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add web demo

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Jeff Zhang 2015-07-25 12:19:58 +08:00
parent 77b52e630b
commit 329021ad69
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29
Readme.md
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# char-rnn-chinese # char-rnn-chinese
Based on https://github.com/karpathy/char-rnn. make the code work well with Chinese. Based on https://github.com/karpathy/char-rnn. make the code work well with Chinese.
## Chinese process
Make the code can process both English and Chinese characters. Make the code can process both English and Chinese characters.
This is my first touch of Lua, so the string process seems silly, but it works well. This is my first touch of Lua, so the string process seems silly, but it works well.
## opt.min_freq
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. 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.
So delete some rare character may help. So delete some rare character may help.
## web interface
A web demo is added for others to test model easily, based on sub/pub of redis.
I use redis because i can't found some good RPC or WebServer work well integrated with Torch.
You should notice that the demo is async by ajax. To setup the demo on ubuntu:
Install redis and start it
```bash
$ wget http://download.redis.io/releases/redis-3.0.3.tar.gz
$ tar xzf redis-3.0.3.tar.gz
$ cd redis-3.0.3
$ make
$ sudo make install
$ redis-server &
```
Then install flask and the redis plugin for python:
```bash
$ sudo pip install flask
$ sudo pip install redis
```
Put you model file in online_model, rename it as 'model.t7', the start the backend and fontend script:
```bash
$ nohup th web_backend.lua &
$ nohup python web_server.py &
```
----------------------------------------------- -----------------------------------------------
Karpathy's raw Readme, please follow this to setup your experiment. ## Karpathy's raw Readme
please follow this to setup your experiment.
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. 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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templates/main.html Normal file
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<!DOCTYPE html>
<html>
<head>
<title>char-rnn API</title>
<meta charset="utf-8">
<meta content="initial-scale=1, minimum-scale=1, width=device-width" name="viewport">
<script src="http://cdn.bootcss.com/jquery/2.1.4/jquery.min.js"></script>
<link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/css/bootstrap.min.css" rel="stylesheet">
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script>
<style>
body{ padding:20px; padding-top:0px;}
#form_net_sample{max-width:650px;margin-right:auto;margin-left:auto;}
.description{font-weight:200;font-size:13px;}
label{margin-top:5px;}
</style>
<script>
function getChar(inputdata,callback) {
$.ajax({
type: "POST",
contentType: "application/json; charset=utf-8",
url: "/api",
data: JSON.stringify(inputdata),
success: function (data) {
callback(data);
},
dataType: "json"
});
}
function getRes(sid,callback2) {
$.ajax({
type: "POST",
contentType: "application/json; charset=utf-8",
url: "/res",
data: JSON.stringify({"sid":sid}),
success: function (res) {
callback2(res);
},
dataType: "json"
});
}
$(function() {
var interval;
function callback(data){
interval = setInterval(function(){
if(data.sid == 0)
$('#form_output').val('backend service not found.');
else
getRes(data.sid, callback2);
}, 1000);
}
function callback2(res){
if(res.responds != '0'){
clearInterval(interval);
$('#form_output').val(res.responds);
}
}
$( "#form_net_sample" ).submit(function( event ) {
event.preventDefault();
$('#form_output').val('load...');
var primetext = $('#form_input').val();
if(primetext.length <= 0){primetext = '';}
var temperature = $('#form_temperature').val();
if(temperature <= 0 || temperature > 10){temperature = '1';}
var seed = $('#form_seed').val();
if(seed.length <= 0){seed = '123';}
getChar({"primetext":primetext, "temperature":temperature, "seed":seed},callback);
});
});
</script>
</head>
<body>
<form method="post" id="form_net_sample" class="form-group">
<label for="form_input">primetext<span class="description"></span></label>
<input name="form_input" type="text" class="form-control" id="form_input" placeholder="your text" value="">
<label for="form_temperature">temperature<span class="description">(0-1)</span></label>
<input name="form_temperature" type="text" class="form-control" id="form_temperature" placeholder="0.7" value="0.7">
<label for="form_seed">seed<span class="description"> (any number)</span></label>
<input name="form_seed" type="text" class="form-control" id="form_seed" placeholder="1" value="1">
<br/>
<button type="submit" class="btn btn-default">submit</button>
<br/><br/>
<label for="form_output">result</label>
<textarea disabled name="form_output" id="form_output" class="form-control" rows="15"></textarea>
</form>
</body>
</html>

