def train(reader, title):
draw_source_data(reader, title)
plt.show()
# net train
num_input = 2
num_output = 1
params = HyperParameters(num_input, num_output, eta=0.5, max_epoch=10000, batch_size=1, eps=2e-3, net_type=NetType.BinaryClassifier)
net = NeuralNet(params)
net.train(reader, checkpoint=1)
# test
print(Test(net, reader))
# visualize
draw_source_data(reader, title)
draw_split_line(net, reader, title)
plt.show()
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())
# 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)
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE file in the project root for full license information.
# warning: 运行本程序将会得到失败的结果，这是by design的，是为了讲解课程内容，后面的程序中会有补救的方法
import numpy as np
from HelperClass.NeuralNet import *
if __name__ == '__main__':
# data
reader = SimpleDataReader()
reader.ReadData()
# net
params = HyperParameters(eta=0.1, max_epoch=10, batch_size=1, eps = 1e-5)
net = NeuralNet(params, 2, 1)
net.train(reader, checkpoint=0.1)
# inference
x1 = 15
x2 = 93
x = np.array([x1,x2]).reshape(1,2)
print(net.inference(x))
from HelperClass.NeuralNet import *
from HelperClass.DataReader import *
file_name = os.getcwd() + '/iris.csv'
if __name__ == '__main__':
reader = DataReader(file_name)
reader.ReadData()
# print(reader.XTrainRaw)
# print(reader.YTrainRaw)
reader.NormalizeY(NetType.MultipleClassifier, base=1)
reader.NormalizeX()
reader.GenerateValidationSet()
# print(reader.XTrain)
# print(reader.YTrain)
n_input = reader.num_feature
n_hidden = 4 #四个输入类型（四个隐藏神经元）
n_output = 3 #三输出
eta, batch_size, max_epoch = 0.1, 5, 10000
eps = 0.01 #学习步长
params = HyperParameters(n_input, n_hidden, n_output, eta, max_epoch,
batch_size, eps, NetType.MultipleClassifier,
InitialMethod.Xavier)
net = NeuralNet(params, "Non-linear Classifier of Iris")
net.train(reader, 10)
net.ShowTrainingHistory()
print("===输出===\nwb1.W = ", net.wb1.W, "\nwb1.B = ", net.wb1.B,
"\nwb2.W = ", net.wb2.W, "\nwb2.B = ", net.wb2.B)
