DL之RNN:人工智能为你写小说——基于TF利用RNN算法训练数据集(William Shakespeare的《Coriolanus》)替代你写英语小说短文、训练&测试过程全记录

简介: DL之RNN:人工智能为你写小说——基于TF利用RNN算法训练数据集(William Shakespeare的《Coriolanus》)替代你写英语小说短文、训练&测试过程全记录

输出结果


1、test01


conce alone,

Which treason thines, and true a mercy with the man,

And honour the sheet in my stal and taste and him,

I house your servants there, and a shall some

They take a thing a bate of him, but then, but he is were

Too hath to the match her wanders there he wants.

APRINS:

The sender, and the that's all him at thy bloods;

To thee an ast that that the solet is a wealer this thar was a

will seemen of the than a court to me were burist.

LUCIANO:

I am how, an are this holds and to her sad

Woo on your toon and as thing, have the will

A show that's tears of my satate, some are.

SIR TOBY RELLA:

If the word of her of this sad of my mans are?

ALINA:

I should say thou the thee with an erms, we'll he is with his better.

ANTELIUS:

Will you have those stalling and as a wild

In that, have would say well, he will be string that all her

to here, that a bosom and at as my bands to--they

women is shall say this wears thee;

Hath see, a say me off the honour to the with

That wither and sting, and

独自一人,

哪一个叛逆变瘦,真正的仁慈,

并在我的石板上品尝这张纸,品尝他,

我把你的仆人安放在那里

他们把一件事拿给他听,但他现在是

她想去的地方,也有她的对手。

APRINS:

发送者,那就是他在你的血液里;

对你来说,一个让自己成为一个富人的圣地

对我来说似乎比法庭更重要。

LUCIANO:

我是如何,这是持有和她的悲伤

对你的香椿和作为事物,有意志

这是我的眼泪,有些是。

SIR TOBY RELLA:

如果她的话我的男人的这种悲伤是什么?

艾琳娜:

我应该说你是一个厄尔默斯,我们会和他更好相处。

ANTELIUS:

你会有那些失速和野性吗?

在那,有人会说,他会把她所有的绳子

到这里,那一个胸怀和我的乐队

女人应该说这穿了你;

看见了,向我告别

枯萎和刺痛,

2、test02


fort,

To hear mine aldouse, and hath have a tongue of her as mine.

SILVIA:

And so and merty to this hand,

To seal you we would take the thought in a time.

POLANIA:

I am all their mind, that this the tongue.

ANDELIO:

He thither at my badis, tink as thou

handly; and and all thy last at tell thee

Tell you sake a court of thou to my store with him of my shame

To any shild a shild and heaven as her throog

As his meant and hath to my man and sence,

I had all the witere the storal one worth.

PRINCE HENRY:

He hath something to stay.

SIR OF ORIANIO:

He have troubles here.

LAUVANICUS:

With made that the stally that to this horses, and terter.

PALIS:

Is have this true any stone to the with the sold,

I were a master, the tongue, that a sharlon on

me to he saw and may thy stallangers to streak,

The weads to the more so fiers in thee. And you think

What is, to honour to thy house at

their stays and hand, whell string, thyness would have this,

A soul to-news would be the his hands.

He love you

堡垒,

听我的话,她有我的舌头。

SILVIA:

所以,梅蒂,这只手,

为了封你,我们会在一段时间内接受这个想法。

POLANIA:

我都是他们的头脑,这就是舌头。

ANDELIO:

他在我的坏蛋那里,丁克如你

你最后一次告诉你

你把我的羞辱告诉你我的商店

对任何一个阴霾和天堂作为她的悸动

正如他对我的男人和我所拥有的,

我拥有所有值得收藏的东西。

PRINCE HENRY:

他有事要留下。

奥里亚尼奥爵士:

他这里有麻烦。

LAUVANICUS:

就这样把那匹马吓了一跳。

PALIS:

是真的有什么石头卖给谁,

我是一个大师,舌头,一个沙龙

我见他,愿你的斯塔兰格连任,

在你身上,维斯的音符越来越高。你认为

什么是荣耀你的家

他们的停留和手,弦,thyess都有这个,

新闻的灵魂是他的手。

他爱你

监控模型





训练过程全记录


2018-10-13 17:05:49.402137:

