def LoadData(num_output):
mdr = MnistImageDataReader("image")
mdr.ReadData()
mdr.NormalizeX()
mdr.NormalizeY(NetType.MultipleClassifier, base=0)
mdr.Shuffle()
mdr.GenerateValidationSet(k=12)
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 LoadData(mode):
mdr = MnistImageDataReader(train_x, train_y, test_x, test_y, mode)
mdr.ReadData()
mdr.NormalizeX()
mdr.NormalizeY(NetType.MultipleClassifier, base=0)
mdr.Shuffle()
mdr.GenerateValidationSet(k=12)
return mdr
def load_data():
dataReader = MnistImageDataReader(mode="timestep")
dataReader.ReadData()
dataReader.NormalizeX()
dataReader.NormalizeY(NetType.MultipleClassifier, base=0)
dataReader.Shuffle()
dataReader.GenerateValidationSet(k=12)
x_train, y_train = dataReader.XTrain, dataReader.YTrain
x_test, y_test = dataReader.XTest, dataReader.YTest
x_val, y_val = dataReader.XDev, dataReader.YDev
x_train = x_train.squeeze()
x_test = x_test.squeeze()
x_val = x_val.squeeze()
x_test_raw = dataReader.XTestRaw[0:64]
y_test_raw = dataReader.YTestRaw[0:64]
return x_train, y_train, x_test, y_test, x_val, y_val, x_test_raw, y_test_raw
