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Machine Learning Assignment

创建日期：2024-06-28 17:51:45

所属分类：计算机computer science

标签： Machine

Machine Learning Assignment

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Question 1: 10 points
In Keras download CIFAR10 dataset using keras.datasets.cifar10.load_data().
The dataset is composed of 60,000 3232-pixel color images (50,000 for training,
10,000 for testing) with 10 classes
Build a DNN with 20 hidden layers of 100 neurons each (that’s too many,
but it’s the point of this exercise). Use He initialization and the ELU activation
function. At the output layer use a softmax output layer with 10 neurons
Question 2: 10 points
Using Nadam optimization and early stopping, train the network.
Remember to search for the right learning rate each time you change the
model’s architecture or hyperparameters.
Question 3: 10 points
Add Batch Normalization and compare the learning curves: Is it converging
faster than before? Does it produce a better model? How does it aect training
speed?
Question 4: 10 points
Try replacing Batch Normalization with SELU, and make the necessary adjuste-
ments to ensure the network self-normalizes (i.e., standardize the input features,
use LeCun normal initialization, make sure the DNN contains only a sequence
of dense layers, etc.).
Question 5: 10 points
Try regularizing the model with alpha dropout. Then, without retraining your
model, see if you can achieve better accuracy using MC Dropout.

Question 6: 10 points
Retrain your model using 1cycle scheduling and see if it improves training speed
and model accuracy.
Requirements
These are the general rules for submitting the homework not only for this as-
signment.
1. Submit jupyter notebook(s) in ipynb and html formats: html format allows
the grader to easily read it in canvas while ipynb allows to execute it if
something is not clear from html.
2. The notebook should be well formatted:
Use markdown to break it into sections, bullet points, etc.
{ See l1_n3.ipynb for example how to use it.
{ If you double click on markdown cell, you can see the code.
{ To show the formatted text, execute the corresponding mark-
down cell.
{ Start the notebook with a markdown title cell of the form:
‘# Assignment X, Jane Doe, date’.
{ When answering a particular question from the assignment, start
the answer with markdown cell
‘## Question Y’
that indicates what question you are answering.
3. Name your notebook (and the corresponding html le) according to the
following template: Assignment_X_Jane_Doe.ipynb
4. If you are submitting several notebooks, append to the name ‘_partZ’
and explain in canvas what each notebook is about.
5. There should not be any failed cells in the notebook. Every cell should
work and have the results of the execution.
6. The notebook should contain only what is needed to answer a particular
question and nothing else. More is as bad as less.
7. All the plots, if needed for the assignment, should be of production quality
with readable labels, titles, etc. Also, try to nd the most approriate type
of plot to clearly demonstrate your statement.

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关键词一关键词二关键词三关键词四关键词五和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 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1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 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