TensorFlow RNN 教程和代码

分析：

看 TensorFlow 也有一段时间了，准备按照 GitHub 上的教程，敲出来，顺便整理一下思路。

RNN部分

定义参数，包括数据相关，训练相关。
定义模型，损失函数，优化函数。
训练，准备数据，输入数据，输出结果。

代码：

#!/usr/bin/env python

# -*- coding: utf-8 -*-

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

from tensorflow.contrib import rnn

mnist=input_data.read_data_sets("./data",one_hot=True)

training_rate=0.001

training_iters=100000

batch_size=128

display_step=10

n_input=28

n_steps=28

n_hidden=128

n_classes=10

x=tf.placeholder("float",[None,n_steps,n_input])

y=tf.placeholder("float",[None,n_classes])

weights={'out':tf.Variable(tf.random_normal([n_hidden,n_classes]))}

biases={'out':tf.Variable(tf.random_normal([n_classes]))}

def RNN(x,weights,biases):

   x=tf.unstack(x,n_steps,1)

   lstm_cell=rnn.BasicLSTMCell(n_hidden,forget_bias=1.0)

   outputs,states=rnn.static_rnn(lstm_cell,x,dtype=tf.float32)

   return tf.matmul(outputs[-1],weights['out'])+biases['out']

pred=RNN(x,weights,biases)

cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))

optimizer=tf.train.AdamOptimizer(learning_rate=training_rate).minimize(cost)

correct_pred=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))

accuaracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32))

init=tf.global_variables_initializer()

with tf.Session() as sess:

   sess.run(init)

   step=1

   while step*batch_size<training_iters:

      batch_x,batch_y=mnist.train.next_batch(batch_size)

      batch_x=batch_x.reshape(batch_size,n_steps,n_input)

      sess.run(optimizer,feed_dict={x:batch_x,y:batch_y})

      if step%display_step==0:

         acc=sess.run(accuaracy,feed_dict={x:batch_x,y:batch_y})

         loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})

         print("Iter " + str(step * batch_size) + ", Minibatch Loss= " + \

               "{:.6f}".format(loss) + ", Training Accuracy= " + \

               "{:.5f}".format(acc))

      step+=1

输出：

/anaconda/bin/python2.7 /Users/xxxx/PycharmProjects/TF_3/tf_rnn.py

Extracting ./data/train-images-idx3-ubyte.gz

Extracting ./data/train-labels-idx1-ubyte.gz

Extracting ./data/t10k-images-idx3-ubyte.gz

Extracting ./data/t10k-labels-idx1-ubyte.gz

2017-07-15 16:41:15.125981: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.

2017-07-15 16:41:15.125994: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.

2017-07-15 16:41:15.125997: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.

2017-07-15 16:41:15.126002: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.

