AI建模师-素养手册(7)-把专家直觉纳入AI的实践篇

目录1.前言2.范例（一）：工厂机器的维修判断3.范例（二）：医院护理师排班4.护理师排班的实践代码、"__1—月I」言-在上一集(第6集)里，举了两个专家直觉的范例，一个是工厂里机器的维修判断。另一个范例是医院护理师的排班。-不过，在上一集里，您只看到ExceI画面的操作，以及所输入的数据和输出的结果。-也许您会很好奇，这Excel背后的Python程序是如何实现的昵？-那么，本集就来演示一下这幕后的程序码。其中使用了TensorFIow框架(Framework)。***本文摘自高焕堂的下列书籍******以及北京【电子世界杂志】连载专栏***ChatGPT的启示-ChatGPT的能力很惊人，但它仍是纵横江湖的野猫，而非真正贴心的〈家猫〉。-ChatGPT的表现让人惊艳，但它仍是位创新组合食材的炒饭快手，还需搭配您自己的素材，才真正创新大厨师。-在ChatGPT上想搭配您自己的食材，可行途径之一是：您自己建立中小格局的AI模型，输入您的素材，您自已训练该模型，训练出〈潜藏空间向量〉，然后将它（向量），融合进去ChatGPT的潜藏空间里。-所以，逐渐地家家户户都将需要〈AI建模师〉来建模、训练，然后融合成有咼度智慧的AI家猫。-许多人对于机器学习，常常只关注于〈训练数据＞与＜算法＞，然后就是〈输出结果〉；而没意会到：数据只是〈沙子＞，经由模型的淬炼才成为〈金子〉，再经由模型铸造才做出漂亮〈金饰〉。-其中，〈金子〉才是关键性的素材。不同素材的创新组合，并贴心依据个人的心意(Attension)而调整和修饰，才是今天ChatGPT的真谛，才是GreatPoint(GPT)。-金子在哪里，就藏在人们无法理解的桃花源里，就是潜藏空间(LatentSpace)，又称为：隐藏空间或隐空间。-金子在哪里，就藏在人们无法理解的桃花源里，就是潜藏空间(LatentSpace)，又称为：隐藏空间或隐空间。I范例：专家直觉-在一个工厂里，有一部机器天天运作中，它会处于3种状态之―，分别以RGB颜色代表之。如下图：()范例：专家直觉-每天中午12:00记录其当天状态。当其状态为顺时钟、或反时钟变化，属于正常变化。如下图：正常變化）正常變化)详细说明：一部机器会处于3种状态，分别以RGB颜色代表之。每天

中午12:00记录其当天状态。当其状态为顺时钟、或反时钟变化，属于正常变化；否则为异常变化（跳机）。如果出现〈连续异常变化〉就必须停机检修。異常變化O范例：专家直觉-否其中值得留意的是，依据工厂的机器管理准则：如果出现〈连续异常变化（跳机）〉就必须停机检修。-现在，我们就来看看过去一周（工作6天）以来，这部机器状态纪录数据，如下：I范例：专家直觉-有一位负责检视机器状态的老师傅来了，他一眼就能看出了这部机器，在过去一周（工作6天）里并没有出现〈连续异常变化（跳机）〉的现象。•所以不必须停机检修。-那么AI是否也能瞬间看出来呢？范例：Al来学习专家直觉-兹把这些数据呈现于Exce1表格里，如下图：I范例：Al来学习专家直觉-运用专家直觉，把它表达于卷积核里：给予的百例：Al来学习专家直觉-请按下〈卷积〉，就拿K0[]和K1[]卷积核来对X[]进行卷积运算，得到Y0[]:范例：Al来学习专家直觉-从上图里的Y0[]就可以看出来了：有一个值达到510，代表发现一次异常（跳机）现象，从红色状态跳到蓝色。-同理，从Y1[]可以看出来：有一个值是达到510，代表发现一次异常（跳机）现象，从蓝色跳到红色。范例：Al来学习专家直觉•接下来，请按下〈相加〉。把K0[]所提取的特征（即Y0）与K1[]所提取的特征（即Y1），合并起来。例如，把Y0[]和Y1[]的对应元素进行V两两相加〉计算，而得到z[]°•从Z[]可以看出来：在本周里总共跳机2次。I范例：Al来学习专家直觉-人类专家一眼就看得出来：本周没有发生〈连续两天跳机＞的现象。•那么，AI是否也能一眼看出来呢？-答案是：可以的。•刚才由两个卷积核：K0[]和kl[]去进行卷积运算（自动提取特征），分别看到了一次跳机现象。但是如何看出来是否V连续跳机〉呢？答案是：再进行一次特征提取（卷积）就可以看出来了。范例：专家直觉•再一次使用卷积核，如下图：I范例：Al来学习专家直觉•接下来，请按下〈卷积〉。就拿KZ[]卷积核来对Z[]进行卷积运算，得到YZ[]，就可以看出来了。范例：Al来学习专家直觉•请看看Python程序，来实践上述的情境。范例：Al来学习专家直觉kO=np.array([1,kl=npfarrayt[0,yO=conv(x,kOf3)piint("\nYD=",np,round(yO,2))yl=Conv(x,k1,3)print("\nYl=",np,round(yl,2))z=np.zeros((yO.size),dtype='int32()far1inrange(yO.Size):z[i]=y0[i]+yl[i]printC'VnZ=”，z)kz=np.array([1,1])yz=conv(z,kz,1)print("\nYZ=",yz)#............ContInued--------------------x=np.array([255,0,0,0,0.255,0,255,。,255,0,0,0,255,01)0,0.255.专家直接给卷积核KO和Kl___________丿-卷积核的W，直接从专家心中来。^1/]]1OQOD-1O1OO_______________范例：Al来学习专家直觉•卷积核的w，直接从专家心中来。#............ContInued.............................x=np.array([255,0,0,0,0,255,0,255,丄0,0,255.255,0,0,0,255,0])k0=np.array([1,0,□,0,Q,1])kl=np.array([0?0,I,1,0,0])yO=conv(x,

