使用 Adaline 网络实现或门
原文:https://www . geeksforgeeks . org/impering-or-gate-use-Adaline-network/
Adline 代表自适应线性神经元。它利用线性激活函数，并使用增量规则进行训练，以最小化实际输出和期望目标输出之间的均方误差。重量和偏差可调。这里，我们执行 10 个时期的训练，并计算每种情况下的总平均误差，总平均误差在某些时期后下降，随后变得几乎恒定。
或门真值表
| **x** | **y** | **x 或 y** | | --- | --- | --- | | -1 | -1 | -1 | | -1 | one | one | | one | -1 | one | | one | one | one |下面是实现。
Python 3
# import the module numpy
import numpy as np
# the features for the or model , here we have
# taken the possible values for combination of
# two inputs
features = np.array(
[
[-1, -1],
[-1, 1],
[1, -1],
[1, 1]
])
# labels for the or model, here the output for
# the features is taken as an array
labels = np.array([-1, 1, 1, 1])
# to print the features and the labels for
# which the model has to be trained
print(features, labels)
# initialise weights, bias , learning rate, epoch
weight = [0.5, 0.5]
bias = 0.1
learning_rate = 0.2
epoch = 10
for i in range(epoch):
# epoch is the number of the the model is trained
# with the same data
print("epoch :", i+1)
# variable to check if there is no change in previous
# weight and present calculated weight
# initial error is kept as 0
sum_squared_error = 0.0
# for each of the possible input given in the features
for j in range(features.shape[0]):
# actual output to be obtained
actual = labels[j]
# the value of two features as given in the features
# array
x1 = features[j][0]
x2 = features[j][1]
# net unit value computation performed to obtain the
# sum of features multiplied with their weights
unit = (x1 * weight[0]) + (x2 * weight[1]) + bias
# error is computed so as to update the weights
error = actual - unit
# print statement to print the actual value , predicted
# value and the error
print("error =", error)
# summation of squared error is calculated
sum_squared_error += error * error
# updation of weights, summing up of product of learning rate ,
# sum of squared error and feature value
weight[0] += learning_rate * error * x1
weight[1] += learning_rate * error * x2
# updation of bias, summing up of product of learning rate and
# sum of squared error
bias += learning_rate * error
print("sum of squared error = ", sum_squared_error/4, "\n\n")
输出:
[[-1 -1]
[-1 1]
[ 1 -1]
[ 1 1]] [-1 1 1 1]
epoch : 1
error = -0.09999999999999998
error = 0.9199999999999999
error = 1.1039999999999999
error = -0.5247999999999999
sum of squared error = 0.5876577599999998
epoch : 2
error = -0.54976
error = 0.803712
error = 0.8172543999999999
error = -0.64406528
sum of squared error = 0.5077284689412096
epoch : 3
error = -0.6729103360000002
error = 0.7483308032
error = 0.7399630438400001
error = -0.6898669486079996
sum of squared error = 0.5090672560860652
epoch : 4
error = -0.7047962935296
error = 0.72625757847552
error = 0.7201693816586239
error = -0.7061914301759491
sum of squared error = 0.5103845399996764
epoch : 5
error = -0.7124421954738586
error = 0.7182636328518943
error = 0.7154472043637898
error = -0.7117071786082882
sum of squared error = 0.5104670846209363
epoch : 6
error = -0.714060481354338
error = 0.715548426006041
error = 0.7144420989392495
error = -0.7134930727032405
sum of squared error = 0.5103479496309858
epoch : 7
error = -0.7143209120714415
error = 0.7146705871452027
error = 0.7142737539596766
error = -0.7140502797165604
sum of squared error = 0.5102658027779979
epoch : 8
error = -0.7143272889928647
error = 0.7143984993919014
error = 0.7142647152041359
error = -0.7142182126044045
sum of squared error = 0.510227607583693
epoch : 9
error = -0.7143072010372341
error = 0.7143174255259156
error = 0.7142744539151652
error = -0.7142671011374249
sum of squared error = 0.5102124122866718
epoch : 10
error = -0.7142946765305948
error = 0.7142942165270032
error = 0.7142809804050706
error = -0.7142808151475037
sum of squared error = 0.5102068786350209
版权属于：月萌API www.moonapi.com，转载请注明出处
