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