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