#corps = list(corps)
#slm = statisLM(corps,100)
#-----------------------------------------------------------------------------------------------------------------------------------------------
#'''pick trained word vec'''
import cPickle
dirs = "C:\\Users\\Administrator.NBJXUEJUN-LI\\Desktop\\project\\Python\\NLP\\savedObject\\brownCorpus\\"
slm = cPickle.load(open(dirs + "slm.pkl", "rb"))
#-----------------------------------------------------------------------------------------------------------------------------------------------
'''
hyper parameters
'''
wordDim = 100
windowDim = 5
actfunc = 'tanh'
outlayer = baseNeuronLayer(100, 1, actfunc='sigmoid')
#hiddenlayer= baseNeuronLayer(100,100,actfunc='tanh')
cnnlayer = recCnn(wordDim, windowDim, actfunc)
learning = 0.1
l2 = 0.001
#-----------------------------------------------------------------------------------------------------------------------------------------------
'''
training
'''
pred = []
true = []
ers = 0
while 2 > 1:
for minibatch in np.random.choice(len(slm.codeCorps), 1):
corp = slm.codeCorps[minibatch]
if len(sent) > 4:
sent = slm.getFakeContext(sent)
hashcorp = []
sent = '#'.join(sent)
sent = '&' + sent + '*'
for ngram in range(2, maxNgram):
for idx in range(len(sent) - ngram + 1):
if sent[idx:idx + ngram] in nchar2code:
hashcorp.append(nchar2code[sent[idx:idx + ngram]])
fakehashCorps.append(hashcorp)
#-----------------------------------------------------------------------------------------------------------------------------------------------
from sklearn.metrics import roc_auc_score
hashVec = np.random.uniform(0, 1, size=(len(nchar2code), 100))
outlayer = baseNeuronLayer(100, 1, actfunc='sigmoid')
hiddenlayer = baseNeuronLayer(100, 100, actfunc='tanh')
learning = 0.05
l2 = 0.00001
while 2 > 1:
for senidx in range(0, len(hashCorps)):
tmpx = np.array([
np.sum(hashVec[hashCorps[senidx]], axis=0),
np.sum(hashVec[fakehashCorps2[senidx]], axis=0)
])
tmpy = np.array([[1], [0]])
tmpindx = [hashCorps[senidx], fakehashCorps2[senidx]]
if senidx == 0:
y = tmpy
hashProj = tmpx
from DeepLearning.CnnNeuron import word2vecCovLayer from NLP.statisticLanguageModel import statisLM import numpy as np #----------------------------------------------------------------------------------------------------------------------------------------------- corps = brown.sents(categories=None) corps = list(corps) slm = statisLM(corps, 50) #----------------------------------------------------------------------------------------------------------------------------------------------- window = 2 wordDim = 50 outDim = 50 outs = 1 hiddenFunc = 'tanh' outFunc = 'sigmoid' cnnlayer = word2vecCovLayer(window, wordDim, outDim, actfunc=hiddenFunc) outlayer = baseNeuronLayer(outDim, outs, actfunc=outFunc) #----------------------------------------------------------------------------------------------------------------------------------------------- '''if pickle from the save''' #import cPickle #dirs = "C:\\Users\\Administrator.NBJXUEJUN-LI\\Desktop\\project\\Python\\NLP\\savedObject\\CompCorpus\\" #slm = cPickle.load(open(dirs+"slm.pkl","rb")) #cnnlayerPara = cPickle.load(open(dirs+"cnnlayer.pkl","rb")) #outlayerPara = cPickle.load(open(dirs+"outlayer.pkl","rb")) #cnnlayer.W,cnnlayer.b = cnnlayerPara #outlayer.W,outlayer.b = outlayerPara #----------------------------------------------------------------------------------------------------------------------------------------------- ''' function ''' l2 = 0.001 learning = 0.1