Beginning Anomaly Detection Using



Download 26,57 Mb.
Pdf ko'rish
bet130/283
Sana12.07.2021
Hajmi26,57 Mb.
#116397
1   ...   126   127   128   129   130   131   132   133   ...   283
Bog'liq
Beginning Anomaly Detection Using Python-Based Deep Learning

Figure 5-22.  Looking at the values for the column Amount to see how they were 

standardized

Figure 5-23.  Looking at the values for the column Time to see how they were 

standardized

Chapter 5   Boltzmann maChines




190

Awesome; looking much better. Now, you can define your training and testing data 

sets (see Figure 

5-24


).

Figure 5-24.  This is a different process than usual because of how the RBM model 

expects the input

Figure 5-25.  The output shapes of the training and testing sets

You should see something like Figure 

5-25

 as the output.



Chapter 5   Boltzmann maChines


191

Getting to the model itself, use the code in Figure 

5-26

.

Figure 5-26.  Initializing the model with a set of parameters



The parameters are as follows:

• 

num_visible: The number of nodes in the visible layer

• 

num_hidden: The number of nodes in the hidden layer

• 


Download 26,57 Mb.

Do'stlaringiz bilan baham:
1   ...   126   127   128   129   130   131   132   133   ...   283




Ma'lumotlar bazasi mualliflik huquqi bilan himoyalangan ©hozir.org 2024
ma'muriyatiga murojaat qiling

kiriting | ro'yxatdan o'tish
    Bosh sahifa
юртда тантана
Боғда битган
Бугун юртда
Эшитганлар жилманглар
Эшитмадим деманглар
битган бодомлар
Yangiariq tumani
qitish marakazi
Raqamli texnologiyalar
ilishida muhokamadan
tasdiqqa tavsiya
tavsiya etilgan
iqtisodiyot kafedrasi
steiermarkischen landesregierung
asarlaringizni yuboring
o'zingizning asarlaringizni
Iltimos faqat
faqat o'zingizning
steierm rkischen
landesregierung fachabteilung
rkischen landesregierung
hamshira loyihasi
loyihasi mavsum
faolyatining oqibatlari
asosiy adabiyotlar
fakulteti ahborot
ahborot havfsizligi
havfsizligi kafedrasi
fanidan bo’yicha
fakulteti iqtisodiyot
boshqaruv fakulteti
chiqarishda boshqaruv
ishlab chiqarishda
iqtisodiyot fakultet
multiservis tarmoqlari
fanidan asosiy
Uzbek fanidan
mavzulari potok
asosidagi multiservis
'aliyyil a'ziym
billahil 'aliyyil
illaa billahil
quvvata illaa
falah' deganida
Kompyuter savodxonligi
bo’yicha mustaqil
'alal falah'
Hayya 'alal
'alas soloh
Hayya 'alas
mavsum boyicha


yuklab olish