ne=vocsize,
de=s['emb_dimension'],
cs=s['win'])
# train with early stopping on validation set
best_f1 = -numpy.inf
s['clr'] = s['lr']
for e in xrange(s['nepochs']):
# shuffle
shuffle([train_lex, train_ne, train_y], s['seed'])
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s['win'])
words = map(lambda x: numpy.asarray(x).astype('int32'), \
minibatch(cwords, s['bs']))
labels = train_y[i]
for word_batch, label_last_word in zip(words, labels):
rnn.train(word_batch, label_last_word, s['clr'])
rnn.normalize()
if s['verbose']:
print '[learning] epoch %i >> %2.2f%%' % (
e, (i + 1) * 100. / nsentences
), 'completed in %.2f (sec) <<\r' % (time.time() - tic),
sys.stdout.flush()
# evaluation // back into the real world : idx -> words
predictions_test = [map(lambda x: idx2label[x], \
rnn.classify(numpy.asarray(contextwin(x, s['win'])).astype('int32'))) \
ne = vocsize,
de = s['emb_dimension'],
cs = s['win'] )
# train with early stopping on validation set
best_f1 = -numpy.inf
s['clr'] = s['lr']
for e in xrange(s['nepochs']):
# shuffle
shuffle([train_lex, train_ne, train_y], s['seed'])
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s['win'])
words = map(lambda x: numpy.asarray(x).astype('int32'),\
minibatch(cwords, s['bs']))
labels = train_y[i]
for word_batch , label_last_word in zip(words, labels):
print "word_batch: ", word_batch
print "label_last_word: ", label_last_word
rnn.train(word_batch, label_last_word, s['clr'])
rnn.normalize()
if s['verbose']:
print '[learning] epoch %i >> %2.2f%%'%(e,(i+1)*100./nsentences),'completed in %.2f (sec) <<\r'%(time.time()-tic),
sys.stdout.flush()
# evaluation // back into the real world : idx -> words
predictions_test = [ map(lambda x: idx2label[x], \
rnn.classify(numpy.asarray(contextwin(x, s['win'])).astype('int32')))\
for x in test_lex ]
groundtruth_test = [ map(lambda x: idx2label[x], y) for y in test_y ]
# train with early stopping on validation set
best_f1 = -numpy.inf
s['clr'] = s['lr']
for e in xrange(s['nepochs']):
# shuffle
shuffle([train_lex,train_y,train_cue], s['seed'])
print '[learning] epoch %d' % e
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
# take the context win of both
# merge the results
cwords = contextwin(train_lex[i], s['win'])
words = map(lambda x: numpy.asarray(x).astype('int32'),\
minibatch(cwords, s['bs']))
if args.c:
ccues = contextwin(train_cue[i],s['win'])
cues_bs = map(lambda x: numpy.asarray(x).astype('int32'),\
minibatch(ccues, s['bs']))
labels = train_y[i]
if not args.c:
for word_batch , label_last_word in zip(words, labels):
rnn.train(word_batch, label_last_word, s['clr'])
rnn.normalize()
else:
for word_batch , cues_batch, label_last_word in zip(words, cues_bs,labels):
rnn.train(word_batch, cues_batch,label_last_word, s['clr'])
rnn.normalize()
rnn.normalize_cue()
if s['verbose']:
# instanciate the model
numpy.random.seed(s["seed"])
random.seed(s["seed"])
rnn = model(nh=s["nhidden"], nc=nclasses, ne=vocsize, de=s["emb_dimension"], cs=s["win"])
# train with early stopping on validation set
best_f1 = -numpy.inf
s["clr"] = s["lr"]
for e in xrange(s["nepochs"]):
