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

状压dp 持续更新
前置知识点:二进制状态压缩,动态规划. 1. AcWing 91 最短Hamilton路径 (https://www.acwing.com/problem/content/93/) 给定一张 n 个点 ...
post请求与get请求的差别
幂等的概念在理解这两者的区别前需要先了解的概念: 幂等性在编程中一个幂等操作的特点是其任意多次执行所产生的影响均与一次执行的影响相同.简单的说就是,浏览器发起一次请求和多次请求,不会改变目标资源的状 ...
canvas初尝试
最近学习了canvas,就拿它做了这么个小东西,感觉已经爱上canvas了.上代码 /* * @auhor : 开发部-前端组-李鑫超 * @property { tableData : {Array ...
从0开始搭建kafka客户端
上一节,我们实现了搭建kafka集群.本节我们将从0开始,使用Java,搭建kafka客户端生产消费模型. 1.创建maven项目2.kafka producer3.kafka consumer4.结 ...
微信小程序接入百度OCR（身份证识别）
微信小程序接入百度OCR(身份证识别) 1.接口描述支持对二代居民身份证正反面所有8个字段进行结构化识别,包括姓名.性别.民族.出生日期.住址.身份证号.签发机关.有效期限,识别准确率超过99%:同 ...
最新IntelliJ IDEA 2019.3版本永久激活，一步到位！
简单介绍一下什么是IDEA? IDEA全称 IntelliJ IDEA,是java编程语言开发的集成环境.IntelliJ在业界被公认为最好的java开发工具,尤其在智能代码助手.代码自动提示.重构. ...
并发工具类的使用 CountDownLatch,CyclicBarrier,Semaphore,Exchanger
1.CountDownLatch 允许一个或多个线程等待直到在其他线程中执行的一组操作完成的同步辅助. A CountDownLatch用给定的计数初始化. await方法阻塞,直到由于countDo ...
Redis系列二 - 数据结构
前言 redis作为我们开发的一大神器,我们接触肯定不会少,但是很多同学也许只会存储String类型的值,这是非常不合理的.在这里,将带大家认识Redis的5中数据结构. 1.问:Redis有那些数据 ...
用反射机制和pandas，实现excel数据的读取以及参数化${arg}的赋值
反射类:class GetData: index = pd.read_excel(file_name, sheet_name).loc[0, ['index']].values[0] email = ...
vector的初始化方式及用法笔记（不断更新）
vector的初始化方式 1)第一种,类似于数组的方式:vector<string> letter(3);letter[0] = "find";letter[1] = ...

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