add web demo
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Jeff Zhang
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29
Readme.md
29
Readme.md
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# char-rnn-chinese
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Based on https://github.com/karpathy/char-rnn. make the code work well with Chinese.
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## Chinese process
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Make the code can process both English and Chinese characters.
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This is my first touch of Lua, so the string process seems silly, but it works well.
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## opt.min_freq
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I also add an option called 'min_freq' because the vocab size in Chinese is very big, which makes the parameter num increase a lot.
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So delete some rare character may help.
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## web interface
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A web demo is added for others to test model easily, based on sub/pub of redis.
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I use redis because i can't found some good RPC or WebServer work well integrated with Torch.
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You should notice that the demo is async by ajax. To setup the demo on ubuntu:
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Install redis and start it
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```bash
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$ wget http://download.redis.io/releases/redis-3.0.3.tar.gz
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$ tar xzf redis-3.0.3.tar.gz
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$ cd redis-3.0.3
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$ make
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$ sudo make install
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$ redis-server &
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```
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Then install flask and the redis plugin for python:
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```bash
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$ sudo pip install flask
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$ sudo pip install redis
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```
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Put you model file in online_model, rename it as 'model.t7', the start the backend and fontend script:
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```bash
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$ nohup th web_backend.lua &
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$ nohup python web_server.py &
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```
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-----------------------------------------------
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Karpathy's raw Readme, please follow this to setup your experiment.
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## Karpathy's raw Readme
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please follow this to setup your experiment.
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This code implements **multi-layer Recurrent Neural Network** (RNN, LSTM, and GRU) for training/sampling from character-level language models. The model learns to predict the probability of the next character in a sequence. In other words, the input is a single text file and the model learns to generate text like it.
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91
templates/main.html
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91
templates/main.html
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<!DOCTYPE html>
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<html>
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<head>
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<title>char-rnn API</title>
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<meta charset="utf-8">
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<meta content="initial-scale=1, minimum-scale=1, width=device-width" name="viewport">
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<script src="http://cdn.bootcss.com/jquery/2.1.4/jquery.min.js"></script>
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<link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/css/bootstrap.min.css" rel="stylesheet">
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<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script>
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<style>
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body{ padding:20px; padding-top:0px;}
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#form_net_sample{max-width:650px;margin-right:auto;margin-left:auto;}
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.description{font-weight:200;font-size:13px;}
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label{margin-top:5px;}
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</style>
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<script>
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function getChar(inputdata,callback) {
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$.ajax({
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type: "POST",
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contentType: "application/json; charset=utf-8",
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url: "/api",
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data: JSON.stringify(inputdata),
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success: function (data) {
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callback(data);
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},
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dataType: "json"
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});
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}
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function getRes(sid,callback2) {
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$.ajax({
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type: "POST",
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contentType: "application/json; charset=utf-8",
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url: "/res",
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data: JSON.stringify({"sid":sid}),
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success: function (res) {
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callback2(res);
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},
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dataType: "json"
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});
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}
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$(function() {
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var interval;
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function callback(data){
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interval = setInterval(function(){
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if(data.sid == 0)
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$('#form_output').val('backend service not found.');
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else
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getRes(data.sid, callback2);
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}, 1000);
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}
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function callback2(res){
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if(res.responds != '0'){
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clearInterval(interval);
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$('#form_output').val(res.responds);
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}
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}
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$( "#form_net_sample" ).submit(function( event ) {
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event.preventDefault();
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$('#form_output').val('load...');
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var primetext = $('#form_input').val();
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if(primetext.length <= 0){primetext = '';}
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var temperature = $('#form_temperature').val();
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if(temperature <= 0 || temperature > 10){temperature = '1';}
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var seed = $('#form_seed').val();
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if(seed.length <= 0){seed = '123';}
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getChar({"primetext":primetext, "temperature":temperature, "seed":seed},callback);
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});
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});
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</script>
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</head>
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<body>
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<form method="post" id="form_net_sample" class="form-group">
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<label for="form_input">primetext<span class="description"></span></label>
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<input name="form_input" type="text" class="form-control" id="form_input" placeholder="your text" value="">
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<label for="form_temperature">temperature<span class="description">(0-1)</span></label>
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<input name="form_temperature" type="text" class="form-control" id="form_temperature" placeholder="0.7" value="0.7">
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<label for="form_seed">seed<span class="description"> (any number)</span></label>
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<input name="form_seed" type="text" class="form-control" id="form_seed" placeholder="1" value="1">
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<br/>
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<button type="submit" class="btn btn-default">submit</button>
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<br/><br/>
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<label for="form_output">result</label>
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<textarea disabled name="form_output" id="form_output" class="form-control" rows="15"></textarea>
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</form>
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</body>
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</html>
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145
web_backend.lua
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145
web_backend.lua
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require 'torch'
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require 'nngraph'
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require 'optim'
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require 'lfs'
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require 'nn'
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require 'util.OneHot'
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require 'util.misc'
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JSON = (loadfile "util/JSON.lua")()
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local redis = require 'redis'
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local client = redis.connect('127.0.0.1', 6379)
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local client2 = redis.connect('127.0.0.1', 6379)
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local channels = {'cv_channel'}
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local model_file = './onlie_model/model.t7'
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local gpuid = 0
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local seed = 123
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-- check that cunn/cutorch are installed if user wants to use the GPU
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if gpuid >= 0 then
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local ok, cunn = pcall(require, 'cunn')
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local ok2, cutorch = pcall(require, 'cutorch')
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if not ok then print('package cunn not found!') end
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if not ok2 then print('package cutorch not found!') end
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if ok and ok2 then
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print('using CUDA on GPU ' .. gpuid .. '...')