# shuffle
shuffle([train_lex, train_ne, train_y], s["seed"])
s["ce"] = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s["win"])
words = map(lambda x: numpy.asarray(x).astype("int32"), minibatch(cwords, s["bs"]))
labels = train_y[i]
for word_batch, label_last_word in zip(words, labels):
rnn.train(word_batch, label_last_word, s["clr"])
rnn.normalize()
if s["verbose"]:
print "[learning] epoch %i >> %2.2f%%" % (
e,
(i + 1) * 100.0 / nsentences,
), "completed in %.2f (sec) <<\r" % (time.time() - tic),
sys.stdout.flush()
# evaluation // back into the real world : idx -> words
predictions_test = [
cs = s.win,
memory_size = s.memory_size,
n_memory_slots = s.n_memory_slots )
# train with early stopping on validation set
best_f1 = -numpy.inf
s.clr = s.lr
for e in xrange(s.n_epochs):
# shuffle
shuffle([train_lex, train_ne, train_y], s.seed)
s.ce = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s.win)
words = map(lambda x: numpy.asarray(x).astype('int32'),\
minibatch(cwords, s.bs))
labels = train_y[i]
for word_batch , label_last_word in zip(words, labels):
rnn.train(word_batch, label_last_word, s.clr)
rnn.normalize()
if s.verbose:
print '[learning] epoch %i >> %2.2f%%'%(e,(i+1)*100./nsentences),'completed in %.2f (sec) <<\r'%(time.time()-tic),
sys.stdout.flush()
# evaluation // back into the real world : idx -> words
predictions_test = [ map(lambda x: idx2label[x], \
rnn.classify(numpy.asarray(contextwin(x, s.win)).astype('int32')))\
for x in test_lex ]
groundtruth_test = [ map(lambda x: idx2label[x], y) for y in test_y ]
words_test = [ map(lambda x: idx2word[x], w) for w in test_lex]
nc = nclasses,
ne = vocsize,
de = s['emb_dimension'],
cs = s['win'] )
# train with early stopping on validation set
best_f1 = -numpy.inf
s['clr'] = s['lr']
for e in xrange(s['nepochs']):
# shuffle
shuffle([train_lex, train_ne, train_y], s['seed'])
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s['win'])
words = map(lambda x: numpy.asarray(x).astype('int32'), minibatch(cwords, s['bs']))
labels = train_y[i]
for word_batch, label_last_word in zip(words, labels):
rnn.train(word_batch, label_last_word, s['clr'])
rnn.normalize()
if s['verbose']:
print('[learning] epoch %i >> %2.2f%%'%(e,(i+1)*100./nsentences),'completed in %.2f (sec) <<\r'%(time.time()-tic), end="")
sys.stdout.flush()
# evaluation // back into the real world : idx -> words
predictions_test = [ map(lambda x: idx2label[x], \
rnn.classify(numpy.asarray(contextwin(x, s['win'])).astype('int32')))\
for x in test_lex ]
groundtruth_test = [ map(lambda x: idx2label[x], y) for y in test_y ]
words_test = [ map(lambda x: idx2word[x], w) for w in test_lex]
cs=s.win,
memory_size=s.memory_size,
n_memory_slots=s.n_memory_slots)
# train with early stopping on validation set
best_f1 = -numpy.inf
s.clr = s.lr
for e in xrange(s.n_epochs):
# shuffle
shuffle([train_lex, train_ne, train_y], s.seed)
s.ce = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s.win)
words = map(lambda x: numpy.asarray(x).astype('int32'),\
minibatch(cwords, s.bs))
labels = train_y[i]
for word_batch, label_last_word in zip(words, labels):
