单层LSTM网络对MNIST数据集分类

2024-01-07 03:18
文章标签 数据 分类 网络 lstm mnist 单层

本文主要是介绍单层LSTM网络对MNIST数据集分类，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

单层LSTM网络对MNIST数据集分类

实验代码：（使用tensorflow框架）

# -*- coding: utf-8 -*-import tensorflow as tf
# 导入 MINST 数据集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/data/", one_hot=True)n_input = 28 # MNIST data 输入 (img shape: 28*28)
n_steps = 28 # timesteps
n_hidden = 128 # hidden layer num of features
n_classes = 10  # MNIST 列别 (0-9 ，一共10类)tf.reset_default_graph()# tf Graph input
x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_classes])x1 = tf.unstack(x, n_steps, 1)#1 BasicLSTMCell
lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x1, dtype=tf.float32)#2 LSTMCell
#lstm_cell = tf.contrib.rnn.LSTMCell(n_hidden, forget_bias=1.0)
#outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x1, dtype=tf.float32)#3 gru
#gru = tf.contrib.rnn.GRUCell(n_hidden)
#outputs = tf.contrib.rnn.static_rnn(gru, x1, dtype=tf.float32)#4 创建动态RNN
#outputs,_  = tf.nn.dynamic_rnn(gru,x,dtype=tf.float32)
#outputs = tf.transpose(outputs, [1, 0, 2])pred = tf.contrib.layers.fully_connected(outputs[-1],n_classes,activation_fn = None)learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# Evaluate model
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# 启动session
with tf.Session() as sess:sess.run(tf.global_variables_initializer())step = 1# Keep training until reach max iterationswhile step * batch_size < training_iters:batch_x, batch_y = mnist.train.next_batch(batch_size)# Reshape data to get 28 seq of 28 elementsbatch_x = batch_x.reshape((batch_size, n_steps, n_input))# Run optimization op (backprop)sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})if step % display_step == 0:# 计算批次数据的准确率acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})# Calculate batch lossloss = 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 += 1print (" Finished!")# 计算准确率 for 128 mnist test imagestest_len = 128test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))test_label = mnist.test.labels[:test_len]print ("Testing Accuracy:", \sess.run(accuracy, feed_dict={x: test_data, y: test_label}))