step: 10/20000...  loss: 3.4659...  0.1860 sec/batch

……

step: 1000/20000...  loss: 2.0612...  0.1168 sec/batch

……

step: 2000/20000...  loss: 1.9092...  0.1278 sec/batch

……

step: 3000/20000...  loss: 1.8643...  0.1283 sec/batch

……

step: 10000/20000...  loss: 1.8001...  0.1329 sec/batch

……

step: 15000/20000...  loss: 1.7402...  0.1689 sec/batch

step: 15010/20000...  loss: 1.8033...  0.2306 sec/batch

step: 15020/20000...  loss: 1.8284...  0.1499 sec/batch

step: 15030/20000...  loss: 1.7952...  0.1359 sec/batch

step: 15040/20000...  loss: 1.7906...  0.1514 sec/batch

step: 15050/20000...  loss: 1.7777...  0.1053 sec/batch

step: 15060/20000...  loss: 1.7665...  0.1298 sec/batch

step: 15070/20000...  loss: 1.7931...  0.1183 sec/batch

step: 15080/20000...  loss: 1.8027...  0.1404 sec/batch

step: 15090/20000...  loss: 1.8116...  0.1238 sec/batch

step: 15100/20000...  loss: 1.7969...  0.1108 sec/batch

……

step: 19800/20000...  loss: 1.8298...  0.1233 sec/batch

step: 19810/20000...  loss: 1.8231...  0.1228 sec/batch

step: 19820/20000...  loss: 1.7674...  0.1329 sec/batch

step: 19830/20000...  loss: 1.7872...  0.1434 sec/batch

step: 19840/20000...  loss: 1.8333...  0.1228 sec/batch

step: 19850/20000...  loss: 1.6446...  0.1464 sec/batch

step: 19860/20000...  loss: 1.8021...  0.1509 sec/batch

step: 19870/20000...  loss: 1.8217...  0.1168 sec/batch

step: 19880/20000...  loss: 1.7298...  0.1178 sec/batch

step: 19890/20000...  loss: 1.6948...  0.1293 sec/batch

step: 19900/20000...  loss: 1.7582...  0.1253 sec/batch

step: 19910/20000...  loss: 1.8246...  0.1414 sec/batch

step: 19920/20000...  loss: 1.7258...  0.1103 sec/batch

step: 19930/20000...  loss: 1.8216...  0.1544 sec/batch

step: 19940/20000...  loss: 1.7866...  0.1243 sec/batch

step: 19950/20000...  loss: 1.7673...  0.1088 sec/batch

step: 19960/20000...  loss: 1.7285...  0.1088 sec/batch

step: 19970/20000...  loss: 1.7658...  0.1073 sec/batch

step: 19980/20000...  loss: 1.8054...  0.1198 sec/batch

step: 19990/20000...  loss: 1.7714...  0.1128 sec/batch

step: 20000/20000...  loss: 1.7530...  0.1228 sec/batch

训练的数据集


           《科利奥兰纳斯》是莎士比亚晚年撰写的一部罗马历史悲剧,讲述了罗马共和国的英雄马歇斯(被称为科利奥兰纳斯),因性格多疑、脾气暴躁,得罪了公众而被逐出罗马的悲剧。作者以英雄与群众的关系为主线,揭示出人性的弱点。


1、部分章节


First Citizen:

Before we proceed any further, hear me speak.

All:

Speak, speak.

First Citizen:

You are all resolved rather to die than to famish?

All:

Resolved. resolved.

First Citizen:

First, you know Caius Marcius is chief enemy to the people.

All:

We know't, we know't.

First Citizen:

Let us kill him, and we'll have corn at our own price.

Is't a verdict?

All:

No more talking on't; let it be done: away, away!

Second Citizen:

One word, good citizens.

First Citizen:

We are accounted poor citizens, the patricians good.

What authority surfeits on would relieve us: if they

would yield us but the superfluity, while it were

wholesome, we might guess they relieved us humanely;

but they think we are too dear: the leanness that

afflicts us, the object of our misery, is as an

inventory to particularise their abundance; our

sufferance is a gain to them Let us revenge this with

our pikes, ere we become rakes: for the gods know I

speak this in hunger for bread, not in thirst for revenge.

Second Citizen:

Would you proceed especially against Caius Marcius?

All:

Against him first: he's a very dog to the commonalty.

Second Citizen:

Consider you what services he has done for his country?

First Citizen:

Very well; and could be content to give him good

report fort, but that he pays himself with being proud.

Second Citizen:

Nay, but speak not maliciously.

First Citizen:

I say unto you, what he hath done famously, he did

it to that end: though soft-conscienced men can be

content to say it was for his country he did it to

please his mother and to be partly proud; which he

is, even till the altitude of his virtue.

Second Citizen:

What he cannot help in his nature, you account a

vice in him. You must in no way say he is covetous.

First Citizen:

If I must not, I need not be barren of accusations;

he hath faults, with surplus, to tire in repetition.

What shouts are these? The other side o' the city

is risen: why stay we prating here? to the Capitol!

All:

Come, come.

First Citizen:

Soft! who comes here?

Second Citizen:

Worthy Menenius Agrippa; one that hath always loved

the people.

First Citizen:

He's one honest enough: would all the rest were so!





