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()
# 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 HelperClass.NeuralNet import *
from HelperClass.HyperParameters import *
# 主程序
if __name__ == '__main__':
# data
reader = SimpleDataReader()
reader.ReadData()
# net
num_input = 2
num_output = 1
params = HyperParameters(num_input,
num_output,
eta=0.1,
max_epoch=100,
batch_size=10,
eps=1e-3,
net_type=NetType.BinaryClassifier)
net = NeuralNet(params)
net.train(reader, checkpoint=1)
# inference
x_predicate = np.array([0.58, 0.92, 0.62, 0.55, 0.39, 0.29]).reshape(3, 2)
a = net.inference(x_predicate)
print("A=", a)
# 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))
# get real weights
def DeNormalizeWeightsBias(net, dataReader):
W_real = np.zeros_like(net.W)
for i in range(W_real.shape[0]):
W_real[i, 0] = net.W[i, 0] / dataReader.X_norm[i, 1]
#end for
B_real = net.B - W_real[0, 0] * dataReader.X_norm[0, 0] - W_real[
1, 0] * dataReader.X_norm[1, 0]
return W_real, B_real
if __name__ == '__main__':
# data
reader = SimpleDataReader()
reader.ReadData()
reader.NormalizeX()
# net
params = HyperParameters(eta=0.01, max_epoch=500, batch_size=10, eps=1e-5)
net = NeuralNet(params, 2, 1)
net.train(reader, checkpoint=0.1)
# inference
W_real, B_real = DeNormalizeWeightsBias(net, reader)
print("W_real=", W_real)
print("B_real=", B_real)
x1 = 15
x2 = 93
x = np.array([x1, x2]).reshape(1, 2)
z = np.dot(x, W_real) + B_real
print("Z=", z)
def draw_predicate_data(net):
x = np.array([0.58,0.92,0.62,0.55,0.39,0.29]).reshape(3,2)
a = net.inference(x)
print("A=", a)
for i in range(3):
if a[i,0] > 0.5:
plt.scatter(x[i,0], x[i,1], marker='^', c='g', s=100)
else:
plt.scatter(x[i,0], x[i,1], marker='^', c='r', s=100)
#end if
#end for
# 主程序
if __name__ == '__main__':
# data
reader = SimpleDataReader()
reader.ReadData()
# net
params = HyperParameters(eta=0.1, max_epoch=10000, batch_size=10, eps=1e-3, net_type=NetType.BinaryClassifier)
num_input = 2
num_output = 1
net = NeuralNet(params, num_input, num_output)
net.train(reader, checkpoint=1)
# show result
draw_source_data(net, reader)
draw_predicate_data(net)
draw_split_line(net)
plt.show()
# Licensed under the MIT license. See LICENSE file in the project root for full license information.
import numpy as np
from HelperClass.NeuralNet import *
# main
if __name__ == '__main__':
# data
reader = SimpleDataReader()
reader.ReadData()
reader.NormalizeX()
reader.NormalizeY()
# net
params = HyperParameters(2,
1,
eta=0.01,
max_epoch=200,
batch_size=10,
eps=1e-5)
net = NeuralNet(params)
net.train(reader, checkpoint=0.1)
# inference
x1 = 15
x2 = 93
x = np.array([x1, x2]).reshape(1, 2)
x_new = reader.NormalizePredicateData(x)
z = net.inference(x_new)
print("z=", z)
Z_real = z * reader.Y_norm[0, 1] + reader.Y_norm[0, 0]
print("Z_real=", Z_real)
# 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(eta=0.1, max_epoch=100, batch_size=1, eps=0.02)
net = NeuralNet(params, 1, 1)
net.train(sdr)
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")
ax.scatter3D(X[:, 0], X[:, 1], Y[:, 0])
x_dr = np.linspace(0, 1, 50)
y_dr = np.linspace(0, 1, 50)
x_dr, y_dr = np.meshgrid(x_dr, y_dr)
R = np.hstack((x_dr.ravel().reshape(2500,
1), y_dr.ravel().reshape(2500, 1)))
z_dr = neural.Forward(R)
z_dr = z_dr.reshape(-50, 50)
ax.plot_surface(x_dr, y_dr, z_dr, cmap="rainbow")
plt.show()
if __name__ == '__main__':
reader = DataReader(file_name)
reader.ReadData() #读入数据
reader.NormalizeX() #归一化X
reader.NormalizeY() #归一化Y
# 具体的神经网络
hp = HyperParameters(2,
1,
eta=0.01,
max_epoch=100,
batch_size=10,
eps=1e-5)
net = NeuralNet(hp)
net.train(reader, checkpoint=0.1)
print("W = ", net.W)
print("B = ", net.B)
showResult(reader, net)
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)
from numpy import array
from HelperClass.NeuralNet import *
from HelperClass.DataReader import *
import os
file_name = os.getcwd()+'/iris.csv'
if __name__ == '__main__':
#data
reader = DataReader(file_name)
reader.ReadData()
reader.NormalizeX()#标准化特征值
reader.NormalizeY(NetType.MultipleClassifier, base=1)#将标签值转化成独热编码
reader.GenerateValidationSet()#产生验证集
n_input = reader.num_feature
n_hidden = 4#（4个隐藏神经元）
# n_hidden = 8
n_output = 3#三输出
eta, batch_size, max_epoch,eps = 0.1,5,10000,1e-3
hp = HyperParameters(n_input, n_hidden, n_output,
eta, max_epoch, batch_size, eps,
NetType.MultipleClassifier, InitialMethod.Xavier)
net = NeuralNet(hp, "非线性分类")
net.train(reader, 10)
net.ShowTrainingHistory()
print("===权重矩阵===\nwb1.W = ", net.wb1.W,"\nwb1.B = ", net.wb1.B,"\nwb2.W = ", net.wb2.W,"\nwb2.B = ", net.wb2.B)
plt.legend([p13,p23,p12], ["13","23","12"])
plt.axis([-0.1,1.1,-0.1,1.1])
DrawThreeCategoryPoints(xt[:,0], xt[:,1], yt[:], xlabel="x1", ylabel="x2", show=True, isPredicate=True)
"""
for i in range(xt.shape[0]):
plt.scatter(xt[i,0], xt[i,1], marker='^', s=200)
plt.show()
"""
# 主程序
if __name__ == '__main__':
num_category = 3
reader = SimpleDataReader()
reader.ReadData()
reader.ToOneHot(num_category, base=1)
# show raw data before normalization
ShowData(reader.XRaw, reader.YTrain)
reader.NormalizeX()
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)
xt_raw = np.array([5,1,7,6,5,6,2,7]).reshape(4,2)
xt = reader.NormalizePredicateData(xt_raw)
output = net.inference(xt)
print(output)
ShowResult(reader.XTrain, reader.YTrain, xt, output)