rnn.train(word_batch, label_last_word, s.clr)
rnn.normalize()
if s.verbose:
print '[learning] epoch %i >> %2.2f%%' % (
e, (i + 1) * 100. / nsentences
), 'completed in %.2f (sec) <<\r' % (time.time() - tic),
sys.stdout.flush()
# evaluation // back into the real world : idx -> words
predictions_test = [ map(lambda x: idx2label[x], \
rnn.classify(numpy.asarray(contextwin(x, s.win)).astype('int32')))\
for x in test_lex ]
groundtruth_test = [map(lambda x: idx2label[x], y) for y in test_y]
def callRNN():
s = {'reload':False,
'model':'the path of the model',
'isemb':True,
'lr':0.0627142536696559,
'verbose':1,
'decay':True, # decay on the learning rate if improvement stops
'win':5, # number of words in the context window
'bs':9, # number of backprop through time steps
'nhidden':100, # number of hidden units
'seed':345,
'emb_dimension':100, # dimension of word embedding
'nepochs':20}
#获取当前文件名
folder = os.path.basename(__file__).split('.')[0]
if not os.path.exists(folder): os.mkdir(folder)
# load the dataset 训练集、开发集、测试集、词典
train_set, valid_set, test_set, dic = pp.preProcess(segfile, labelfile, embfile)
#train_set, valid_set, test_set, dic = load.atisfold(s['fold'])
# 字典中存在labels字典和词典 词-》编号 编号-》词
idx2label = dict((k,v) for v,k in dic['labels2idx'].iteritems())
idx2word = dict((k,v) for v,k in dic['words2idx'].iteritems())
#对同一个文件进行处理，处理完成后进行切分，现在没做的
#数据集中包括编号、每行个数、编号， 训练集4:1切分为训练和开发
train_lex, train_ne, train_y = train_set
valid_lex, valid_ne, valid_y = valid_set
test_lex, test_ne, test_y = test_set
#vocsize = len(set(reduce(\
# lambda x, y: list(x)+list(y),\
# train_lex+valid_lex+test_lex)))
#分类个数，一共多少种类，这个可以直接赋值的
nclasses = len(idx2word)
#句子数，训练语料的训练句子，用于对句子进行遍历，把握进度
nsentences = len(train_lex)
# instanciate the model
numpy.random.seed(s['seed'])
random.seed(s['seed'])
#初始化模型参数
print 'init model'
rnn = model( nh = s['nhidden'],
nc = nclasses,
ne = 1,
isemb = s['isemb'],
de = s['emb_dimension'],
cs = s['win'] )
if s['reload']:
print 'load model'
rnn.load(s[model])
# train with early stopping on validation set
best_f1 = -numpy.inf
s['clr'] = s['lr']
print 'start train'
for e in xrange(s['nepochs']):
# shuffle
shuffle([train_lex, train_ne, train_y], s['seed'])
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s['win'])
words = map(lambda x: numpy.asarray(x).astype('int32'),\
minibatch(cwords, s['bs']))
labels = train_y[i]
for word_batch , label_last_word in zip(words, labels):
rnn.train(word_batch, label_last_word, s['clr']) #开始训练
rnn.normalize()
if s['verbose']:
print '[learning] epoch %i >> %2.2f%%'%(e,(i+1)*100./nsentences),'completed in %.2f (sec) <<\r'%(time.time()-tic),
sys.stdout.flush()
# evaluation // back into the real world : idx -> words
#通过开发集进行调参，主要调节学习率
#对测试集进行测试，并将结果转化为字母标签
predictions_test = [ map(lambda x: idx2label[x], \
rnn.classify(numpy.asarray(contextwin(x, s['win'])).astype('int32')))\
for x in test_lex ]
#将test_y的值使用字母标签进行代替
groundtruth_test = [ map(lambda x: idx2label[x], y) for y in test_y ]
#进test_lex使用词本身代替
words_test = [ map(lambda x: idx2word[x], w) for w in test_lex]
#对开发集结果进行测试，并将结果转化为字母标签
predictions_valid = [ map(lambda x: idx2label[x], \
