Python NeuralNet_2_2 Examples

Example #1

def train(n_hidden):

    n_input = dataReader.num_feature
    n_output = dataReader.num_category
    eta, batch_size, max_epoch = 0.1, 10, 10000
    eps = 0.01

    hp = HyperParameters_2_0(n_input, n_hidden, n_output, eta, max_epoch,
                             batch_size, eps, NetType.MultipleClassifier,
                             InitialMethod.Xavier)
    net = NeuralNet_2_2(hp, "Bank_2N3")
    net.train(dataReader, 100, True)
    net.ShowTrainingHistory()
    loss = net.GetLatestAverageLoss()

    fig = plt.figure(figsize=(6, 6))
    DrawThreeCategoryPoints(dataReader.XTrain[:, 0], dataReader.XTrain[:, 1],
                            dataReader.YTrain, hp.toString())
    ShowClassificationResult25D(
        net, 50, str.format("{0}, loss={1:.3f}", hp.toString(), loss))
    plt.show()

Example #2

File: Level2_BankHow.py Project: microsoft/ai-edu

                           color=colors[j],
                           marker=shapes[j])
    plt.show()


if __name__ == '__main__':
    dataReader = DataReader_2_0(train_data_name, test_data_name)
    dataReader.ReadData()
    dataReader.NormalizeY(NetType.MultipleClassifier, base=1)

    dataReader.NormalizeX()
    dataReader.Shuffle()
    dataReader.GenerateValidationSet()

    n_input = dataReader.num_feature
    n_hidden = 3
    n_output = dataReader.num_category
    eta, batch_size, max_epoch = 0.1, 10, 5000
    eps = 0.1

    hp = HyperParameters_2_0(n_input, n_hidden, n_output, eta, max_epoch,
                             batch_size, eps, NetType.MultipleClassifier,
                             InitialMethod.Xavier)
    net = NeuralNet_2_2(hp, "Bank_233_2")

    #net.LoadResult()
    net.train(dataReader, 100, True)
    net.ShowTrainingHistory()

    Show3D(net, dataReader)