def extract_data(BATCH_SIZE,onehot = False):
global content_index
content_index = content_index + BATCH_SIZE
if content_index>=70000:#202599
content_index = 0
lbl = _label[content_index:content_index+BATCH_SIZE]
if onehot:
lbl = ConvNet.onehot(10,lbl)
return _data[content_index:content_index+BATCH_SIZE],lbl
###################
# loadedimage = extract_data()
# ConvNet.saveImagesMono(loadedimage, saveSize, "test0.png")
# loadedimage = extract_data()
# ConvNet.saveImagesMono(loadedimage, saveSize, "test1.png")
# exit()
# for idx in xrange(0, 1000000000):
# loadedimage = extract_data()
# global file_index
# global content_index
# print(str(file_index)+","+str(content_index))
# exit()
###################
def extract_traindata(BATCH_SIZE,onehot = False):
global train_index
train_index = train_index + BATCH_SIZE
if train_index>=60000:#202599
train_index = 0
lbl = train_label[train_index:train_index+BATCH_SIZE]
if onehot:
lbl = ConvNet.onehot(10,lbl)
return train_data[train_index:train_index+BATCH_SIZE],lbl
def extract_testdata(onehot = False):
global test_index
test_index = test_index + 1
if test_index>=10000:#202599
test_index = 0
lbl = test_label[test_index:test_index+1]
if onehot:
lbl = ConvNet.onehot(10,lbl)
return test_data[test_index:test_index+1],lbl