rnn.classify(numpy.asarray(contextwin(x, s['win'])).astype('int32')))\
for x in valid_lex ]
#将开发集标签使用字母标签替换
groundtruth_valid = [ map(lambda x: idx2label[x], y) for y in valid_y ]
#将valid_lex使用词替换
words_valid = [ map(lambda x: idx2word[x], w) for w in valid_lex]
# evaluation // compute the accuracy using conlleval.pl
# 调用conlleval.pl，对test和valid数据集进行结果分析，并将结果进行保存
res_test = conlleval(predictions_test, groundtruth_test, words_test, folder +'/test'+str(e)+'.txt')
res_valid = conlleval(predictions_valid, groundtruth_valid, words_valid, folder + '/valid'+str(e)+'.txt')
#保存模型
if not os.path.exists('result'): os.mkdir('result')
rnn.save('result/'+folder+str(e))
#对测试集的F值进行比较
print '第',e,'次迭代的F值为：',res_test['f1'],'开发集F值为',res_valid['f1']
if res_valid['f1'] > best_f1:
best_f1 = res_valid['f1']
if s['verbose']:
print 'NEW BEST: epoch', e, 'valid F1', res_valid['f1'], 'best test F1', res_test['f1'], ' '*20
s['vf1'], s['vp'], s['vr'] = res_valid['f1'], res_valid['p'], res_valid['r']
s['tf1'], s['tp'], s['tr'] = res_test['f1'], res_test['p'], res_test['r']
s['be'] = e
#开启子线程执行mv命令，其实就是改名
subprocess.call(['mv', folder + '/test'+str(e)+'.txt', folder + '/best.test'+str(e)+'.txt'])
subprocess.call(['mv', folder + '/valid'+str(e)+'.txt', folder + '/best.valid'+str(e)+'.txt'])
else:
print ''
# learning rate decay if no improvement in 10 epochs
if s['decay'] and abs(s['be']-s['ce']) >= 5:
s['clr'] *= 0.5
print '学习率修改为=',s['clr']
if s['clr'] < 1e-5: break
print 'BEST RESULT: epoch', e, 'valid F1', s['vf1'], 'best test F1', s['tf1'], 'with the model', folder
def callRNN():
s = {
'reload': False,
'model': 'the path of the model',
'isemb': True,
'lr': 0.0627142536696559,
'verbose': 1,
'decay': True, # decay on the learning rate if improvement stops
'win': 5, # number of words in the context window
'bs': 9, # number of backprop through time steps
'nhidden': 100, # number of hidden units
'seed': 345,
'emb_dimension': 100, # dimension of word embedding
'nepochs': 20
}
#获取当前文件名
folder = os.path.basename(__file__).split('.')[0]
if not os.path.exists(folder): os.mkdir(folder)
# load the dataset 训练集、开发集、测试集、词典
train_set, valid_set, test_set, dic = pp.preProcess(
segfile, labelfile, embfile)
#train_set, valid_set, test_set, dic = load.atisfold(s['fold'])
# 字典中存在labels字典和词典 词-》编号 编号-》词
idx2label = dict((k, v) for v, k in dic['labels2idx'].iteritems())
idx2word = dict((k, v) for v, k in dic['words2idx'].iteritems())
#对同一个文件进行处理，处理完成后进行切分，现在没做的
#数据集中包括编号、每行个数、编号， 训练集4:1切分为训练和开发
train_lex, train_ne, train_y = train_set
valid_lex, valid_ne, valid_y = valid_set
test_lex, test_ne, test_y = test_set
#vocsize = len(set(reduce(\
# lambda x, y: list(x)+list(y),\
# train_lex+valid_lex+test_lex)))
#分类个数，一共多少种类，这个可以直接赋值的
nclasses = len(idx2word)
#句子数，训练语料的训练句子，用于对句子进行遍历，把握进度
nsentences = len(train_lex)
# instanciate the model
numpy.random.seed(s['seed'])
random.seed(s['seed'])
#初始化模型参数
print 'init model'
rnn = model(nh=s['nhidden'],
nc=nclasses,
ne=1,
isemb=s['isemb'],
de=s['emb_dimension'],
cs=s['win'])
if s['reload']:
print 'load model'
rnn.load(s[model])