Iter 1280, Minibatch Loss= 1.842738, Training Accuracy= 0.33594

Iter 2560, Minibatch Loss= 1.489123, Training Accuracy= 0.50000

Iter 3840, Minibatch Loss= 1.300060, Training Accuracy= 0.57812

Iter 5120, Minibatch Loss= 1.244872, Training Accuracy= 0.62500

Iter 6400, Minibatch Loss= 0.947143, Training Accuracy= 0.71094

Iter 7680, Minibatch Loss= 0.709695, Training Accuracy= 0.75781

Iter 8960, Minibatch Loss= 0.799844, Training Accuracy= 0.76562

Iter 10240, Minibatch Loss= 0.594611, Training Accuracy= 0.83594

Iter 11520, Minibatch Loss= 0.529350, Training Accuracy= 0.82031

Iter 12800, Minibatch Loss= 0.624426, Training Accuracy= 0.82031

Iter 14080, Minibatch Loss= 0.481889, Training Accuracy= 0.82812

Iter 15360, Minibatch Loss= 0.449692, Training Accuracy= 0.84375

Iter 16640, Minibatch Loss= 0.418820, Training Accuracy= 0.85938

Iter 17920, Minibatch Loss= 0.412161, Training Accuracy= 0.85156

Iter 19200, Minibatch Loss= 0.256099, Training Accuracy= 0.90625

Iter 20480, Minibatch Loss= 0.227309, Training Accuracy= 0.90625

Iter 21760, Minibatch Loss= 0.431014, Training Accuracy= 0.85938

Iter 23040, Minibatch Loss= 0.377097, Training Accuracy= 0.87500

Iter 24320, Minibatch Loss= 0.268153, Training Accuracy= 0.89844

Iter 25600, Minibatch Loss= 0.170557, Training Accuracy= 0.95312

Iter 26880, Minibatch Loss= 0.286947, Training Accuracy= 0.91406

Iter 28160, Minibatch Loss= 0.189623, Training Accuracy= 0.94531

Iter 29440, Minibatch Loss= 0.228949, Training Accuracy= 0.95312

Iter 30720, Minibatch Loss= 0.157198, Training Accuracy= 0.94531

Iter 32000, Minibatch Loss= 0.205744, Training Accuracy= 0.93750

Iter 33280, Minibatch Loss= 0.195218, Training Accuracy= 0.92188

Iter 34560, Minibatch Loss= 0.177956, Training Accuracy= 0.92969

Iter 35840, Minibatch Loss= 0.131563, Training Accuracy= 0.96875

Iter 37120, Minibatch Loss= 0.215156, Training Accuracy= 0.92969

Iter 38400, Minibatch Loss= 0.232274, Training Accuracy= 0.94531

Iter 39680, Minibatch Loss= 0.324053, Training Accuracy= 0.91406

Iter 40960, Minibatch Loss= 0.196385, Training Accuracy= 0.93750

Iter 42240, Minibatch Loss= 0.151221, Training Accuracy= 0.95312

Iter 43520, Minibatch Loss= 0.242021, Training Accuracy= 0.95312

Iter 44800, Minibatch Loss= 0.304008, Training Accuracy= 0.90625

Iter 46080, Minibatch Loss= 0.185177, Training Accuracy= 0.93750

Iter 47360, Minibatch Loss= 0.190960, Training Accuracy= 0.94531

Iter 48640, Minibatch Loss= 0.141995, Training Accuracy= 0.94531

Iter 49920, Minibatch Loss= 0.199995, Training Accuracy= 0.94531

Iter 51200, Minibatch Loss= 0.193773, Training Accuracy= 0.92188

Iter 52480, Minibatch Loss= 0.151757, Training Accuracy= 0.94531

Iter 53760, Minibatch Loss= 0.153755, Training Accuracy= 0.94531

Iter 55040, Minibatch Loss= 0.141472, Training Accuracy= 0.93750

Iter 56320, Minibatch Loss= 0.168057, Training Accuracy= 0.96094

Iter 57600, Minibatch Loss= 0.135691, Training Accuracy= 0.96094

Iter 58880, Minibatch Loss= 0.097003, Training Accuracy= 0.97656

Iter 60160, Minibatch Loss= 0.274090, Training Accuracy= 0.92188

Iter 61440, Minibatch Loss= 0.147230, Training Accuracy= 0.95312

Iter 62720, Minibatch Loss= 0.106019, Training Accuracy= 0.96094

Iter 64000, Minibatch Loss= 0.101133, Training Accuracy= 0.97656

Iter 65280, Minibatch Loss= 0.169548, Training Accuracy= 0.93750

Iter 66560, Minibatch Loss= 0.101966, Training Accuracy= 0.96094

Iter 67840, Minibatch Loss= 0.106501, Training Accuracy= 0.96875

Iter 69120, Minibatch Loss= 0.082817, Training Accuracy= 0.96875

Iter 70400, Minibatch Loss= 0.192926, Training Accuracy= 0.96094

Iter 71680, Minibatch Loss= 0.086935, Training Accuracy= 0.96875

Iter 72960, Minibatch Loss= 0.052052, Training Accuracy= 0.98438

Iter 74240, Minibatch Loss= 0.129968, Training Accuracy= 0.95312

Iter 75520, Minibatch Loss= 0.058070, Training Accuracy= 0.99219

Iter 76800, Minibatch Loss= 0.089518, Training Accuracy= 0.96875

Iter 78080, Minibatch Loss= 0.106092, Training Accuracy= 0.98438

Iter 79360, Minibatch Loss= 0.223101, Training Accuracy= 0.92188

Iter 80640, Minibatch Loss= 0.069419, Training Accuracy= 0.97656

Iter 81920, Minibatch Loss= 0.050585, Training Accuracy= 0.99219

Iter 83200, Minibatch Loss= 0.048002, Training Accuracy= 0.98438

Iter 84480, Minibatch Loss= 0.094293, Training Accuracy= 0.96875

Iter 85760, Minibatch Loss= 0.152253, Training Accuracy= 0.96094

Iter 87040, Minibatch Loss= 0.085382, Training Accuracy= 0.97656

Iter 88320, Minibatch Loss= 0.147018, Training Accuracy= 0.95312

Iter 89600, Minibatch Loss= 0.099780, Training Accuracy= 0.96094

Iter 90880, Minibatch Loss= 0.118362, Training Accuracy= 0.93750

Iter 92160, Minibatch Loss= 0.110498, Training Accuracy= 0.96094

Iter 93440, Minibatch Loss= 0.077664, Training Accuracy= 0.98438

Iter 94720, Minibatch Loss= 0.070865, Training Accuracy= 0.96094

Iter 96000, Minibatch Loss= 0.156309, Training Accuracy= 0.94531

Iter 97280, Minibatch Loss= 0.116825, Training Accuracy= 0.94531

Iter 98560, Minibatch Loss= 0.099852, Training Accuracy= 0.96875

Iter 99840, Minibatch Loss= 0.116358, Training Accuracy= 0.96875

Process finished with exit code 0

原文链接：http://www.tensorflownews.com/2017/07/15/tensorflow-rnn-turorial-mnist-code/

