plt.scatter(x[i,0], x[i,1], marker='^', c='r', s=200)
else:
plt.scatter(x[i,0], x[i,1], marker='^', c='b', s=200)
"""
# 主程序
if __name__ == '__main__':
# data
reader = SimpleDataReader()
reader.ReadData()
draw_source_data(reader, show=True)
# net
num_input = 2
num_output = 1
params = HyperParameters(num_input,
num_output,
eta=0.1,
max_epoch=10000,
batch_size=10,
eps=1e-3,
net_type=NetType.BinaryClassifier)
net = NeuralNet(params)
net.train(reader, checkpoint=10)
# show result
draw_source_data(reader, show=False)
draw_predicate_data(net)
draw_split_line(net)
plt.show()
def ShowResult(net, dataReader, title):
# draw train data
X,Y = dataReader.XTrain, dataReader.YTrain
plt.plot(X[:,0], Y[:,0], '.', c='b')
# create and draw visualized validation data
TX1 = np.linspace(0,1,100).reshape(100,1)
TX = np.hstack((TX1, TX1[:,]**2))
TX = np.hstack((TX, TX1[:,]**3))
TX = np.hstack((TX, TX1[:,]**4))
TX = np.hstack((TX, TX1[:,]**5))
TY = net.inference(TX)
plt.plot(TX1, TY, 'x', c='r')
plt.title(title)
plt.show()
#end def
if __name__ == '__main__':
dataReader = DataReaderEx(file_name)
dataReader.ReadData()
dataReader.Add()
print(dataReader.XTrain.shape)
# net
num_input = 5
num_output = 1
params = HyperParameters(num_input, num_output, eta=0.2, max_epoch=10000, batch_size=10, eps=0.005, net_type=NetType.Fitting)
net = NeuralNet(params)
net.train(dataReader, checkpoint=10)
ShowResult(net, dataReader, params.toString())
plt.plot(X[:,0], Y[:,0], '.', c='b')
# create and draw visualized validation data
TX1 = np.linspace(0,1,100).reshape(100,1)
TX2 = np.hstack((TX1, TX1[:,]**2))
TX3 = np.hstack((TX2, TX1[:,]**3))
TX4 = np.hstack((TX3, TX1[:,]**4))
TX5 = np.hstack((TX4, TX1[:,]**5))
TX6 = np.hstack((TX5, TX1[:,]**6))
TX7 = np.hstack((TX6, TX1[:,]**7))
TX8 = np.hstack((TX7, TX1[:,]**8))
TY = net.inference(TX8)
plt.plot(TX1, TY, 'x', c='r')
plt.title(title)
plt.show()
#end def
if __name__ == '__main__':
dataReader = DataReaderEx(file_name)
dataReader.ReadData()
dataReader.Add()
print(dataReader.XTrain.shape)
# net
num_input = 8
num_output = 1
params = HyperParameters(num_input, num_output, eta=0.2, max_epoch=50000, batch_size=10, eps=1e-3, net_type=NetType.Fitting)
#params = HyperParameters(eta=0.2, max_epoch=1000000, batch_size=10, eps=1e-3, net_type=NetType.Fitting)
net = NeuralNet(params)
net.train(dataReader, checkpoint=500)
ShowResult(net, dataReader, "Polynomial")
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE file in the project root for full license information.
from HelperClass.NeuralNet import *
from HelperClass.HyperParameters import *
if __name__ == '__main__':
sdr = SimpleDataReader()
sdr.ReadData()
params = HyperParameters(1, 1, eta=0.1, max_epoch=100, batch_size=1, eps = 0.02)
net = NeuralNet(params)
net.train(sdr)
def inference(net, reader):
xt_raw = np.array([5, 1, 7, 6, 5, 6, 2, 7]).reshape(4, 2)
xt = reader.NormalizePredicateData(xt_raw)
output = net.inference(xt)
r = np.argmax(output, axis=1) + 1
print("output=", output)
print("r=", r)
# 主程序
if __name__ == '__main__':
num_category = 3
reader = SimpleDataReader()
reader.ReadData()
reader.NormalizeX()
reader.ToOneHot(num_category, base=1)
num_input = 2
params = HyperParameters(num_input,
num_category,
eta=0.1,
max_epoch=100,
batch_size=10,
eps=1e-3,
net_type=NetType.MultipleClassifier)
net = NeuralNet(params)
net.train(reader, checkpoint=1)
inference(net, reader)