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. # 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))
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()
# 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)
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)