kOf3)print("\nY0=H,np,round(yO,2))yl=Conv(x,k1,3)print("\nYl=",np,round(yl,2))z

=npnzeros((yO,size),dtype='int32')fciriinrange(yD.SIze):z[i]=y0[i]+yl[i]printfAnZ=”，z)kz=np.array([1,1])yz=conv(z,

kz,1)print("\nYZ=\ys)#End____________________拿KO去卷积____________________范例：Al来学习专家直觉kO=np.array([1,kl=npfarrayt[0,yO=conv(x,kOf3)piint("\nYD=",np,round(yO,2))#............ContInued--------------------x=np.array([255,0,0,0,0.255,0,255,。,255,0,0,0,255,01)0,0,255,print("\nYZ=",yz)kz=np.array([1,1])yz=conv(z,kz,I)z=np.zeros((yO.size),dtype='int32')far1inrange(yO.Size):z[i]=y0[i]+yl[i]printC'VnZ=”，z)y1=Conv(x,k1,3)print("\nYlnp,round(yL2))拿Kl去卷积-卷积核的W，直接从专家心中来。]]loQoD-1o1oo_______________范例：Al来学习专家直觉kO=np.array([1,kl=np.array([0,#............ContInued--------------------..............................x=np.array([255,0,0,0,0.255,0,255,。,255,0,0,0,255,01)____________________________________yO=conv(x,k。，____________prmt(F,\nYlJ=",np,round(yU,27J_________________________________________Iyl=Conv(x,kl,3)0,0,255,primei=*,np,round(yl,ZTJprint("\nYZ=\yz)kz=np.array([1,1])yz=conv(z,kz,I)z=npnzeros((yO,size),dtype='int32')fciriinrange(yD.SIze):z[i]=y0[i]+yl[i]printC'VnZ=”，z)___yO特征表[FeatureMap)____________________________汰_丿______________yl特征表(FeatureMap)____________丿-卷积核的w，直接从专家心中来。^1/]]1OQOD-1O1OOkO=np.array([1,kl=npfarrayt[0,yl=Conv(x,k1,3)print("\nYl=",np,round(yl,2))yO=conv(x,kOf3)piint("\nYD=",np,round(yO,2))范例：Al来学习专家直觉•卷积核的w，直接从专家心中来。Continuedx=np.array([255,0,0,0,0,255,0,255,丄0,0,255,255,0,0,0,255,01)z=npnzeros((yO,size),dtype='int32,)fciriinrange(yD.SIze):z[i]=y0[i]+yl[i]printfAnZ=”，z)将两个特征表相加______________丿kz=np.array([1,1])yz=conv(z,kz,1)print("\nYZ=",yz)]]loQoD-1o1oo_______________范例：Al来学习专家直觉kO=np.array([1,kl=npfarrayt[0,kz=np.array([1,1])yz=conv(z,

