Python DataReader_2_0 примеры использования

Пример #1

def LoadData():
    dr = DataReader_2_0(train_file, test_file)
    dr.ReadData()
    dr.NormalizeX()
    dr.Shuffle()
    dr.GenerateValidationSet()
    return dr

Пример #2

Файл: dnn_classification.py Проект: Knowledge-Precipitation-Tribe/Neural-network

def load_data():
    dataReader = DataReader_2_0(train_data_name, test_data_name)
    dataReader.ReadData()
    dataReader.NormalizeX()
    dataReader.Shuffle()
    dataReader.GenerateValidationSet()
    return dataReader

Пример #3

def LoadData():
    dr = DataReader_2_0(train_file, test_file)
    dr.ReadData()
    dr.NormalizeX()
    dr.NormalizeY(NetType.MultipleClassifier, base=1)
    dr.Shuffle()
    dr.GenerateValidationSet()
    return dr

Пример #4

def LoadData():
    dr = DataReader_2_0(train_file, test_file)
    dr.ReadData()
    dr.NormalizeX()
    #dr.NormalizeY(YNormalizationMethod.BinaryClassifier)
    dr.Shuffle()
    dr.GenerateValidationSet()
    return dr

Пример #5

Файл: EchoRandomNumber-keras.py Проект: Knowledge-Precipitation-Tribe/Recurrent-neural-network

def load_data():
    dataReader = DataReader_2_0(train_file, test_file)
    dataReader.ReadData()
    dataReader.GenerateValidationSet(k=10)
    x_train, y_train = dataReader.XTrain, dataReader.YTrain
    x_test, y_test = dataReader.XTest, dataReader.YTest
    x_val, y_val = dataReader.XDev, dataReader.YDev
    return x_train, y_train, x_test, y_test, x_val, y_val

Пример #6

def LoadImageData():
    print("reading data...")
    dr = DataReader_2_0(train_data_name, test_data_name)
    dr.ReadData()
    dr.NormalizeX()
    dr.NormalizeY(NetType.MultipleClassifier, base=0)
    dr.Shuffle()
    dr.GenerateValidationSet(k=10)
    return dr

Пример #7

def load_data():
    train_data_name = "../data/ch14.Income.train.npz"
    test_data_name = "../data/ch14.Income.test.npz"

    dataReader = DataReader_2_0(train_data_name, test_data_name)
    dataReader.ReadData()
    dataReader.NormalizeX()
    dataReader.Shuffle()
    dataReader.GenerateValidationSet()

    x_train, y_train = dataReader.XTrain, dataReader.YTrain
    x_test, y_test = dataReader.XTest, dataReader.YTest
    x_val, y_val = dataReader.XDev, dataReader.YDev

    return x_train, y_train, x_test, y_test, x_val, y_val

Пример #8

Файл: dnn_multiClassification-keras.py Проект: Knowledge-Precipitation-Tribe/Neural-network

def load_data():
    train_file = "../data/ch11.train.npz"
    test_file = "../data/ch11.test.npz"

    dataReader = DataReader_2_0(train_file, test_file)
    dataReader.ReadData()
    dataReader.NormalizeX()
    dataReader.NormalizeY(NetType.MultipleClassifier, base=1)
    dataReader.Shuffle()
    dataReader.GenerateValidationSet()

    x_train, y_train = dataReader.XTrain, dataReader.YTrain
    x_test, y_test = dataReader.XTest, dataReader.YTest
    x_val, y_val = dataReader.XDev, dataReader.YDev

    return x_train, y_train, x_test, y_test, x_val, y_val

Пример #9

def load_data():
    dr = DataReader_2_0(train_file, test_file)
    dr.ReadData()
    dr.Shuffle()
    dr.GenerateValidationSet(k=0)
    return dr

Пример #10

def ShowResult2D(net, dr):
    ShowDataHelper(dr.XTrain[:, 0], dr.XTrain[:, 1], dr.YTrain[:, 0],
                   "Classifier Result", "x1", "x2", False, False)
    count = 50
    X, Y = Prepare3DData(net, count)
    Z = net.output.reshape(count, count)
    plt.contourf(X, Y, Z, cmap=plt.cm.Spectral, zorder=1)
    plt.show()


#end def

if __name__ == '__main__':
    dataReader = DataReader_2_0(train_data_name, test_data_name)
    dataReader.ReadData()
    dataReader.NormalizeX()
    dataReader.Shuffle()
    dataReader.GenerateValidationSet()

    num_input = 2
    num_hidden = 2
    num_output = 1

    max_epoch = 10000
    batch_size = 5
    learning_rate = 0.1

    params = HyperParameters_4_0(learning_rate,
                                 max_epoch,