from DBN import DBN
from dbn_util import *
d, l = loadDataSet("train.txt", 784)
#d=d[0:100]
#l=l[0:100]
testd, testl = loadDataSet("test.txt", 784)
#testd=testd[0:10]
#testl=testl[0:10]
testD = DBN([100, 100])
testD.trainDBN(d, l, batchsize=[200, 20], numepoch=[50, 200])
testD.Print()
r = []
for i in range(0, len(testd)):
r.append(testD.predict(testd[i]))
print testl
c = map(lambda x: x + 1, map(argmax, r))
print c
dif = array(c) - array(testl)
print dif
rate = 0.0
for i in range(0, len(testd)):
if dif[i] == 0:
rate += 1
print rate / len(testd)
from DBN import DBN from dbn_util import * import pickle import sys train_file = sys.argv[1] model_file = sys.argv[2] d, l = loadDataSet(train_file, 784) d = d[0:100] l = l[0:100] #testd=testd[0:10] #testl=testl[0:10] model = DBN([20, 10]) model.trainDBN(d, l, batchsize=[20, 10], numepoch=[10, 20]) file = open(model_file, "wb") pickle.dump(model, file) model.Print() file.close()
from DBN import DBN
from dbn_util import *
d, l = loadDataSet("train.txt", 784)
# d=d[0:100]
# l=l[0:100]
testd, testl = loadDataSet("test.txt", 784)
# testd=testd[0:10]
# testl=testl[0:10]
testD = DBN([100, 100])
testD.trainDBN(d, l, batchsize=[200, 20], numepoch=[50, 200])
testD.Print()
r = []
for i in range(0, len(testd)):
r.append(testD.predict(testd[i]))
print testl
c = map(lambda x: x + 1, map(argmax, r))
print c
dif = array(c) - array(testl)
print dif
rate = 0.0
for i in range(0, len(testd)):
if dif[i] == 0:
rate += 1
print rate / len(testd)
from DBN import DBN from dbn_util import * import pickle import sys train_file = sys.argv[1] model_file = sys.argv[2] d,l=loadDataSet(train_file,784) d=d[0:100] l=l[0:100] #testd=testd[0:10] #testl=testl[0:10] model = DBN([20,10]) model.trainDBN(d,l,batchsize=[20,10],numepoch=[10,20]) file = open(model_file,"wb") pickle.dump(model,file) model.Print() file.close()
