seen = 0
totloss = 0
totacc = 0
apseen = 0
totobjAP = 0
totactAP = 0
progbar = Progbar(target=len(indtrain))
for i in range(len(indtrain)):
# pdb.set_trace()
# if not os.path.isfile(os.path.join(jsonpath,str(indtrain[i])+'.json')):
# print('no file exist')
# continue
start = time.time()
# print('get DB from json files')
X_train, act_label, obj_label = readjson.getDB(ind=indtrain[i])
# y_train = readjson.to_categorical_dual(y_train,10)
endread = time.time()
if isGraph:
model.fit({'input1':X_train,'actout':act_label,'objout':obj_label},batch_size=128,\
nb_epoch=1,shuffle=False,verbose=0,callbacks=[MPLC])
else:
# print('model fit')
model.fit(X_train,act_label,batch_size=128,nb_epoch=1,show_accuracy=True,shuffle=False,\
verbose=0,callbacks=[MPLC])
endfit = time.time()
# pdb.set_trace()
totlen = len(indtrain)
for i in range(totlen):
if i%50 == 0:
print('cur ' +str(i))
if i%1000 == 0:
f = open(savefold+str(i)+'.pkl.gz','w')
tofile.append(trainall)
tofile.append(actall)
tofile.append(objall)
cPickle.dump(tofile,f)
f.close()
tofile = []
trainall = []
actall = []
objall = []
X_train, act_label, obj_label = readjson.getDB(jsonpath=jsonpath,ind=indtrain[i])
# pdb.set_trace()
X_train = normalized(X_train)
trainall.append(X_train)
# at here, all labels are same. should it be appended?
actall.append(act_label)
objall.append(obj_label)
# f = open('vals.pkl.gz','w')
f = open(savefold+str(totlen)+'.pkl.gz','w')
tofile.append(trainall)
tofile.append(actall)
tofile.append(objall)
cPickle.dump(tofile,f)
f.close()
tofile = []
