# lstm
A basic lstm network can be written from scratch in a few hundred lines of python, yet most of us have a hard time figuring out how lstm's actually work. The original Neural Computation [paper](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0CDAQFjACahUKEwj1iZLX5efGAhVMpIgKHbv3DiI&url=http%3A%2F%2Fdeeplearning.cs.cmu.edu%2Fpdfs%2FHochreiter97_lstm.pdf&ei=ZuirVfW-GMzIogS777uQAg&usg=AFQjCNGoFvqrva4rDCNIcqNe_SiPL_VPxg&sig2=ZYnsGpdfHjRbK8xdr1thBg&bvm=bv.98197061,d.cGU) is too technical for non experts. Most blogs online on the topic seem to be written by people
who have never implemented lstm's for people who will not implement them either. Other blogs are written by experts (like this [blog post](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)) and lack simplified illustrative source code that actually does something. The [Apollo](https://github.com/Russell91/apollo) library built on top of caffe is terrific and features a fast lstm implementation. However, the downside of efficient implementations is that the source code is hard to follow.
This repo features a minimal lstm implementation for people that are curious about lstms to the point of wanting to know how lstm's might be implemented. The code here follows notational conventions set forth in [this](http://arxiv.org/abs/1506.00019)
well written tutorial introduction. This article should be read before trying to understand this code (at least the part about lstm's). By running `python test.py` you will have a minimal example of an lstm network learning to predict an output sequence of numbers in [-1,1] by using a Euclidean loss on the first element of each node's hidden layer.
Play with code, add functionality, and try it on different datasets. Pull requests welcome.
Please read [my blog article](http://nicodjimenez.github.io/2014/08/08/lstm.html) if you want details on the backprop part of the code.
Also, check out a version of this code written in the D programming language by Mathias Baumann: https://github.com/Marenz/lstm
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基于python的聊天机器人源码.zip本资源中的源码都是经过本地编译过可运行的,资源项目的难度比较适中,内容都是经过助教老师审定过的能够满足学习、使用需求,如果有需要的话可以放心下载使用。 基于python的聊天机器人源码.zip本资源中的源码都是经过本地编译过可运行的,资源项目的难度比较适中,内容都是经过助教老师审定过的能够满足学习、使用需求,如果有需要的话可以放心下载使用。 基于python的聊天机器人源码.zip本资源中的源码都是经过本地编译过可运行的,资源项目的难度比较适中,内容都是经过助教老师审定过的能够满足学习、使用需求,如果有需要的话可以放心下载使用。 基于python的聊天机器人源码.zip本资源中的源码都是经过本地编译过可运行的,资源项目的难度比较适中,内容都是经过助教老师审定过的能够满足学习、使用需求,如果有需要的话可以放心下载使用。 基于python的聊天机器人源码.zip本资源中的源码都是经过本地编译过可运行的,资源项目的难度比较适中,内容都是经过助教老师审定过的能够满足学习、使用需求,如果有需要的话可以放心下载使用。基于python的聊天机器人
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基于python的聊天机器人源码.zip (153个子文件)
answer 16KB
word2vec.c 26KB
word2phrase.c 9KB
compute-accuracy.c 5KB
word-analogy.c 5KB
distance.c 4KB
read_images.c 3KB
scrapy.cfg 276B
scrapy.cfg 268B
main2012.dic 2.91MB
main2012.dic 2.91MB
quantifier.dic 2KB
quantifier.dic 2KB
stopword.dic 316B
ext.dic 44B
.DS_Store 6KB
.gitignore 76B
graph 23KB
result.html 202KB
train.input 256B
test.input 40B
IKQueryExpressionParser.java 19KB
AnalyzeContext.java 11KB
Dictionary.java 10KB
DictSegment.java 9KB
LetterSegmenter.java 9KB
Action.java 7KB
CN_QuantifierSegmenter.java 7KB
Lexeme.java 6KB
LexemePath.java 6KB
QuickSortSet.java 6KB
LuceneIndexAndSearchDemo.java 5KB
IKSegmenter.java 5KB
SWMCQueryBuilder.java 5KB
IKArbitrator.java 5KB
DefaultConfig.java 4KB
CJKSegmenter.java 4KB
IKTokenizer.java 4KB
Indexer.java 3KB
CharacterUtil.java 3KB
IKAnalzyerDemo.java 3KB
Hit.java 3KB
IKAnalyzer.java 2KB
Configuration.java 2KB
NettyHttpServletResponse.java 2KB
Searcher.java 2KB
ISegmenter.java 1KB
HttpServerInboundHandler.java 836B
AppTest.java 652B
LICENSE 11KB
events.out.tfevents.1481183189.localhost 10KB
pattern_recognition.lua 4KB
makefile 718B
README.md 2KB
readme.md 672B
train.output 288B
test.output 44B
zh_wiki.py 140KB
seq2seq_example.py 17KB
encoder_decoder_seq2seq.py 17KB
seq2seq_model.py 14KB
translate.py 12KB
data_utils.py 12KB
demo.py 10KB
my_seq2seq_v2.py 9KB
langconv.py 8KB
lstm_train.py 8KB
subtitle_spider.py 8KB
lstm.py 8KB
my_seq2seq.py 7KB
one_lstm_sequence_generate.py 7KB
sample_data.py 7KB
demo.py 6KB
seq2seq_patch.py 6KB
lstm.py 4KB
hello_sequence.py 4KB
settings.py 3KB
07_lstm.py 3KB
settings.py 3KB
my_lstm_test.py 3KB
digital_recognition_cnn.py 3KB
filter.py 3KB
classify.py 3KB
2.py 2KB
word_vectors_loader.py 2KB
test2.py 2KB
my_tflearn_demo.py 2KB
word_token.py 2KB
extract_sentence_ssa.py 1KB
extract_sentence_ass.py 1KB
3.py 1KB
word_vectors_loader.py 1KB
baidu_search.py 1KB
test.py 1KB
extract_sentence_srt.py 1KB
digital_recognition.py 1KB
get_charset_and_conv.py 819B
word_segment.py 786B
gensim_word2vec.py 536B
get_charset.py 522B
共 153 条
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