# 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.
# 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))
# 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)
q = np.linspace(0, 1)
P, Q = np.meshgrid(p, q)
R = np.hstack((P.ravel().reshape(2500, 1), Q.ravel().reshape(2500, 1)))
Z = net.inference(R)
Z = Z.reshape(50, 50)
ax.plot_surface(P, Q, Z, cmap='rainbow')
plt.show()
if __name__ == '__main__':
# data
reader = SimpleDataReader()
reader.ReadData()
reader.NormalizeX()
# net
params = HyperParameters(2,
1,
eta=0.1,
max_epoch=10,
batch_size=1,
eps=1e-5)
#params = HyperParameters(2, 1, eta=0.01, max_epoch=500, 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)
print("z=", net.inference(x))
ShowResult(net, reader)
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)
