def train(dataReader, max_epoch):
n_input = dataReader.num_feature
n_hidden = 2
n_output = 1
eta, batch_size = 0.1, 5
eps = 0.01
hp = HyperParameters2(n_input, n_hidden, n_output, eta, max_epoch, batch_size, eps, NetType.BinaryClassifier, InitialMethod.Xavier)
net = NeuralNet2(hp, "Arc_221_epoch")
#net.LoadResult()
net.train(dataReader, 5, True)
#net.ShowTrainingTrace()
ShowTransformation(net, dataReader, max_epoch)
ShowResult2D(net, dataReader, max_epoch)
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 = HyperParameters2(n_input, n_hidden, n_output, eta, max_epoch, batch_size, eps, NetType.MultipleClassifier, InitialMethod.Xavier)
net = NeuralNet2(hp, "Bank_2N3")
net.train(dataReader, 100, True)
net.ShowTrainingTrace()
loss = net.GetLatestAverageLoss()
fig = plt.figure(figsize=(6,6))
ShowDataByOneHot2D(dataReader.XTrain[:,0], dataReader.XTrain[:,1], dataReader.YTrain, hp.toString())
ShowClassificationResult25D(net, 50, str.format("{0}, loss={1:.3f}", hp.toString(), loss))
plt.show()
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE file in the project root for full license information.
import numpy as np
from HelperClass2.DataReader import *
from HelperClass2.HyperParameters2 import *
from HelperClass2.NeuralNet2 import *
train_data_name = "../../Data/ch10.train.npz"
test_data_name = "../../Data/ch10.test.npz"
if __name__ == '__main__':
dataReader = DataReader(train_data_name, test_data_name)
dataReader.ReadData()
dataReader.NormalizeX()
dataReader.Shuffle()
dataReader.GenerateValidationSet()
n_input = dataReader.num_feature
n_hidden = 2
n_output = 1
eta, batch_size, max_epoch = 0.1, 5, 10000
eps = 0.08
hp = HyperParameters2(n_input, n_hidden, n_output, eta, max_epoch, batch_size, eps, NetType.BinaryClassifier, InitialMethod.Xavier)
net = NeuralNet2(hp, "Arc_221")
net.train(dataReader, 5, True)
net.ShowTrainingTrace()