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cutorch.setDevice(gpuid + 1) -- note +1 to make it 0 indexed! sigh lua
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cutorch.manualSeed(seed)
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else
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print('Falling back on CPU mode')
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gpuid = -1 -- overwrite user setting
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end
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end
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if not lfs.attributes(model_file, 'mode') then
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print('Error: File ' .. model_file .. ' does not exist.')
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end
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checkpoint = torch.load(model_file)
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protos = checkpoint.protos
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protos.rnn:evaluate() -- put in eval mode so that dropout works properly
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-- initialize the vocabulary (and its inverted version)
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local vocab = checkpoint.vocab
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local ivocab = {}
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for c,i in pairs(vocab) do ivocab[i] = c end
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-- parse characters from a string
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function get_char(str)
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local len = #str
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local left = 0
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local arr = {0, 0xc0, 0xe0, 0xf0, 0xf8, 0xfc}
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local unordered = {}
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local start = 1
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local wordLen = 0
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while len ~= left do
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local tmp = string.byte(str, start)
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local i = #arr
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while arr[i] do
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if tmp >= arr[i] then
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break
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end
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i = i - 1
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end
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wordLen = i + wordLen
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local tmpString = string.sub(str, start, wordLen)
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start = start + i
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left = left + i
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unordered[#unordered+1] = tmpString
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end
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return unordered
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end
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-- start listen
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for msg in client:pubsub({subscribe = channels}) do
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if msg.kind == 'subscribe' then
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print('Subscribed to channel '..msg.channel)
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elseif msg.kind == 'message' then
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-- print('Received the following message from '..msg.channel.."\n "..msg.payload.."\n")
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local req = JSON:decode(msg.payload)
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local primetext = '|' .. req['text'] .. '| '
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local session_id = req['sid']
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local seed = req['seed']
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local temperature = req['temp']
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-- initialize the rnn state to all zeros
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local current_state
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local num_layers = checkpoint.opt.num_layers
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current_state = {}
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for L = 1,checkpoint.opt.num_layers do
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-- c and h for all layers
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local h_init = torch.zeros(1, checkpoint.opt.rnn_size)
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if gpuid >= 0 then h_init = h_init:cuda() end
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table.insert(current_state, h_init:clone())
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table.insert(current_state, h_init:clone())
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end
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state_size = #current_state
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-- use input to init state
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torch.manualSeed(seed)
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for i,c in ipairs(get_char(primetext)) do
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prev_char = vocab[c]
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if prev_char then
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prev_char = torch.Tensor{vocab[c]}
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io.write(ivocab[prev_char[1]])
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if gpuid >= 0 then prev_char = prev_char:cuda() end
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local lst = protos.rnn:forward{prev_char, unpack(current_state)}
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-- lst is a list of [state1,state2,..stateN,output]. We want everything but last piece
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current_state = {}
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for i=1,state_size do table.insert(current_state, lst[i]) end
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prediction = lst[#lst] -- last element holds the log probabilities
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end
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end
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-- start sampling/argmaxing
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result = ''
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not_end = true
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for i=1,1000 do
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-- log probabilities from the previous timestep
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-- make sure the output char is not UNKNOW
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real_char = 'UNKNOW'
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while(real_char == 'UNKNOW') do
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torch.manualSeed(seed+1)
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prediction:div(temperature) -- scale by temperature
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local probs = torch.exp(prediction):squeeze()
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probs:div(torch.sum(probs)) -- renormalize so probs sum to one
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prev_char = torch.multinomial(probs:float(), 1):resize(1):float()
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real_char = ivocab[prev_char[1]]
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end
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-- forward the rnn for next character
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local lst = protos.rnn:forward{prev_char, unpack(current_state)}
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current_state = {}
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for i=1,state_size do table.insert(current_state, lst[i]) end
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prediction = lst[#lst] -- last element holds the log probabilities
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result = result .. ivocab[prev_char[1]]
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if string.find(result, '\n\n\n\n\n') then
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not_end = false
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break
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end
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end
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if not_end then result = result .. '……' end
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-- client2:set(session_id, result)
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client2:setex(session_id, 100, result)
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end
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end
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51
web_server.py
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51
web_server.py
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#!/usr/bin/python
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#encoding=utf-8
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import sys
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reload(sys)
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sys.setdefaultencoding('utf8')
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from flask import Flask
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from flask import jsonify,render_template,request,abort
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import redis
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import time
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import json
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import hashlib
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app = Flask(__name__)
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channel_name = 'cv_channel'
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@app.route('/')
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def index():
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return render_template('main.html')
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@app.route('/api', methods=['POST'])
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def api():
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if not request.json or not 'primetext' in request.json:
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abort(400)
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req = {}
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req['text'] = request.json['primetext']
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req['temp'] = request.json['temperature']
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req['seed'] = request.json['seed']
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m = hashlib.md5()
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m.update(str(time.time()))
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req['sid'] = m.hexdigest()
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r = redis.StrictRedis(host='localhost', port=6379, db=0)
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res = r.publish(channel_name, json.dumps(req))
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print res
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if res == 0:
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req['sid'] = 0
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return jsonify({'sid': req['sid']}), 200
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@app.route('/res', methods=['POST'])
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def res():
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r = redis.StrictRedis(host='localhost', port=6379, db=0)
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sid = request.json['sid']
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responds = r.get(sid)
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if responds is None:
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responds = '0'
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return jsonify({'responds': responds}), 200
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if __name__ == "__main__":
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app.run(host='0.0.0.0', port=8080, debug=True)
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