izrI)______________________________________________yO=conv(x,

kOf3)print("\nY0=H,np,round(yO,2))______________________yl=Conv(x,k1,3)print("\nYl=",np,round(yl,2))z=npnzeros((yO,size),dtype='int32')fciriinrange(yD.SIze):z[i]=y0[i]+yl[i]printfAnZ=”，z)_________________________________________print("\nYZ=\yz)______再一次卷积___________丿-卷积核的W，直接从专家心中来。#............ContInued.............................x=np.array([255,0,0,0,0,255,0,255,丄0,0,255.255,0,0,0,255,01)]]loQoD-1o1ooYO=[51002550255]Y1=[025505100]Z=[510255255510255]YZ=[765510765765]»>两个特征表Y0=[51002550255]Y1=[025505100]Z=[510255255510255]YZ=[765510765765]»>丿YO=[51002550255]Y1=[025505100]Z=[510255255510255]YZ=[765510765765]»>特征表相加丿-发现了2次跳机YO=[51002550255]Z=Y1=[025505100]匝)255255(510)255]YZ=[765510765765]»>YO=[51002550255]Y1=[025505100]Z=[510255255510255]-YZ□都小于1020，没有〈连续跳机〉的现象Y0=[51002550255]Y1=[025505100]Z=[510255255510255]YZ=「7655107657651»>最后的卷积表专家只提供直觉判断范例：Al来学习专家直觉-设计一个分类器，来吸纳专家智慧ABCDEFGHIJKLMN0126RGBRGBTZ3紅2550002550綠0（沒問題）4S6綠0255000255藍0（沒問題）5藍0025502550綠0（沒問題）6Epoch500綠0255025500紅0（沒問題）7紅2550000255藍_1（跳機）8藍0025525500紅1（跳機）910111213141516Initial學習EpochInitial學習正規化机台的各种状态变化（沒問題）（沒問題）（沒問題）（沒問題）ABCDEFGHIJKLM.N0126RGBRGBTTZ3紅2550002550綠0（沒問題）4S6綠0255000255藍0（沒問題）5藍0025502550綠0（沒問題）6Epoch500綠0255025500紅0（沒問題）7紅2550000255藍_1（跳機）8藍0025525500紅1（跳機）91011Initial12范例：Al来学习专家直觉-正规化ABCDEFGHIJKLMN126RGBRGBT3紅100010綠0（沒問題）4s6綠010001藍0（沒問題）5藍001010綠0（沒問題）6Epoch500綠010100紅0（沒問題）7紅100001藍_1（跳機）8藍001100紅1（跳機）91011Initial120Z17范例：Al来学习专家直觉-展幵训练ABCDEHJKLMNO1N6TZ2（沒問題）00.03（沒問題）S600.034（沒問題）00.035（沒問題）Epoch50000.036（跳機）10.977（跳機）10.97891011Initial1213W-5.211.741.6-5.341.614正規化B1516x0xlx2藍紅藍紅迁移到卷积核1.740RGBRGB紅100010綠010001藍001010綠010100紅100001藍001100第1天第2天第3天第4天第5天第6天00002550255000255255000255W1.74-5.211.741.65341.6第1天第2天第3天第4天第5天第6天255000255第1天第2天第3天第4天第5天第6天255000255ATX[]00002550255001.6QRs第6天EFG第2天12KLM第4天NOP第5天BCD第1天HIJ第3天z255J56J_8Ar'255000--tT"二_——w1.74「5211.741.6P-5.34B0请看看Python程序，来实践上述的情境。#ex_All_04.pyimportiiiiiiipy；技】項importkurasfLik?iil：,iiiudE!1siipui'

記qu日ritialfro/iikeras.layersimportDensefromkeras.optionizersimpo11SGDfromkeras.modelsijiiportMod&ldefsigmoid(y):return1/(1+np.6xp(-y))1,dtype=np.f1Bl132)丿准备分类器的训练数据t=np.array([[0],[0],[0],[0],[1],[1]],dtypNnp,f1oat32)...........Continued......................................EditFormatRunOptionsWindowHelp05005]525025>50o505?22?o•>55o02020050055505505?5♦72o2?o55ff.f200020np请看看Python程序，来实践上述的情境。Continued建立分类器模型d.$at_w日ights(wb)i=

Dense(0,activation=1

si^moid'?

name="dM

,

input_di叶”model=Sequentia1()modelpadd(d)modelnCDinpilet1dss=keras.losses.MSE,optimizer=SGD(1r=0.15),metrics=['accuracy'])wb=[tip,array([[0.5],[-0.5],[0.5],[04],[-04],[04]],dtype=np.fLoat32),np.ariay([0.0],dtyp&=np,float32)]kkw=Nuiiekkb=Mone#...........continued...............................6£-!______-=--训练分类器wo=d.g日t_weights()[0]kkw=】項，£qiMPZ：顷WQ)print(n\n-----training-----*')printf=‘，np^roundtwo,2))bo=d.^et_weights()[1]kkb=】項，阪旺荚顷bo)print(ll\nB=11,np.round(bo,2))y=npdot(x,kkw)+kkbz

