项目 内容 这个作业属于哪个课程 人工智能实战2019 这个作业的要求在哪 作业要求 我在这个课程的目标是 了解人工智能相关知识，提高编程能力 这个作业在哪个具体方面帮助我实现目标 学会使用激活函数和初步掌握分类问题的解决 作业正文 （https://www.cnblogs.com/-myq123/p/10669933.html）
一 . 代码及结果
import numpy as np
import matplotlib.pyplot as plt
def Read_LOGIC_Data(logic):
if logic == 'AND':
X = np.array([0,0,1,1,0,1,0,1]).reshape(2,4)
Y = np.array([0,0,0,1]).reshape(1,4)
elif logic == 'OR':
X = np.array([0,0,1,1,0,1,0,1]).reshape(2,4)
Y = np.array([0,1,1,1]).reshape(1,4)
else:
print("error,please input again")
return X,Y
def Sigmoid(x):
s=1/(1+np.exp(-x))
return s
# 前向计算
def ForwardCalculationBatch(W, B, batch_X):
Z = np.dot(W, batch_X) + B
A = Sigmoid(Z)
return A
# 反向计算
def BackPropagationBatch(batch_X, batch_Y, A):
m = batch_X.shape[1]
dZ = A - batch_Y
# dZ列相加，即一行内的所有元素相加
dB = dZ.sum(axis=1, keepdims=True)/m
dW = np.dot(dZ, batch_X.T)/m
return dW, dB
# 更新权重参数
def UpdateWeights(W, B, dW, dB, eta):
W = W - eta * dW
B = B - eta * dB
return W, B
# 计算损失函数值
def CheckLoss(W, B, X, Y):
m = X.shape[1]
A = ForwardCalculationBatch(W,B,X)
p4 = np.multiply(1-Y ,np.log(1-A))
p5 = np.multiply(Y, np.log(A))
LOSS = np.sum(-(p4 + p5)) #binary classification
loss = LOSS / m
return loss
# 初始化权重值
def InitialWeights(num_input, num_output, method):
if method == "zero":
# zero
W = np.zeros((num_output, num_input))
elif method == "norm":
# normalize
W = np.random.normal(size=(num_output, num_input))
elif method == "xavier":
# xavier
W=np.random.uniform(
-np.sqrt(6/(num_input+num_output)),
np.sqrt(6/(num_input+num_output)),
size=(num_output,num_input))
B = np.zeros((num_output, 1))
return W,B
def train(X, Y, ForwardCalculationBatch, CheckLoss):
num_example = X.shape[1]
num_feature = X.shape[0]
num_category = Y.shape[0]
# hyper parameters
eta = 0.5
max_epoch = 10000
# W(num_category, num_feature), B(num_category, 1)
W, B = InitialWeights(num_feature, num_category, "zero")
# calculate loss to decide the stop condition
loss = 5 # initialize loss (larger than 0)
error = 2e-3 # stop condition
# if num_example=200, batch_size=10, then iteration=200/10=20
for epoch in range(max_epoch):
for i in range(num_example):
# get x and y value for one sample
x = X[:,i].reshape(num_feature,1)
y = Y[:,i].reshape(1,1)
# get z from x,y
batch_a = ForwardCalculationBatch(W, B, x)
# calculate gradient of w and b
dW, dB = BackPropagationBatch(x, y, batch_a)
# update w,b
W, B = UpdateWeights(W, B, dW, dB, eta)
# end if
# end for
# calculate loss for this batch
loss = CheckLoss(W,B,X,Y)
print(epoch,i,loss,W,B)
# end if
if loss < error:
break
# end for
return W,B
def ShowResult(W,B,X,Y,title):
w = -W[0,0]/W[0,1]
b = -B[0,0]/W[0,1]
x = np.array([0,1])
y = w * x + b
plt.plot(x,y)
for i in range(X.shape[1]):
if Y[0,i] == 0:
plt.scatter(X[0,i],X[1,i],marker="o",c='b',s=64)
else:
plt.scatter(X[0,i],X[1,i],marker="^",c='r',s=64)
plt.axis([-0.1,1.1,-0.1,1.1])
plt.title(title)
plt.show()
def Test(W,B,logic):
n1 = input("input number one:")
x1 = float(n1)
n2 = input("input number two:")
x2 = float(n2)
a = ForwardCalculationBatch(W, B, np.array([x1,x2]).reshape(2,1))
print(a)
if logic == 'AND':
y = x1 and x2
else:
y = x1 or x2
if np.abs(a-y) < 1e-2:
print("True")
else:
print("False")
if __name__ == '__main__':
# SGD, MiniBatch, FullBatch
# read data
logic=input("'OR' or 'AND':")
X,Y = Read_LOGIC_Data(logic)
W, B = train(X, Y, ForwardCalculationBatch, CheckLoss)
print("w=",W)
print("b=",B)
ShowResult(W,B,X,Y,logic)
# test
while True:
Test(W,B,logic)
逻辑或门：w=[11.74573383,11.747490636] b=-5.41268583
逻辑与门: w=[11.76694002,11.76546912] b=-17.81530488