相关文章
|
9月前
|
数据采集 人工智能 监控
人工智能驱动的软件工程:测试左移的崛起价值
本文探讨了人工智能驱动下测试左移理念在软件工程中的重要性,分析测试工程师在需求评估、AI代码生成及遗留系统优化中的关键作用,揭示AI带来的挑战与机遇,并指出测试工程师需提升技能、关注合规与可维护性,以在AI时代保障软件质量。
501 89
|
7月前
|
机器学习/深度学习 人工智能 测试技术
EdgeMark:嵌入式人工智能工具的自动化与基准测试系统——论文阅读
EdgeMark是一个面向嵌入式AI的自动化部署与基准测试系统,支持TensorFlow Lite Micro、Edge Impulse等主流工具,通过模块化架构实现模型生成、优化、转换与部署全流程自动化,并提供跨平台性能对比,助力开发者在资源受限设备上高效选择与部署AI模型。
657 9
EdgeMark:嵌入式人工智能工具的自动化与基准测试系统——论文阅读
|
机器学习/深度学习 算法 数据挖掘
K-means聚类算法是机器学习中常用的一种聚类方法,通过将数据集划分为K个簇来简化数据结构
K-means聚类算法是机器学习中常用的一种聚类方法,通过将数据集划分为K个簇来简化数据结构。本文介绍了K-means算法的基本原理,包括初始化、数据点分配与簇中心更新等步骤,以及如何在Python中实现该算法,最后讨论了其优缺点及应用场景。
1638 6
|
9月前
|
机器学习/深度学习 存储 算法
强化学习算法基准测试:6种算法在多智能体环境中的表现实测
本文系统研究了多智能体强化学习的算法性能与评估框架,选用井字棋和连珠四子作为基准环境,对比分析Q-learning、蒙特卡洛、Sarsa等表格方法在对抗场景中的表现。实验表明,表格方法在小规模状态空间(如井字棋)中可有效学习策略,但在大规模状态空间(如连珠四子)中因泛化能力不足而失效,揭示了向函数逼近技术演进的必要性。研究构建了标准化评估流程,明确了不同算法的适用边界,为理解强化学习的可扩展性问题提供了实证支持与理论参考。
477 0
强化学习算法基准测试:6种算法在多智能体环境中的表现实测
|
算法 数据安全/隐私保护 计算机视觉
基于FPGA的图像双线性插值算法verilog实现,包括tb测试文件和MATLAB辅助验证
本项目展示了256×256图像通过双线性插值放大至512×512的效果,无水印展示。使用Matlab 2022a和Vivado 2019.2开发,提供完整代码及详细中文注释、操作视频。核心程序实现图像缩放,并在Matlab中验证效果。双线性插值算法通过FPGA高效实现图像缩放,确保质量。
|
10月前
|
自然语言处理 算法 数据可视化
文本聚类效果差?5种主流算法性能测试帮你找到最佳方案
本文探讨了自然语言处理中句子嵌入的聚类技术,使用Billingsmoore数据集(925个英语句子)进行实验。通过生成句子嵌入向量并可视化分析,对比了K-Means、DBSCAN、HDBSCAN、凝聚型层次聚类和谱聚类等算法的表现。结果表明,K-Means适合已知聚类数量的场景,DBSCAN和HDBSCAN适用于未知聚类数量且存在异常值的情况,而谱聚类在句子嵌入领域表现不佳。最终建议根据数据特征和计算资源选择合适的算法以实现高质量聚类。
753 0
文本聚类效果差?5种主流算法性能测试帮你找到最佳方案
|
机器学习/深度学习 算法 数据可视化
利用SVM(支持向量机)分类算法对鸢尾花数据集进行分类
本文介绍了如何使用支持向量机(SVM)算法对鸢尾花数据集进行分类。作者通过Python的sklearn库加载数据,并利用pandas、matplotlib等工具进行数据分析和可视化。
1271 70
|
人工智能 安全 测试技术
Burp Suite Professional 2025.3 发布,引入 Burp AI 通过人工智能增强安全测试工作流程
Burp Suite Professional 2025.3 发布,引入 Burp AI 通过人工智能增强安全测试工作流程
872 0
Burp Suite Professional 2025.3 发布,引入 Burp AI 通过人工智能增强安全测试工作流程
|
机器学习/深度学习 监控 计算机视觉
目标检测实战(八): 使用YOLOv7完成对图像的目标检测任务(从数据准备到训练测试部署的完整流程)
本文介绍了如何使用YOLOv7进行目标检测,包括环境搭建、数据集准备、模型训练、验证、测试以及常见错误的解决方法。YOLOv7以其高效性能和准确率在目标检测领域受到关注,适用于自动驾驶、安防监控等场景。文中提供了源码和论文链接,以及详细的步骤说明,适合深度学习实践者参考。
4066 1
目标检测实战(八): 使用YOLOv7完成对图像的目标检测任务(从数据准备到训练测试部署的完整流程)
|
并行计算 算法 测试技术
C语言因高效灵活被广泛应用于软件开发。本文探讨了优化C语言程序性能的策略,涵盖算法优化、代码结构优化、内存管理优化、编译器优化、数据结构优化、并行计算优化及性能测试与分析七个方面
C语言因高效灵活被广泛应用于软件开发。本文探讨了优化C语言程序性能的策略,涵盖算法优化、代码结构优化、内存管理优化、编译器优化、数据结构优化、并行计算优化及性能测试与分析七个方面,旨在通过综合策略提升程序性能,满足实际需求。
601 1

热门文章

最新文章