=sigMoid(y)Print("\nZ=",np.round(zt2))|#...........coniinued.............................*...........coniinued......................defone_ruiiiid(x,t):globalmodeldxx=xriptiEwaxis,：：dtt=tjnp,newaxis,二model.fit(dtt,1,1,D,stuffle=False)deftiainir^()：globalmoiel,

kkw,kkbX=DX/255forepir；r；in^&(2000):for1inrange(S)ioneround(x[i],t[i])im~i=1.gwt^urwi(J[。]kkw=】項，£qiMPZ：顷WQ)H*("，n■=、■'*俨*ig，ge----_11

Jprintf,np.rQundtwQ,2))bo=d.^et_weights()[1]kkb=】項，阪旺荚顷bo)print(ll\nB=11,np.round(bo,2))y=npdot(x,kkw)+kkbz

=sigMoid(y)Print("\nZ=",np.round(zt2))|#...........coniinued.............................#...........coniinued......................defane_ruiiiid(x,t):globalmodeldxx=xrip臨船乂is,：：dtt=tjnp,newaxis,二jiiode1hfit(dxx,d11,1,1,0,shuffle=aIse)deftiainir^()：globalmoiel,

kkw,kkbX=DX/255forepir；r；in^&(2000):for1inrange(S)ione_round(x[i],t[i])迁移到卷积核___________)_____#...........Continued.............................ddx=np.array([1,0,0,0,0,1],dtype=np.float32)defgetY(dxf

kw,kb):y=np.sum(ilx*kw)+kbreturnydefcdhv(x,kw,kb,stride)：

/xz=x.sizekz=kw.$izesteps=int((xz-kz)/stride)+1y=np.zeros((Steps),dtype=np.float32)foriinrange(steps):start=i*stridedx=x[start:start+l:z]y[i]=?etY(dx7kw,kb)returny#............continued.............................准备卷积运算函数continued...............................#...........Conv-1.............”)_____________________yl=Conv(tx,kkw,kkb,3)zlSIgmoldC^Ff/.....print(h\nprint(MZ1=",np.round(zlt

2))diefconvolution():TX=nP.array([255.0,0,0,0,255.0,255,。，0,0,255,255,0』，0?255f0],dtype=np.float32)tx=TX/255_k神2=np.array([1,1])z2=Conv(zl,kw2,0,1)...................print("\n-----conv-2--------print("Z2=",npround(z2,2))_________________________使用迁移来的卷积核Convolutioii()#End................................#_一二……training()#continuedConv-1IJT专家直接提供的卷积核丿yl=Conv(tx,kkw,kkb,3)zl=signu)id(yl)print(h\nprint(MZ1np.round(zlt

