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()
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)