# train with early stopping on validation set
best_f1 = -numpy.inf
s['clr'] = s['lr']
print 'start train'
for e in xrange(s['nepochs']):
# shuffle
shuffle([train_lex, train_ne, train_y], s['seed'])
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s['win'])
words = map(lambda x: numpy.asarray(x).astype('int32'),\
minibatch(cwords, s['bs']))
labels = train_y[i]
for word_batch, label_last_word in zip(words, labels):
rnn.train(word_batch, label_last_word, s['clr']) #开始训练
rnn.normalize()
if s['verbose']:
print '[learning] epoch %i >> %2.2f%%' % (
e, (i + 1) * 100. / nsentences
), 'completed in %.2f (sec) <<\r' % (time.time() - tic),
sys.stdout.flush()
# evaluation // back into the real world : idx -> words
#通过开发集进行调参，主要调节学习率
#对测试集进行测试，并将结果转化为字母标签
predictions_test = [ map(lambda x: idx2label[x], \
rnn.classify(numpy.asarray(contextwin(x, s['win'])).astype('int32')))\
for x in test_lex ]
#将test_y的值使用字母标签进行代替
groundtruth_test = [map(lambda x: idx2label[x], y) for y in test_y]
#进test_lex使用词本身代替
words_test = [map(lambda x: idx2word[x], w) for w in test_lex]
#对开发集结果进行测试，并将结果转化为字母标签
predictions_valid = [ map(lambda x: idx2label[x], \
rnn.classify(numpy.asarray(contextwin(x, s['win'])).astype('int32')))\
for x in valid_lex ]
#将开发集标签使用字母标签替换
groundtruth_valid = [map(lambda x: idx2label[x], y) for y in valid_y]
#将valid_lex使用词替换
words_valid = [map(lambda x: idx2word[x], w) for w in valid_lex]
# evaluation // compute the accuracy using conlleval.pl
# 调用conlleval.pl，对test和valid数据集进行结果分析，并将结果进行保存
res_test = conlleval(predictions_test, groundtruth_test, words_test,
folder + '/test' + str(e) + '.txt')
res_valid = conlleval(predictions_valid, groundtruth_valid,
words_valid, folder + '/valid' + str(e) + '.txt')
#保存模型
if not os.path.exists('result'): os.mkdir('result')
rnn.save('result/' + folder + str(e))
#对测试集的F值进行比较
print '第', e, '次迭代的F值为：', res_test['f1'], '开发集F值为', res_valid['f1']
if res_valid['f1'] > best_f1:
best_f1 = res_valid['f1']
if s['verbose']:
print 'NEW BEST: epoch', e, 'valid F1', res_valid[
'f1'], 'best test F1', res_test['f1'], ' ' * 20
s['vf1'], s['vp'], s['vr'] = res_valid['f1'], res_valid[
'p'], res_valid['r']
s['tf1'], s['tp'], s['tr'] = res_test['f1'], res_test[
'p'], res_test['r']
s['be'] = e
#开启子线程执行mv命令，其实就是改名
subprocess.call([
'mv', folder + '/test' + str(e) + '.txt',
folder + '/best.test' + str(e) + '.txt'
])
subprocess.call([
'mv', folder + '/valid' + str(e) + '.txt',
folder + '/best.valid' + str(e) + '.txt'
])
else:
print ''
# learning rate decay if no improvement in 10 epochs
if s['decay'] and abs(s['be'] - s['ce']) >= 5:
s['clr'] *= 0.5
print '学习率修改为=', s['clr']
if s['clr'] < 1e-5: break
print 'BEST RESULT: epoch', e, 'valid F1', s['vf1'], 'best test F1', s[
'tf1'], 'with the model', folder
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-v', '--verbose', action='count', default=1,
help='Adjust level of verbosity.')
parser.add_argument('-nh', '--num-hidden', dest='num_hidden', type=int, default=100,
help='Set dimension of hidden units.')
parser.add_argument('-w', '--window', type=int, default=5,
help='Set size of context window (in words).')
