#coding=utf-8 import tensorflow as tf import numpy as np #constant(hello world),variable,session两种方式，用梯度下降拟合曲线，构建简易的神经网络层 def constant(): output = tf.constant('hello, tensorflow!') sess = tf.Session(); print sess.run(output) m1 = tf.constant([[1,2]]) m2 = tf.constant([[2,2],[3,3]]) m3 = tf.matmul(m1,m2) print sess.run(m3) sess.close() #constant(); def variable(): v1 = tf.Variable(0) newV1 = tf.add(v1,1) update = tf.assign(v1,newV1) with tf.Session() as sess: tf.global_variables_initializer().run() for i in range(0,10): #sess.run(newV1) sess.run(update) print sess.run(v1) #variable() x1 = tf.constant([[1,2],[3,3]]) x2 = tf.constant([[3,4],[4,4]]) x3 = tf.reduce_sum(tf.square(x2-x1),0) with tf.Session() as sess: print sess.run(x3)