2))print("\nprint("Z2=",npround(z2,2))diefconvolution():TX=nP.array([255.0,0,0,0,255.0,255,。，0,0,255,255,0』，0?255f0],dtype=np.float32)tx=TX/255np.ar髯页-ft,z2=Conv(zl1kw2,.0,1)Convolutioii()#Endtraining()-发现了2次跳机Z=[0.050.030.030,030.970.97]Z1=(0.970.03□.03(0,97)0.03]-----Conv-222=[0.990.050.990,99]t[88515rL7Io__yo-■41114-42242_=--woB]J1L-9-Z2[]都小于1.0，没有〈连续跳机〉的现象Z=[0.050.030.050,030.970,97]Z2=[0.990.050.990,99]-----conv-1-----------*-Z1=[0.970.030.030,970.03]-d2owB]J1L-9t[88515rL7Io__yo-■41114-42242把专家直觉纳入Al模型里|说明•专家直觉(ExpertIntuition)就是您可以看出来眼前的情况与过去发生情况的某些相似点(即相似特征)。•您的专门知识愈深，就愈能看出许多相似情况，而在菜鸟眼中，每个情况都是新且独立的情况。•专家直觉带给人们瞬间洞察力，也就是鉴往知来的能力。把专家直觉纳入Al模型里|说明-例如，下图是医院里的护理师排班表:7ABCBCDBCEBCFBCGOFHOFIOFJBCKBCLBCMBCNBC0OFPBCQBCRBCsOFTOFuOFVOFwBCXBCYBCzBCAAOFABBCACBCADBCAEBC8BCBCOFBCBCBCBCOFBCBCBCOFBCBCBCBCOFOFOFJBBCBCOFOFBCBCBCBCOF9JBJBJBJBOFOFBCBCBCOFOFOFBCBCBCBCOFOFOFOFBCBCOFOFBCBCOFOFBC0R=OFOFOFBCBCOFBCBCOFJBJBJBJBOFOFOFOFBCBCBCBCBCBCBCOFBCBCBCOFOF11BE=BCOFBCBCOFBCBCBCBCOFBCBCBCOFOFOFJBBCBCBCBCOFOFBCBCOFBCBCBCOF2BCOFOFBCBCBCBCOFBCBCBCBCOFOFOFRABCBCBCBCOFOFOFBCBCOFOFBCBC3白底=BC=白班JBJBOFBCBCBCOFBCBCBCBCBCOFOFOFOFBCRABCBCOFOFOFBCBCOFOFOFBC14黃底夜BCBCBCBCBCOFOFOFBCBCBCBCOFOFOFOFBCBCJBBCOFOFBCBCBCOFOFBCBC5藍底攻人=大夜BCBCOFOFBCBCBCBCOFBCBCBCOFOFOFOFBCJBBCBCOFOFBCBCBCBCBCOFOF.6BCBCBCOFOFJBJBJBJBJBOFBCBCOFOFOFOFBCRABCOFOFOFBCBCBCOFBCBC70=100BCBCOFOFBCBCBCBCBCOFBCBCOFOFOFOFBCBCOFBCRAJBBCOFOFOFBCBCBC8E&I0IBCBCBCOFOFBCBCBCBCOFOFBCBCJBRABCOFOFOFOFBCBCBCOFOFBCBCBCBC.9|jB=110OFOFBCBCBCBCOFBCBCBCBCOFBCJBJBBCOFOFOFOFBCBCBCBCOFOFOFBCBC!0RA=111OFOFBCBCBCOFOFBCBCBCOFOFBCRARABCBCOFBCBCBCBCOFBCBCBCBCOFOF•其中：OF代表休假；BC代表白天班；JB代表小夜班；RA代表大夜班。把专家直觉纳入Al模型里I说明-有经验的护理师，一眼就能看出这不是一张好的排班内容，其凭借的就是专家直觉。-如果我们能够探知这位资深护理师所观察到的特征，并且将其表现于AI模型里，就能大大提升AI系统的质量。-例如，护理师们有一个概念称为：花式排班。AI人员就去探知〈花式排班〉的特征，表达于AI模型上。，把专家直觉纳入Al模型里|设计分类器•藉由分类器来吸纳专家的智慧。-一旦分类器训练好了，就将分类器的W迁移过来，成卷积核(Kernel)。•有了卷积核就能进行卷积运算(Convo山tion)来自动提取特征了。•这样就引入专家的经验、智慧(又称为〈专家直觉＞)，纳入到AI模型里。例如，专业术语〈花式排班〉就蕴含了专家智慧。把专家直觉纳入Al模型里设计FX（FeatureExtractor）分类器BN0PQR102護理排班Al模型03代表：OF（休息）04代表：BC（日班）05代表：JB（小夜班）06代表：RA（大夜班）07080代表GckxI09101H1121131014151161017181920211代表Bad訓練1500回合LZAT丫環-1丫環一2丫環-3CDEFGIIIJKX[]100010001000010010000010100000010100100000101000000■1100001000100010000100100000100100100000■101000010001000100000001001000000022FX分类器神经网络结构，把专家直觉纳入Al模型里|展幵训练•于是专家直觉就成为FX分类器的内涵了。-当您按下＜训练〉按钮，FX分类器就幵始学习了。•学习之后，这位资深护理的专业直觉，就成为这分类器的智慧了。并以权重来表达这项智慧，如下图：，把专家直觉纳入Al模型里|展幵训练護理排班AI模型0代表Ggl0.06學習完成!0.080.06-3.952.6850.0222.57969訓練1500回合代表：QF（休息）代表：BC（日班）代表：JB（小夜班）代表：RA（大夜班）-6.798457.25021-6.842732.7792-0.2960.030.020.020.03丫環-1丫環-2丫環-30.450.2720.4591代表Bad权2.7585-3..121.18-0.162.765-3.980.581.1-0.2960.0225.46-3.91-0.3-3.885.472.77921.2010.62-3.952.685-0.21.12-3.2、-017585-3.121.18-0.161765-3.980.581.1把专家直觉纳入Al模型里123YYYrI-6.79845-7.25021_-6.84273u2'

