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 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 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("vector") mdr.ReadLessData(1000) mdr.NormalizeX() mdr.NormalizeY(NetType.MultipleClassifier, base=0) mdr.GenerateValidationSet(k=10) return mdr
def LoadData(): print("reading data...") dr = MnistImageDataReader(mode="vector") dr.ReadData() dr.NormalizeX() dr.NormalizeY(NetType.MultipleClassifier) dr.GenerateValidationSet(k=20) print(dr.num_validation, dr.num_example, dr.num_test, dr.num_train) return dr
def LoadData(): #读取时最好上锁 lock.acquire() print("reading MNIST data...") dr = MnistImageDataReader(mode="vector") dr.ReadData() dr.NormalizeX() dr.NormalizeY(NetType.MultipleClassifier) dr.GenerateValidationSet(k=20) print(dr.num_validation, dr.num_example, dr.num_test, dr.num_train) #释放锁 lock.release() return dr
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
def load_data(): dataReader = MnistImageDataReader(mode="vector") dataReader.ReadData() dataReader.NormalizeX() dataReader.NormalizeY(NetType.MultipleClassifier) dataReader.GenerateValidationSet(k=20) 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.reshape(x_train.shape[0], 28 * 28) x_test = x_test.reshape(x_test.shape[0], 28 * 28) x_val = x_val.reshape(x_val.shape[0], 28 * 28) return x_train, y_train, x_test, y_test, x_val, y_val
def LoadData(): mdr = MnistImageDataReader("image") mdr.ReadLessData(1000) mdr.NormalizeX() return mdr