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README.md
chapter10_Natural-Language-Process
- Char-RNN
- char_rnn
  - README.md
  - char_rnn.ipynb
  - config.py
  - dataset
    - jay.txt
    - poetry.txt
  - data
    - __init__.py
    - dataset.py
  - main.py
  - models
    - __init__.py
    - char_rnn.py

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`‎README.md‎`

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`@@ -81,7 +81,7 @@ Learn Deep Learning with PyTorch`
`81`	`81`	`- Deep Dream: 探索卷积网络眼中的世界`
`82`	`82`
`83`	`83`	`- Chapter 10: 自然语言处理`
`84`		`- - [Char RNN 实现文本生成](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter10_Natural-Language-Process/Char-RNN/)`
	`84`	`+ - [Char RNN 实现文本生成](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter10_Natural-Language-Process/char_rnn/)`
`85`	`85`	`- Image Caption: 实现图片字幕生成`
`86`	`86`	`- seq2seq 实现机器翻译`
`87`	`87`	`- cnn + rnn + attention 实现文本识别`

`‎chapter10_Natural-Language-Process/Char-RNN‎`

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`‎chapter10_Natural-Language-Process/char_rnn/README.md‎`

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	`1`	`+# Char-RNN-PyTorch`
	`2`	`+使用字符级别的RNN进行文本生成,使用PyTorch框架。[Gluon实现](https://github.com/SherlockLiao/Char-RNN-Gluon)`
	`3`	`+`
	`4`	`+## Requirements`
	`5`	`+[PyTorch 0.3](http://pytorch.org/)`
	`6`	`+`
	`7`	`+[MxTorch](https://github.com/SherlockLiao/mxtorch)`
	`8`	`+`
	`9`	`+[tensorboardX](https://github.com/lanpa/tensorboard-pytorch)`
	`10`	`+`
	`11`	`+按照 pytorch 官网安装 pytorch,将 mxtorch 下载下来,放到根目录,安装 tensorboardX 实现 tensorboard 可视化`
	`12`	`+`
	`13`	+```bash
	`14`	`+\Char-RNN-PyTorch`
	`15`	`+ \mxtorch`
	`16`	`+ \data`
	`17`	`+ \dataset`
	`18`	`+ \models`
	`19`	`+ config.py`
	`20`	`+ main.py`
	`21`	+```
	`22`	`+`
	`23`	`+`
	`24`	`+`
	`25`	`+### 训练模型`
	`26`	`+`
	`27`	`+所有的配置文件都放在 config.py 里面,通过下面的代码来训练模型`
	`28`	`+`
	`29`	+```bash
	`30`	`+python main.py train`
	`31`	+```
	`32`	`+`
	`33`	`+也可以在终端修改配置`
	`34`	`+`
	`35`	+```bash
	`36`	`+python main.py train \`
	`37`	`+ --txt='./dataset/poetry.txt' \ # 训练用的txt文本`
	`38`	`+ --batch=128 \ # batch_size`
	`39`	`+ --max_epoch=300 \`
	`40`	`+ --len=30 \ # 输入RNN的序列长度`
	`41`	`+ --max_vocab=5000 \ # 最大的字符数量`
	`42`	`+ --embed_dim=512 \ # 词向量的维度`
	`43`	`+ --hidden_size=512 \ # 网络的输出维度`
	`44`	`+ --num_layers=2 \ # RNN的层数`
	`45`	`+ --dropout=0.5`
	`46`	+```
	`47`	`+`
	`48`	`+如果希望使用训练好的网络进行文本生成,使用下面的代码`
	`49`	`+`
	`50`	+```bash
	`51`	`+python main.py predict \`
	`52`	`+ --begin='天青色等烟雨' \ # 生成文本的开始,可以是一个字符,也可以一段话`
	`53`	`+ --predict_len=100 \ # 希望生成文本的长度`
	`54`	`+ --load_model='./checkpoints/CharRNN_best_model.pth' # 读取训练模型的位置`
	`55`	+```
	`56`	`+`
	`57`	`+## Result`
	`58`	`+如果使用古诗的数据集进行训练,可以得到下面的结果`
	`59`	`+`
	`60`	+```bash
	`61`	`+天青色等烟雨翩黄望堪魄弦夜逐奏文明际天月辉豪天明月天趣天外何山重满遥天明上天心空游无拂天外空寂室叨`
	`62`	+```
	`63`	`+`
	`64`	`+如果使用周杰伦的歌词作为训练集,可以得到下面的结果`
	`65`	`+`
	`66`	+```bash
	`67`	`+这感觉得可能我这玻童来城堡药比生对这些年风天脚剧飘逐在尘里里步的路麦缘日下一经经听觉得远回白择`
	`68`	+```

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