2579692.77921.2010.62-3.952.685-0.21.12-3.2-0.2960.0225.46-391-0.3-3.885.47-0把专家直觉纳入Al模型里17585-3.121.18-0.1617653980.581.10.450.2720.4592.57969123.丫丫丫2把专家直觉纳入Al模型里1319-6.798452.77921.2010.62-3.952.685-0.21.12-3.20.45207.250^1/丫環-2-0.2960.0225.46-3.91-0.3-3.885.47-0027221-6.^3-丫環-32.7585-3.121.18-0.161765-3.980.581.10.459222.57969*把专家直觉纳入Al模型里从分类器迁移出来,成为卷积核丫環-12.7792丫環-2-0.2960.0220.272丫環一32.7585-0.162.765-3.980.459丫环的卷积核1819-6.79845207.2502121-5.84273222.57969r-3.952.685-0.21.12-3.2.从FX分类器迁移出来，成为卷积核丫环-2的卷积核2.579692223-6.798457.25021-5.84273181920210.450.2720.459从FX分类器迁移出来，成为卷积核丫環-12.77921.2010.62-3.952.685-0.2231.1：丫环-3的卷积核0.450.2720.459-6.798457.25021-6.842732.579591819202122准备排班表（卷积的对象）汇入卷积核2.78Conv2.76庭4通道匯合匯入丫環智慧0.450.270.460ABCDEFGHIJKLMN0PQRSTUVWXYZAA1OFRARARAOF210000001000100010001000110034OFOFOFRARAOFOF510001000100000010001100010067亨JBOFOFOFJBJBOF00101000100010000010001010091011RARARAOFRARARA0001000100011000000100010003个丫环的卷积核把专家直觉纳入Al模型里训练完成,得到卷积核-为了简单起见，上图里只列出4笔排班的原始资料（4位护士的本本月份排班表）。•其中：OF代表休假；BC代表白天班；JB代表小夜班；RA代表大夜班。•请按下〈卷积〉，就展开对原始数据进行卷积运算，来提取特征（具有花式排班的表征）。0.27-6.840.950.810.9870.9970.810.810.810.990.4980.020.530.990.5270.9970.310.310.5170.00]0.4880.0250.990.960.9870.8020.960.3060.990.00]0.990.990.0250.0190.990.710.8020.9550.310.]]0.00]0.020.990.]80.9970.0250.9390.960.990.8020.020.990.]7匯入丫環智慧卷积的结果针对第1笔资料3个丫环的卷积结果2.咫].20.6-0.305.5r卷積4通道ConvH匯合0.990.950.50.020.530.32-45.5-0-4().6].]]]0.&。.&0.50000.5().300ABCDEFGH1JKLMN0PQj]]000]000]000]100012AAABACADAEA]]1213141a1516上17182237卷积的结果F0A00450.270.46B0C0E0卷積ConvD]J0通道匯合H]]_0L]针对第2笔资料3个丫环的卷积结果G0W0V0]00匯入丫環智慧Z000]00.030.S000.8000.800.03。盅00.03。盅00.03。盅00.03。盅0.310.020.9610.421-6.87.25-6.842.5S0.50.5]3.2].]0.520.490.99MN0P10002.咫].20.6-4-0.305.5392.763]1.2-0.2190.5200000.3]0.950.810.80.9幻0.9970.950.&0.S0..]0.&。.&。屈0.990.950.810.&]0.81。屈0.99]200.490.030.030.030.0300.420.020.0300.4980.420.02000.50000.03().50.020.030.030.030.03().502]{).00.R0.R0.R0.8]]0.32000.5270.9970.32000.5().30000.530.320000().530.31]]0.5200.3：]]0.52000.30.5170.00]004‘0.3]]]0.420.420.490.030.020.40.490.030.0300.4880.0250.030000.40.40.4*]]0.990.R0.96]0.990.&0E]0.9幻0.8020E0.&o.s]]]]r>、館290.9S]]0.990.9]0.5200.080.]0.00]0.3061]]]0.90.90.9]]0.990.90.90.90.9&0.990.90.90.930]0.420.42]]]0.490.030.86]0.0250.0190.420.40.4]]]]]0.4]]]1]]]]]310.99]]0.980.9]0.990.&0.710.90.8020.9551]]]0.90.90.9]]0.980.90.90.90.990.980.90.90.9323334000.3]0.52000.31]]]0.