实验结果：
Iter 1280, Minibatch Loss= 2.098885, Training Accuracy= 0.30469
Iter 2560, Minibatch Loss= 1.772232, Training Accuracy= 0.38281
Iter 3840, Minibatch Loss= 1.404505, Training Accuracy= 0.52344
Iter 5120, Minibatch Loss= 1.321466, Training Accuracy= 0.57031
Iter 6400, Minibatch Loss= 1.020606, Training Accuracy= 0.65625
Iter 7680, Minibatch Loss= 0.767583, Training Accuracy= 0.76562
Iter 8960, Minibatch Loss= 0.945606, Training Accuracy= 0.66406
Iter 10240, Minibatch Loss= 0.643211, Training Accuracy= 0.78906
Iter 11520, Minibatch Loss= 0.737389, Training Accuracy= 0.76562
Iter 12800, Minibatch Loss= 0.589967, Training Accuracy= 0.83594
Iter 14080, Minibatch Loss= 0.432091, Training Accuracy= 0.89062
Iter 15360, Minibatch Loss= 0.375092, Training Accuracy= 0.90625
Iter 16640, Minibatch Loss= 0.509971, Training Accuracy= 0.82031
Iter 17920, Minibatch Loss= 0.431015, Training Accuracy= 0.85156
Iter 19200, Minibatch Loss= 0.420453, Training Accuracy= 0.85156
Iter 20480, Minibatch Loss= 0.338827, Training Accuracy= 0.88281
Iter 21760, Minibatch Loss= 0.427024, Training Accuracy= 0.86719
Iter 23040, Minibatch Loss= 0.419629, Training Accuracy= 0.87500
Iter 24320, Minibatch Loss= 0.343750, Training Accuracy= 0.90625
Iter 25600, Minibatch Loss= 0.232130, Training Accuracy= 0.92188
Iter 26880, Minibatch Loss= 0.491618, Training Accuracy= 0.89062
Iter 28160, Minibatch Loss= 0.226970, Training Accuracy= 0.92188
Iter 29440, Minibatch Loss= 0.287028, Training Accuracy= 0.91406
Iter 30720, Minibatch Loss= 0.348053, Training Accuracy= 0.90625
Iter 32000, Minibatch Loss= 0.232494, Training Accuracy= 0.92969
Iter 33280, Minibatch Loss= 0.294077, Training Accuracy= 0.89062
Iter 34560, Minibatch Loss= 0.269400, Training Accuracy= 0.90625
Iter 35840, Minibatch Loss= 0.257503, Training Accuracy= 0.92969
Iter 37120, Minibatch Loss= 0.176288, Training Accuracy= 0.95312
Iter 38400, Minibatch Loss= 0.263634, Training Accuracy= 0.89844
Iter 39680, Minibatch Loss= 0.350406, Training Accuracy= 0.89062
Iter 40960, Minibatch Loss= 0.175449, Training Accuracy= 0.94531
Iter 42240, Minibatch Loss= 0.311644, Training Accuracy= 0.89844
Iter 43520, Minibatch Loss= 0.202412, Training Accuracy= 0.92188
Iter 44800, Minibatch Loss= 0.238732, Training Accuracy= 0.92188
Iter 46080, Minibatch Loss= 0.262362, Training Accuracy= 0.91406
Iter 47360, Minibatch Loss= 0.277031, Training Accuracy= 0.92188
Iter 48640, Minibatch Loss= 0.167007, Training Accuracy= 0.93750
Iter 49920, Minibatch Loss= 0.208343, Training Accuracy= 0.95312
Iter 51200, Minibatch Loss= 0.237634, Training Accuracy= 0.91406
Iter 52480, Minibatch Loss= 0.133993, Training Accuracy= 0.96094
Iter 53760, Minibatch Loss= 0.255377, Training Accuracy= 0.92188
Iter 55040, Minibatch Loss= 0.204812, Training Accuracy= 0.92969
Iter 56320, Minibatch Loss= 0.183624, Training Accuracy= 0.92969
Iter 57600, Minibatch Loss= 0.131443, Training Accuracy= 0.96094
Iter 58880, Minibatch Loss= 0.096448, Training Accuracy= 0.97656
Iter 60160, Minibatch Loss= 0.163977, Training Accuracy= 0.96875
Iter 61440, Minibatch Loss= 0.185323, Training Accuracy= 0.95312
Iter 62720, Minibatch Loss= 0.107512, Training Accuracy= 0.97656
Iter 64000, Minibatch Loss= 0.174152, Training Accuracy= 0.95312
Iter 65280, Minibatch Loss= 0.173235, Training Accuracy= 0.95312
Iter 66560, Minibatch Loss= 0.115825, Training Accuracy= 0.96875
Iter 67840, Minibatch Loss= 0.190322, Training Accuracy= 0.92969
Iter 69120, Minibatch Loss= 0.073072, Training Accuracy= 0.97656
Iter 70400, Minibatch Loss= 0.161416, Training Accuracy= 0.93750
Iter 71680, Minibatch Loss= 0.148715, Training Accuracy= 0.95312
Iter 72960, Minibatch Loss= 0.174622, Training Accuracy= 0.95312
Iter 74240, Minibatch Loss= 0.100780, Training Accuracy= 0.97656
Iter 75520, Minibatch Loss= 0.177840, Training Accuracy= 0.96094
Iter 76800, Minibatch Loss= 0.119568, Training Accuracy= 0.96094
Iter 78080, Minibatch Loss= 0.116565, Training Accuracy= 0.96094
Iter 79360, Minibatch Loss= 0.124705, Training Accuracy= 0.96094
Iter 80640, Minibatch Loss= 0.068246, Training Accuracy= 0.97656
Iter 81920, Minibatch Loss= 0.152009, Training Accuracy= 0.97656
Iter 83200, Minibatch Loss= 0.150834, Training Accuracy= 0.96094
Iter 84480, Minibatch Loss= 0.082806, Training Accuracy= 0.98438
Iter 85760, Minibatch Loss= 0.239210, Training Accuracy= 0.94531
Iter 87040, Minibatch Loss= 0.194339, Training Accuracy= 0.94531
Iter 88320, Minibatch Loss= 0.141747, Training Accuracy= 0.96094
Iter 89600, Minibatch Loss= 0.110870, Training Accuracy= 0.97656
Iter 90880, Minibatch Loss= 0.066232, Training Accuracy= 0.98438
Iter 92160, Minibatch Loss= 0.085497, Training Accuracy= 0.96875
Iter 93440, Minibatch Loss= 0.141791, Training Accuracy= 0.96094
Iter 94720, Minibatch Loss= 0.143089, Training Accuracy= 0.93750
Iter 96000, Minibatch Loss= 0.234196, Training Accuracy= 0.93750
Iter 97280, Minibatch Loss= 0.143507, Training Accuracy= 0.94531
Iter 98560, Minibatch Loss= 0.069923, Training Accuracy= 0.96875
Iter 99840, Minibatch Loss= 0.079662, Training Accuracy= 0.98438
Finished!
Testing Accuracy: 0.976562

