cluster_point = 6
for hidden_num in range(64, 320, 32):
for magweight in range(6, 20, 2):
magweight = magweight / 10.
# drop_pro = float(drop)/10.
net_size = [chardim, hidden_num * 2, hidden_num, len(tags)]
#net_size = [chardim, hidden_num*2, hidden_num,len(tags)]
print "model initialization ............network size " + str(
net_size)
model = semimodel(len(tags),
em_num,
net_size,
magweight,
sam_cnum,
layerid,
hinge,
lrate=0.02,
opt="adaGrad",
embeddic={"dic_1": embedding})
print "model training preparing.........................."
model.train_ready(True, 'joint-train')
model.evaluate_ready()
print "model training ready ............................."
epoch = 1
modelstr = storedir + "/hid_" + str(hidden_num) + "_magweight_" + str(
magweight)
if os.path.exists(modelstr):
shutil.rmtree(modelstr)
os.mkdir(modelstr)
#os.mknod(modelstr+"/trainscore")
#list1 = range(100,110,10)#tune
#list2 = [0]*len(list1)#tune
#list1 = [100]*10
#list2 = range(5,55,5)
#for hidden_num, semiweight in zip(list1,list2):
list1 = [120]
list2 = [90]
for hidden_num1, hidden_num2 in zip(list1,list2):
semiweight = 0
#semiweight = semiweight/10.
#net_size = [dim, hidden_num1*2, len(tags)]
net_size = [dim, hidden_num1*2,hidden_num2*2, len(tags)]
print "model initialization ..............network size "+str(net_size)
model = semimodel(len(tags),em_num, net_size,dropout = dropout_pro, lrate=0.1, wdecay = 0.0, opt = "adaGrad",fix_emb = True, embeddic = embeddic, premodel = loadedmodel)
print "model training preparing.........................."
model.train_ready()
model.evaluate_ready()
print "model training ready ............................."
modelstr = storedir+"/hid1_"+str(hidden_num1)+"_hid2_"+str(hidden_num2)
if os.path.exists(modelstr):
shutil.rmtree(modelstr)
os.mkdir(modelstr)
os.mknod(modelstr+"/trainscore")
os.mknod(modelstr+"/devscore")
print "start fine-tuning .........."
print "model initialization ..............model type " + str(mtype)
print "model initialization ..............network size " + str(net_size)
print "model initialization .............pre-training epoch " + str(
pretime)
#model = semimodel(len(tags),em_num, net_size,typenum = labelnum, model_type = mtype, wordnum = wordnum, LMweight = LMweight, padding_id = outpadding_id,layerid = layerid,
#dropout = dropout_pro, lrate=0.03, wdecay = 0., opt = "adaGrad",fix_emb = False, embeddic = {"dic_1": embedding})
model = semimodel(len(tags),
em_num,
net_size,
typenum=labelnum,
model_type=mtype,
wordnum=wordnum,
LMweight=LMweight,
padding_id=outpadding_id,
layerid=layerid,
dropout=dropout_pro,
lrate=0.02,
wdecay=0.,
opt="adaGrad",
fix_emb=False,
embeddic=embeddic,
premodel=premodel)
print "model training preparing.........................."
model.train_ready()
model.evaluate_ready()
model.unsup_evaluate_ready()
print "model training ready ............................."
modelstr = storedir + "/hid1_" + str(hidden_num1) + "_hid2_" + str(
hidden_num2)