %i" % sess.run(a+b)
    print "Multiplication with constants: %i" % sess.run(a*b)

代码中定义了a,b两个常量，分别为2和3，然后建立一个default session，输出a,b,a+b,a*b的值

在TensorFlow中，a,b也可以先不赋值，在运行过程中才赋具体的值，比如以下代码：

import tensorflow as tf

a = tf.placeholder(tf.int16)
b = tf.placeholder(tf.int16)

add = tf.add(a, b)
mul = tf.multiply(a, b)

with tf.Session() as sess:
    print "Addition with variables: %i" % sess.run(add, feed_dict={a: 1, b: 2})
    print "Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3})

tf.placeholder(dtype, shape=None, name=None) 可以理解成形参，在session run的时候，a,b有了具体的值

最后，TensorFlow的矩阵运算

import tensorflow as tf

matrix1 = tf.constant([[1., 2.]])

matrix2 = tf.constant([[3.],[4.]])

product = tf.matmul(matrix1, matrix2)

with tf.Session() as sess:
    result = sess.run(product)
    print result

matrix1是1行2列，matrix2是2行1列，可以注意一下矩阵的表示方法，两者相乘的结果是一个1行1列的矩阵，元素为11