开发者峰会&＃xff1a;TensorflowLite

中文官网&＃xff1a;https&＃xff1a;//tensorflow.google.cn/

中文社区&＃xff1a;http://www.tensorfly.cn/

一、常量与变量

import tensorflow as tf
import numpy as np
print(tf.__version__)#变量定义的几种方法
def var_demo():# 1tha &＃61; tf.Variable(tf.linspace(-5.,5.,10),dtype&＃61;tf.float32)# 2thb &＃61; tf.Variable(tf.random_normal([3,3], mean&＃61;1.0,stddev&＃61;2.0, dtype&＃61;tf.float32), dtype&＃61;tf.float32)# 3thc &＃61; tf.Variable(b.initialized_value(), dtype&＃61;tf.float32)# 4thd &＃61; tf.Variable(tf.zeros([3,3], dtype&＃61;tf.float32), dtype&＃61;tf.float32)# 5the &＃61; tf.assign(a, tf.linspace(-1.,1.,10))f &＃61; tf.cast(e, dtype&＃61;tf.int32)c1 &＃61; tf.constant(3)c2 &＃61; tf.constant([2,3])# 初始化方式init &＃61; tf.global_variables_initializer()sess &＃61; tf.Session()sess.run(init)print(sess.run(a))print(sess.run(b))print(sess.run(c))print(sess.run(d))print(sess.run(e))print(sess.run(f))print(sess.run(c1))print(sess.run(c2))var_demo()


二、操作数与占位符

import tensorflow as tf
import numpy as np
print(tf.__version__)def ops_demo():a &＃61; tf.constant([[1, 2, 3], [4, 5, 6]])b &＃61; tf.constant(4)c &＃61; tf.Variable(tf.random_normal([2,3], 1.0,3.0), dtype&＃61;tf.float32)d &＃61; tf.add(c, tf.cast(tf.divide(a, b), dtype&＃61;tf.float32))m1 &＃61; tf.Variable(tf.random_normal([3, 3], 1.0, 3), dtype&＃61;tf.float32)m2 &＃61; tf.Variable(tf.random_normal([3, 3], 3.0, 1), dtype&＃61;tf.float32)m3 &＃61; tf.add(m1, m2)m4 &＃61; tf.subtract(m1, m2)m5 &＃61; tf.divide(m1, m2)m6 &＃61; tf.multiply(m1, m2)mm &＃61; tf.matmul(m1, m2)x &＃61; tf.placeholder(shape&＃61;[3,3], dtype&＃61;tf.float32)y &＃61; tf.placeholder(shape&＃61;[3,2], dtype&＃61;tf.float32)xy &＃61; tf.matmul(x, y)#初始化init &＃61; tf.global_variables_initializer()sess &＃61; tf.Session()sess.run(init)xy_result &＃61; sess.run(xy, feed_dict&＃61;{x: [[1,1,1], [2,2,2],[3,3,3]], y:[[4,4],[5,5],[6,6]]})print(xy_result)ops_demo()