：]]0.00]0.020.9]]0.90.90.9]]0.990.90.980.5200.02o.is00.3350.030.030.020.490.0300.020.420.42]0.9970.0250]]]]]]]0.4]]]0.490.0300.570.030360.80.R0.960.990.8o.s0.96]]]0.9390.8020.020.]]]0.90.90.9]]0.980.90.990.990.80.020.170.8]38AAABACADAEA0.322]27323334353637卷积的结果针对第3笔资料3个丫环的卷积结果0450.270.46]00Z000]W000.50.51).3.]]、0.40.4]]]*3)00.03。盅00.03。盅0.810.030.8000.80.8ABCDEFGH1JKLMX0PQ000]000]000]10000121314匯入卷積通道2.咫].20.6-42.715丫環智慧Conv匯合-0.305.539-0162.763]1.2-0.22.81718zHi19().5200000.3]0.950.810.80.9幻0.9970.950.&0.S0.80.1200.490.030.030.030.0300.420.020.0300.4980.420.0200°1210.990.R0.R0.R0.8]]0.32000.5270.9970.32000J-6.87.25-6.842.580.990.950.810.&]0.R1。屈0.990.50.020.030.030.030.030.50.530.320000().530.3]10.5200000.020.420.490.030.030.0300.9610.990.&0.R0.8。盅2324]]0.5200.3：]]0.52000.30.5170.00]0000.3250.420.420.490.030.020.40.490.030.0300.4880.0250.0300026]]0.990.R0.96]0.990.&0E]0.9幻0.8020E0.&O.S]*0.98]]0.990.9]0.5200.080.]0.00]0.3061]]]0.90.90.9]]0.990.90.90.90.9&0.990.90.90.9]0.420.42]]]0.490.030.86]0.0250.0190.420.40.4]]]]]0.4]]]1]]]]]0.99]]0.980.9]0.990.&0.710.90.8020.9551]]]0.90.90.9]]0.980.90.90.90.990.980.90.90.9000.3]0.52000.31]]]0.：]]0.00]0.020.9]]0.90.90.9]]0.990.90.980.5200.02o.is00.30.030.030.020.490.0300.020.420.42]0.9970.0250]]]]]]]0.4]]]0.490.0300.570.0300.80.R0.960.990.8O.S0.96]]]0.9390.8020.020.]]]0.90.90.9]]0.980.90.990.990.80.020.170.8]38wAAABACADAEA-6.80.452.580.8000022]0.500.40.50]]0.8270.9().9]]]0.4Z90.90.9]]卷积的结果针对第4笔资料3个丫环的卷积结果7.25-6.840.270.46]00]]Z000]X00]ABCDEFGH1JKLMX0PQ000]000]000]10000121314匯入卷積通道2.咫].20.6-42.715丫環智慧Conv匯合-0.305.539-0162.763]1.2-0.22.8171819().5200000.3]0.950.810.80.9幻0.9970.950.&0.S0.8200.490.030.030.030.0300.420.020.0300.4980.420.02000210.990.R0.R0.R0.8]]0.32000.5270.9970.32000。屈0.990.950.810.&]0.81。屈0.99]0.03().50.020.030.030.030.03().5000.530.320000().530.3000.3]10.5200000.030.030.020.420.490.030.030.0300.R0.R0.9610.990.&0.R0.8。盅0.990.90.90.90.980.990.90.90.9]]]1]]]]]旃0.90.90.90.990.9&0.90.90.92324]]0.5200.3：]]0.52000.30.5170.00]0000.3i250.420.420.490.030.020.40.490.030.0300.4880.0250.0300o126]]0.990.R0.96]0.990.&0E]0.9幻0.8020E0.&O.S]J28290.98]]0.990.9]0.5200.080.]0.00]0.3061]]30]0.420.42]]]0.490.030.86]0.0250.0190.420.40.4],310.99]]0.980.9]0.990.&0.710.90.8020.9551]]]]32与，V鈿000.3]0.52000.31]]]0.