参考资料：《深度学习之Tensorflow》李金洪编著

这篇关于单层LSTM网络对MNIST数据集分类的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！


http://www.chinasem.cn/article/578550。

相关文章

SpringBoot集成Milvus实现数据增删改查功能

SpringBoot集成Milvus实现数据增删改查功能

《SpringBoot集成Milvus实现数据增删改查功能》milvus支持的语言比较多,支持python,Java,Go,node等开发语言,本文主要介绍如何使用Java语言,采用springboo... 目录1、Milvus基本概念2、添加maven依赖3、配置yml文件4、创建MilvusClient
阅读更多...
SpringValidation数据校验之约束注解与分组校验方式

SpringValidation数据校验之约束注解与分组校验方式

《SpringValidation数据校验之约束注解与分组校验方式》本文将深入探讨SpringValidation的核心功能,帮助开发者掌握约束注解的使用技巧和分组校验的高级应用,从而构建更加健壮和可... 目录引言一、Spring Validation基础架构1.1 jsR-380标准与Spring整合1
阅读更多...
MySQL 中查询 VARCHAR 类型 JSON 数据的问题记录

MySQL 中查询 VARCHAR 类型 JSON 数据的问题记录

《MySQL中查询VARCHAR类型JSON数据的问题记录》在数据库设计中,有时我们会将JSON数据存储在VARCHAR或TEXT类型字段中,本文将详细介绍如何在MySQL中有效查询存储为V... 目录一、问题背景二、mysql jsON 函数2.1 常用 JSON 函数三、查询示例3.1 基本查询3.2
阅读更多...
SpringBatch数据写入实现

SpringBatch数据写入实现

《SpringBatch数据写入实现》SpringBatch通过ItemWriter接口及其丰富的实现,提供了强大的数据写入能力,本文主要介绍了SpringBatch数据写入实现,具有一定的参考价值,... 目录python引言一、ItemWriter核心概念二、数据库写入实现三、文件写入实现四、多目标写入
阅读更多...
使用Python将JSON,XML和YAML数据写入Excel文件

使用Python将JSON,XML和YAML数据写入Excel文件

《使用Python将JSON,XML和YAML数据写入Excel文件》JSON、XML和YAML作为主流结构化数据格式,因其层次化表达能力和跨平台兼容性,已成为系统间数据交换的通用载体,本文将介绍如何... 目录如何使用python写入数据到Excel工作表用Python导入jsON数据到Excel工作表用
阅读更多...
Mysql如何将数据按照年月分组的统计

Mysql如何将数据按照年月分组的统计

《Mysql如何将数据按照年月分组的统计》：本文主要介绍Mysql如何将数据按照年月分组的统计方式,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教... 目录mysql将数据按照年月分组的统计要的效果方案总结Mysql将数据按照年月分组的统计要的效果方案① 使用 DA
阅读更多...
鸿蒙中Axios数据请求的封装和配置方法

鸿蒙中Axios数据请求的封装和配置方法

《鸿蒙中Axios数据请求的封装和配置方法》：本文主要介绍鸿蒙中Axios数据请求的封装和配置方法,本文给大家介绍的非常详细,对大家的学习或工作具有一定的参考借鉴价值,需要的朋友参考下吧... 目录1.配置权限 应用级权限和系统级权限2.配置网络请求的代码3.下载在Entry中 下载AxIOS4.封装Htt
阅读更多...
Python获取中国节假日数据记录入JSON文件

Python获取中国节假日数据记录入JSON文件

《Python获取中国节假日数据记录入JSON文件》项目系统内置的日历应用为了提升用户体验,特别设置了在调休日期显示“休”的UI图标功能,那么问题是这些调休数据从哪里来呢？我尝试一种更为智能的方法：P... 目录节假日数据获取存入jsON文件节假日数据读取封装完整代码项目系统内置的日历应用为了提升用户体验，
阅读更多...
Linux系统配置NAT网络模式的详细步骤(附图文)

Linux系统配置NAT网络模式的详细步骤(附图文)

《Linux系统配置NAT网络模式的详细步骤(附图文)》本文详细指导如何在VMware环境下配置NAT网络模式,包括设置主机和虚拟机的IP地址、网关,以及针对Linux和Windows系统的具体步骤,... 目录一、配置NAT网络模式二、设置虚拟机交换机网关2.1 打开虚拟机2.2 管理员授权2.3 设置子
阅读更多...
揭秘Python Socket网络编程的7种硬核用法

揭秘Python Socket网络编程的7种硬核用法

《揭秘PythonSocket网络编程的7种硬核用法》Socket不仅能做聊天室,还能干一大堆硬核操作,这篇文章就带大家看看Python网络编程的7种超实用玩法,感兴趣的小伙伴可以跟随小编一起... 目录1.端口扫描器：探测开放端口2.简易 HTTP 服务器：10 秒搭个网页3.局域网游戏：多人联机对战4.
阅读更多...

Copyright China编程 23002807@qq.com

沪ICP备2023033072号-2