parser.add_argument('-d', '--depth', type=int, default=3,
help='Set number of stacked layers')
parser.add_argument('--seed', type=int, default=345,
help='Set PRNG seed')
parser.add_argument('--emb-dim', dest='emb_dimension', type=int, default=100,
help='Set size of word embeddings')
parser.add_argument('-e', '--num-epochs', dest='num_epochs', type=int, default=50,
help='Set number of epochs to train')
args = parser.parse_args()
s = {'fold':3, # 5 folds 0,1,2,3,4
'lr':0.0627142536696559,
'verbose': args.verbose,
'decay': False, # decay on the learning rate if improvement stops
'win': args.window, # number of words in the context window
'bs':9, # number of backprop through time steps
'nhidden': args.num_hidden, # number of hidden units
'depth': args.depth, # number of layers in space
'seed': args.seed,
'emb_dimension': args.emb_dimension, # dimension of word embedding
'nepochs': args.num_epochs}
folder = os.path.basename(__file__).split('.')[0]
if not os.path.exists(folder): os.mkdir(folder)
# load the dataset
train_set, valid_set, test_set, dic = load.atisfold(s['fold'])
idx2label = dict((k,v) for v,k in dic['labels2idx'].iteritems())
idx2word = dict((k,v) for v,k in dic['words2idx'].iteritems())
train_lex, train_ne, train_y = train_set
valid_lex, valid_ne, valid_y = valid_set
test_lex, test_ne, test_y = test_set
vocsize = len(set(reduce(\
lambda x, y: list(x)+list(y),\
train_lex+valid_lex+test_lex)))
nclasses = len(set(reduce(\
lambda x, y: list(x)+list(y),\
train_y+test_y+valid_y)))
nsentences = len(train_lex)
# instantiate the model
numpy.random.seed(s['seed'])
random.seed(s['seed'])
rnn = model( nh = s['nhidden'],
nc = nclasses,
ne = vocsize,
de = s['emb_dimension'],
cs = s['win'],
depth = s['depth'] )
# train with early stopping on validation set
best_f1 = -numpy.inf
s['clr'] = s['lr']
for e in xrange(s['nepochs']):
# shuffle
shuffle([train_lex, train_ne, train_y], s['seed'])
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s['win'])
words = map(lambda x: numpy.asarray(x).astype('int32'),\
minibatch(cwords, s['bs']))
labels = train_y[i]
for word_batch, label_last_word in zip(words, labels):
print word_batch
#print label_last_word
#pdb.set_trace()
rnn.train(word_batch, label_last_word, s['clr'])
rnn.normalize()
if s['verbose']:
print '[learning] epoch %i >> %2.2f%%'%(e,(i+1)*100./nsentences),'completed in %.2f (sec) <<\r'%(time.time()-tic),
sys.stdout.flush()
# evaluation // back into the real world : idx -> words
predictions_test = [ map(lambda x: idx2label[x], \
rnn.classify(numpy.asarray(contextwin(x, s['win'])).astype('int32')))\
for x in test_lex ]
groundtruth_test = [ map(lambda x: idx2label[x], y) for y in test_y ]
words_test = [ map(lambda x: idx2word[x], w) for w in test_lex]
predictions_valid = [ map(lambda x: idx2label[x], \
rnn.classify(numpy.asarray(contextwin(x, s['win'])).astype('int32')))\
for x in valid_lex ]
groundtruth_valid = [ map(lambda x: idx2label[x], y) for y in valid_y ]
words_valid = [ map(lambda x: idx2word[x], w) for w in valid_lex]
# evaluation // compute the accuracy using conlleval.pl
res_test = conlleval(predictions_test, groundtruth_test, words_test, folder + '/current.test.txt')
res_valid = conlleval(predictions_valid, groundtruth_valid, words_valid, folder + '/current.valid.txt')
if res_valid['f1'] > best_f1:
rnn.save(folder)
best_f1 = res_valid['f1']
if s['verbose']:
print 'NEW BEST: epoch', e, 'valid F1', res_valid['f1'], 'best test F1', res_test['f1'], ' '*20
s['vf1'], s['vp'], s['vr'] = res_valid['f1'], res_valid['p'], res_valid['r']
s['tf1'], s['tp'], s['tr'] = res_test['f1'], res_test['p'], res_test['r']
s['be'] = e
subprocess.call(['mv', folder + '/current.test.txt', folder + '/best.test.txt'])
subprocess.call(['mv', folder + '/current.valid.txt', folder + '/best.valid.txt'])
else:
print ''
# learning rate decay if no improvement in 10 epochs
if s['decay'] and abs(s['be']-s['ce']) >= 10: s['clr'] *= 0.5
if s['clr'] < 1e-5: break
print 'BEST RESULT: epoch', e, 'valid F1', s['vf1'], 'best test F1', s['tf1'], 'with the model', folder