：]]0.00]0.020.9]0.030.030.020.490.0300.020.420.42]0.9970.0250]]]0.80.R0.960.990.8o.s0.96]]]0.9390.8020.020.]]]0.90.90.9]]0.990.90.980.5200.02o.is00.3]]]]0.4]]]0.490.0300.570.0300.90.90.9]]0.980.90.990.990.80.020.170.8]38交由大丫环汇合特征表-得到大丫环的特征表丿这笔有两处花式排班202]2200000023242526270000003800000000000000000000000000000000000000000002829303132000000000000000000000000000033343536370000000000000000000000000交由大丫环汇合特征表-得到大丫环的特征表这笔有许多花式排班丿I于是，得到大丫环的特征表-您可以看到了，青色底的部分特征值为1，表示发现到有〈花式排班〉的表征。I进行池化(Pooling)-然后，进行CNN的池化(Pooling)运算，萃取一周内是否出现〈花式排班〉现象。把池化的特征表,交给格格-最后，建立全连接层(FCL)分类器，并进行训练。把专家直觉纳入Al模型里|训练完成-这样就完成了〈排班＞CNN深度学习的模型的设计与训练了。-目前已经完成〈排班〉模型的训练阶段了。I进行测试-现在就拿两位新护士的排班表，来给AI评估看看。看看是否有不良的花式排班现象。把专家直觉纳入Al模型里|测试结果-预测的结果呈现于粉红色底的部分：•第1位新护士的排班是正常的。-第2位新护士的排班则并不理想。把专家直觉纳入Al模型里更上一层楼-以上的范例，只是展示〈特征提取器＞的基础能力：支持基本的分类任务。例如，辨别＜好＞与〈不好〉的排班表。-基于这项基础，未来可以进一步组合AE模型，来进行理想的排班补值（生成），由AI来帮助您做〈智慧排班〉的工作。・祝福您轻松愉快更上一层楼。范例实践I延续上一小节的护理排班范例10RARARAOFRARARAOF110001000100011000000100010001101218范例实践步骤1：建立FX(FeatureExtractor)模型-目标一FX模型，能吸纳专家的智慧：即分辨〈花式排班〉。-方法--设计一个分类器，让专家贴上标签，进行训练，学习专家直觉。范例实践|步骤2:迁移出3个卷积核(Kernel)-目标一建立特征提取器，例如卷积核(Kernel)。-方法一从(步骤1)已经训练好的分类器里，迁移出Wh成为3个卷积核。范例实践|步骤3:幵始进行卷积-目标一针对排班表进行特征提取（即卷积），如同专家审视排班表。-方法--让3个卷积核（昵称：丫环），对排班表进行卷积。范例实践|步骤4:将3个特征表（Featuremap）汇合-目标一针对排班表的每一笔，得出一个特征表。-方法一使用分类器的Wo来3个（丫环）特征表，计算（汇合）出单一特征表。范例实践|步骤5:设计高层分类器（昵称：格格），并进行训练。-目标一设计&训练格格（分类器）。-方法一拿（步骤4）特征表，作为格格分类模型的输入（训练）资料，并展幵训练。范例实践|步骤6:汇出FX和FCL两个模型-目标一提供两个*.pb档案给OpenVINO。-方法一将训练好的FX和FCL模型导出到*.pb档案。范例实践|撰写Python程序•撰写一支Python程序。-训练好的FX和FCL模型导出到*.pb档案。],dtype=np,floatB2)CIDI苴BFa/rly([[LO.0,0,1,0,0,13][1,0,0,0,OJ.O^CJ[l,0f0jc,0,03,0][1,0,0』，0,0,0,1][0,1,0.11,1,0.0,01[0,0,L0,l,0,0,0][0J.0J,1,0,0,01[0Jf0,0,03,0,0][DJ.OJO,0,0,1,01[04,0,0,CI,O』，1][0JJf0,0,1,0,01[0,0,0J,0,1,0,0][OfOJFOf0,0,1,03[OfOJ,O70,0,03][D,OfO,l,0,03,0][0,0,0,1,0,0,0,1]dtt=np.array([O,O,OfO,0,0,0,0,lfl,O],dtype=np,float32)#---------tinued---————-——-二^--------------------------defsigmoid(x):return1/(14-np.6xp(-x))准备FX（特征提取）模型的训练数据#ex_Al1_06.pyimportnumpy豎npfromkerasmodelsimportSequentialfromkeras.layersimpo11ActIvationfDensefromkeras.optimizersimportSGDkerashack巳ndKimporttensorfIowastf訓練1500回合N(LP護理排班Al梧代表：OF<代表：BC代表：IB（代表：RA0代表Good1代表Bad1000010010000010100000010100100000101000000I10000100卻1000100v00100100000■100100100000101000010001000100001000I00100001000■1AT0000FileEditFormatRunOptionsV/indowHelp撰写Python程序whi=[np.lrriy(continued[[0[-0.[0,[0,ro.[o,[-0.[-0,np.aU.1],0.0,0J]0,1]0J]0.1]0J]0.1]0J]dtype^np,float32),0.0],dtype=np.f1oat32权重初期woi=[np.array([[-0.1],[0,5],>0,1]],dtype=np,float32),np.array([0,0],dtype=np.float32)]丿kernel=Nonebias=Nonewo=Nonebo=Nonefeature_map=Nonepo_fm=Nonemodel_l=NonAmodel2=NonpContinued准备FX的丿o?5■555555