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web_backend.lua Normal file
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require 'torch'
require 'nngraph'
require 'optim'
require 'lfs'
require 'nn'
require 'util.OneHot'
require 'util.misc'
JSON = (loadfile "util/JSON.lua")()
local redis = require 'redis'
local client = redis.connect('127.0.0.1', 6379)
local client2 = redis.connect('127.0.0.1', 6379)
local channels = {'cv_channel'}
local model_file = './onlie_model/model.t7'
local gpuid = 0
local seed = 123
-- check that cunn/cutorch are installed if user wants to use the GPU
if gpuid >= 0 then
local ok, cunn = pcall(require, 'cunn')
local ok2, cutorch = pcall(require, 'cutorch')
if not ok then print('package cunn not found!') end
if not ok2 then print('package cutorch not found!') end
if ok and ok2 then
print('using CUDA on GPU ' .. gpuid .. '...')
cutorch.setDevice(gpuid + 1) -- note +1 to make it 0 indexed! sigh lua
cutorch.manualSeed(seed)
else
print('Falling back on CPU mode')
gpuid = -1 -- overwrite user setting
end
end
if not lfs.attributes(model_file, 'mode') then
print('Error: File ' .. model_file .. ' does not exist.')
end
checkpoint = torch.load(model_file)
protos = checkpoint.protos
protos.rnn:evaluate() -- put in eval mode so that dropout works properly
-- initialize the vocabulary (and its inverted version)
local vocab = checkpoint.vocab
local ivocab = {}
for c,i in pairs(vocab) do ivocab[i] = c end
-- parse characters from a string
function get_char(str)
local len = #str
local left = 0
local arr = {0, 0xc0, 0xe0, 0xf0, 0xf8, 0xfc}
local unordered = {}
local start = 1
local wordLen = 0
while len ~= left do
local tmp = string.byte(str, start)
local i = #arr
while arr[i] do
if tmp >= arr[i] then
break
end
i = i - 1
end
wordLen = i + wordLen
local tmpString = string.sub(str, start, wordLen)
start = start + i
left = left + i
unordered[#unordered+1] = tmpString
end
return unordered
end
-- start listen
for msg in client:pubsub({subscribe = channels}) do
if msg.kind == 'subscribe' then
print('Subscribed to channel '..msg.channel)
elseif msg.kind == 'message' then
-- print('Received the following message from '..msg.channel.."\n "..msg.payload.."\n")
local req = JSON:decode(msg.payload)
local primetext = '|' .. req['text'] .. '| '
local session_id = req['sid']
local seed = req['seed']
local temperature = req['temp']
-- initialize the rnn state to all zeros
local current_state
local num_layers = checkpoint.opt.num_layers
current_state = {}
for L = 1,checkpoint.opt.num_layers do
-- c and h for all layers
local h_init = torch.zeros(1, checkpoint.opt.rnn_size)
if gpuid >= 0 then h_init = h_init:cuda() end
table.insert(current_state, h_init:clone())
table.insert(current_state, h_init:clone())
end
state_size = #current_state
-- use input to init state
torch.manualSeed(seed)
for i,c in ipairs(get_char(primetext)) do
prev_char = vocab[c]
if prev_char then
prev_char = torch.Tensor{vocab[c]}
io.write(ivocab[prev_char[1]])
if gpuid >= 0 then prev_char = prev_char:cuda() end
local lst = protos.rnn:forward{prev_char, unpack(current_state)}
-- lst is a list of [state1,state2,..stateN,output]. We want everything but last piece
current_state = {}
for i=1,state_size do table.insert(current_state, lst[i]) end
prediction = lst[#lst] -- last element holds the log probabilities
end
end
-- start sampling/argmaxing
result = ''
not_end = true
for i=1,1000 do
-- log probabilities from the previous timestep
-- make sure the output char is not UNKNOW
real_char = 'UNKNOW'
while(real_char == 'UNKNOW') do
torch.manualSeed(seed+1)
prediction:div(temperature) -- scale by temperature
local probs = torch.exp(prediction):squeeze()
probs:div(torch.sum(probs)) -- renormalize so probs sum to one
prev_char = torch.multinomial(probs:float(), 1):resize(1):float()
real_char = ivocab[prev_char[1]]
end
-- forward the rnn for next character
local lst = protos.rnn:forward{prev_char, unpack(current_state)}
current_state = {}
for i=1,state_size do table.insert(current_state, lst[i]) end
prediction = lst[#lst] -- last element holds the log probabilities
result = result .. ivocab[prev_char[1]]
if string.find(result, '\n\n\n\n\n') then
not_end = false
break
end
end
if not_end then result = result .. '……' end
-- client2:set(session_id, result)
client2:setex(session_id, 100, result)
end
end

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web_server.py Normal file
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#!/usr/bin/python
#encoding=utf-8
import sys
reload(sys)
sys.setdefaultencoding('utf8')
from flask import Flask
from flask import jsonify,render_template,request,abort
import redis
import time
import json
import hashlib
app = Flask(__name__)
channel_name = 'cv_channel'
@app.route('/')
def index():
return render_template('main.html')
@app.route('/api', methods=['POST'])
def api():
if not request.json or not 'primetext' in request.json:
abort(400)
req = {}
req['text'] = request.json['primetext']
req['temp'] = request.json['temperature']
req['seed'] = request.json['seed']
m = hashlib.md5()
m.update(str(time.time()))
req['sid'] = m.hexdigest()
r = redis.StrictRedis(host='localhost', port=6379, db=0)
res = r.publish(channel_name, json.dumps(req))
print res
if res == 0:
req['sid'] = 0
return jsonify({'sid': req['sid']}), 200
@app.route('/res', methods=['POST'])
def res():
r = redis.StrictRedis(host='localhost', port=6379, db=0)
sid = request.json['sid']
responds = r.get(sid)
if responds is None:
responds = '0'
return jsonify({'responds': responds}), 200
if __name__ == "__main__":
app.run(host='0.0.0.0', port=8080, debug=True)