TensorFlow RNN 教程和代码的更多相关文章

tensorflow rnn 最简单实现代码
tensorflow rnn 最简单实现代码 #!/usr/bin/env python # -*- coding: utf-8 -*- import tensorflow as tf from te ...
Tensorflow快餐教程(1) - 30行代码搞定手写识别
版权声明:本文为博主原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接和本声明. 本文链接:https://blog.csdn.net/lusing/article/details ...
Tensorflow学习教程------读取数据、建立网络、训练模型，小巧而完整的代码示例
紧接上篇Tensorflow学习教程------tfrecords数据格式生成与读取,本篇将数据读取.建立网络以及模型训练整理成一个小样例,完整代码如下. #coding:utf-8 import t ...
TensorFlow 安装教程
1.准备好Anaconda环境 tensorflow是属于很高层的应用.高层应用的一个比较大的麻烦就是需要依赖的底层的东西很多,如果底层依赖没有弄好的话,高层应用是没法玩转的. 在极客学院有关tens ...
（转）干货｜这篇TensorFlow实例教程文章告诉你GANs为何引爆机器学习？（附源码）
干货|这篇TensorFlow实例教程文章告诉你GANs为何引爆机器学习?(附源码) 该博客来源自:https://mp.weixin.qq.com/s?__biz=MzA4NzE1NzYyMw==& ...
TensorFlow (RNN)深度学习双向LSTM(BiLSTM)+CRF 实现 sequence labeling 序列标注问题源码下载
http://blog.csdn.net/scotfield_msn/article/details/60339415 在TensorFlow (RNN)深度学习下双向LSTM(BiLSTM)+CR ...
云栖社区 Tensorflow快餐教程
云栖社区 Tensorflow快餐教程(1) - 30行代码搞定手写识别:https://yq.aliyun.com/articles/582122云栖社区 Tensorflow快餐教程(2) - 标 ...
Tensorflow学习教程------过拟合
Tensorflow学习教程------过拟合回归:过拟合情况 / 分类过拟合防止过拟合的方法有三种: 1 增加数据集 2 添加正则项 3 Dropout,意思就是训练的时候隐层神经元每次随机 ...
windows下用pycharm安装tensorflow简易教程
https://blog.csdn.net/heros_never_die/article/details/79760616 最近开始学习深度学习的相关知识,准备实战一下,看了一些关于tensorfl ...

随机推荐

记录一下自己写PHP程序时走过的一些坑
写在前面: 喔噢,转眼间发现自己正式开发程序(PHP)已经有快有1个月了,一路上走了许多的坑,有时遇到坑的时候真想放弃,但是还是坚持下来了!所以写了这篇文章来帮助那些刚刚接触PHP的小白们.[: )] ...
HTML标签学习总结（1）
1. <em>和<strong>标签是为了强调一段话中的关键字时使用,它们的语义是强调. 2. <span>标签是没有语义的,它的作用就是为了设置单独的样式用的. ...
webpack中打包拷贝静态文件CopyWebpackPlugin插件
copyWebpackPlugin: 作用:用于webpack打包时拷贝文件的插件包安装:npm install copyWebpackPlugin@版本号使用:// copy custom st ...
使用D3.js构建实时图形
首先你需要在计算机上安装Node和npm. 数据的可视化表示是传递复杂信息的最有效手段之一,D3.js提供了创建这些数据可视化的强大工具和灵活性. D3.js是一个JavaScript库,用于使用SV ...
ionic监听android返回键（实现“再按一次退出”功能）
在android平台上的app,在主页面时经常会遇到"再按一次退出app"的功能,避免只按一下返回键就退出app提升体验优化. 1.这个功能需要我们用到ionic提供的regist ...
docker部署tensorflow serving以及模型替换
Using TensorFlow Serving with Docker 1.Ubuntu16.04下安装docker ce 1-1:卸载旧版本的docker sudo apt-get remove ...
进阶之路 | 奇妙的Thread之旅
前言本文已经收录到我的Github个人博客,欢迎大佬们光临寒舍: 我的GIthub博客需要已经具备的知识: Thread的基本概念及使用 AsyncTask的基本概念及使用学习清单: 线程概述 ...
日常破解--XCTF easy_apk
一.题目来源来源:XCTF社区安卓题目easy_apk 二.破解思路 1.首先运行一下给的apk,发现就一个输入框和一个按钮,随便点击一下,发现弹出Toast验证失败.如下图所示: ...
Go组件学习：如何读取ini配置文件
代码示例全部保存在,欢迎star:https://github.com/EnochZg/golang-examples 安装组件 go get gopkg.in/ini.v1 使用先创建ini后缀的 ...
JVM进阶：JVM的监控利器
每次聊起性能测试,最后的终极话题就是怎么做优化.其实在Java的复杂项目中都会有内存不足问题.内存泄露问题.线程死锁问题.CPU问题.这些问题在小压力的情况下有可能并不明显,很容易被忽视.但是真正到了 ...

TensorFlow RNN 教程和代码

TensorFlow RNN 教程和代码的更多相关文章

随机推荐

热门专题