CS 20I : note 2

TensorFlow Ops

这是阅读 http://web.stanford.edu/class/cs20si/syllabus.html slides_02.pdf 的笔记.

• 基本操作
• Tensor 类型
• Project speed dating
• Placeholders and feeding inputs
• Lazy loading

第一个 TensorFlow 程序

import tensorflow as tfa = tf.constant(2)b = tf.constant(3)x = tf.add(a,b)with tf.Session() as sess:    print(sess.run(x))

5

使用 TensorBoard 可视化

TensorFlow 是一个可视化的工具, 可以展示运算过程中的计算图等信息.

import tensorflow as tfa = tf.constant(2)b = tf.constant(3)x = tf.add(a,b)with tf.Session() as sess:    # add this line to use TensorBoard.    writer = tf.summary.FileWriter("./graphs",sess.graph)    print(sess.run(x))writer.close() # close the writer when you're done using it

5

在本地文件,可以看到 graphs 目录中有一个文件.

启动 TensorBoard

打开 Anaconda Prompt, 激活到安装有 TensorFlow 的环境. 然后 cd 到 graphs 上层目录.
运行 tensorboard --logdir = graphs, 然后在浏览器中输入 http://localhost:6006/ 即可进入 TensorBoard.

在网页中点击 Graph 就可以看到一些东西了.

注意到这里两个是显示 Const, Const_1 , 那么如何给它们改名字呢?

import tensorflow as tfa = tf.constant(2,name = "a")b = tf.constant(3,name="b")x = tf.add(a,b,name="add")with tf.Session() as sess:    writer = tf.summary.FileWriter("./graphs",sess.graph)    print(sess.run(x))

5

再通过 tensorboard 可以看到 graph 对应节点有名字了

constant 的更多例子

tf.constant(value, dtype = None, shape = None, name = "Const", verify_shape = Flase)

import tensorflow as tf# 注意 a,b 维度不同, 在运算时发生了 boardcasting# 更多关于 https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html# https://www.tensorflow.org/api_guides/python/math_ops#Arithmetic_Operatorsa = tf.constant([2,2],name="a")b = tf.constant([[0,1],[2,3]],name="b")x = tf.add(a,b,name="add")y = tf.multiply(a,b,name="mul")with tf.Session() as sess:    x = sess.run(x)    print(x)    print('-----------------')    y = sess.run(y)    print(y)

[[2 3] [4 5]]-----------------[[0 2] [4 6]]

import tensorflow as tf# 生成指定维度的零 tensor# 一般定义: tf.zeros(shape, dtype=tf.float32, name=None)v1 = tf.zeros([3,4],tf.int32)# 值填充# tf.fill(dims, value, name = none)v2 = tf.fill([2,3],4)with tf.Session() as sess:    print(sess.run(v1))    print(sess.run(v2))

[[0 0 0 0] [0 0 0 0] [0 0 0 0]][[4 4 4] [4 4 4]]

Variables

# 官方文档: https://www.tensorflow.org/programmers_guide/variables## 创建变量import tensorflow as tfa = tf.Variable(2, name = "scalar")b = tf.Variable(tf.truncated_normal([2,3]))# 初始化变量# 一次初始化所有变量init = tf.global_variables_initializer()with tf.Session() as sess:    sess.run(init)    print(sess.run(a))    print(sess.run(b))# provide name and shape#v1 = tf.get_variable("v1",[1,2,3])#print(v1)

2[[-0.89090335 -0.62117159  0.95811218] [ 1.0819943   0.74583757 -0.73409617]]

import tensorflow as tf# 变量初始值为 10w = tf.Variable(10)# 赋值为 100w.assign(100)with tf.Session() as sess:    sess.run(w.initializer)    print(w.eval()) # 但是结果仍然是 10, 为什么

10

import tensorflow as tf# 变量初始值为 10w = tf.Variable(10)# 赋值为 100assign_op = w.assign(100)with tf.Session() as sess:    sess.run(assign_op)    print(w.eval()) # 这时的结果就正确了

100

Placeholders (占位符)

TF 程序通常有两个阶段:
- assemble a graph
- use a session to execute operations in the graph.

import tensorflow as tf# 创建一个 placeholdera = tf.placeholder(tf.float32,shape=[3])b = tf.constant([5,5,5],tf.float32)c = a + bwith tf.Session() as sess:    # 用字典来初始化 a    print(sess.run(c,{a:[1,2,3]}))

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