def LoadData():
mdr = MnistImageDataReader("vector")
mdr.ReadLessData(1000)
mdr.NormalizeX()
mdr.NormalizeY(NetType.MultipleClassifier, base=0)
mdr.GenerateValidationSet(k=10)
return mdr
def load_data():
dataReader = MnistImageDataReader(mode="timestep")
dataReader.ReadLessData(10000)
dataReader.NormalizeX()
dataReader.NormalizeY(NetType.MultipleClassifier, base=0)
dataReader.Shuffle()
dataReader.GenerateValidationSet(k=12)
return dataReader
def GenerateDataSet(subfolder, count=10):
isExists = os.path.exists(subfolder)
if not isExists:
os.makedirs(subfolder)
mdr = MnistImageDataReader("vector")
mdr.ReadLessData(1000)
for i in range(count):
X = np.zeros_like(mdr.XTrainRaw)
Y = np.zeros_like(mdr.YTrainRaw)
list = np.random.choice(1000, 1000)
k = 0
for j in list:
X[k] = mdr.XTrainRaw[j]
Y[k] = mdr.YTrainRaw[j]
k = k + 1
# end for
np.savez(subfolder + "/" + str(i) + ".npz", data=X, label=Y)
def LoadData():
mdr = MnistImageDataReader("image")
mdr.ReadLessData(1000)
mdr.NormalizeX()
return mdr
