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EEE3314 INTRODUCTION TO AI
创建日期：2024-08-15 15:03:53
所属分类：经济Economics
标签： EEE3314
INTRODUCTION TO AI

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INTRODUCTION TO AI


INSTRUCTIONS:

1. This paper consists of 5 pages with 2 Questions only.

2. You may use deep learning library/toolbox to solve the problem. However, you must describe

the related information such as name, version etc in the introduction part.

3. Submit a report containing the following items:

Example:

Introduction: A brief introduction of the problem.

Problems and Solution

Discussion

Conclusion

Remark: Introduction and conclusion only write once in the report.

4. Use English in the report.

5. Attach your code as an appendix. You may show the code snippet (not whole code) to aid your

explanation.

Question 1: AlexNet with CIFAR-10

In this question, we will adopt AlexNet discussed in the lecture. We will use CIFAR-10 dataset.

You can find the tutorial for how do you train a

CNN with CIFAR-10 according to the specific deep learning library [1]-[4]. Most of the major

deep learning libraries (Tensorflow, Pytorch, MATLAB) come with a tutorial with CIFAR10

example.

You should have good knowledge on AlexNet architecture so that you can modify the

hyperparameters in the network according to the CIFAR 10 characteristics.

Train a standard AlexNet with CIFAR 10 via CE loss. Note the following:

? Modify output layer (Softmax layer) to 10 nodes, correspond to 10 classes in CIFAR-10.

? Use original CIFAR 10 images (32 x 32 x 3) to input AlexNet. With this input size, you will

find feature map vanishes at the last conv layer, so modify the stride number, padding etc.

? If you face insufficent memory problem, modify the filter size, channel numbers, number of

neurons in the FC layers.

? What you cannot change is the architecture (5 conv layers, 3 max pooling layers and 2 FC

layers).

(a) Describe training setup eg. data pre-processing/augmentation, initialization, hyperparameters

such as learning rate schedule, momentum, dropout rate, batch number, BN etc. Present them

systematically in the table form.

(b) Fill in the table below to show the weights and neurons of each layer in your modified

AlexNet network.

(c) Plot the following:

? Training and test loss (not classification accuracy) vs epoch.

? Classification accuracy on the training and test set vs epoch.

(d) Fill the table with the final accuracy that you obtained.

Training Accuracy (%) Testing Accuracy (%)

Remark:

1. The testing accuracy should be at least 75% and above. With proper tuning the network

hyperparameters, data augmentation, BN etc you can achieve more than 85%.

2. The different of training accuracy and testing accuracy should not more than 10% only.

3. The generalization techniques discussed in the lecture would be helpful. The score for this part

is proportional to the accuracy that you can obtain. Note the accuracy values should be consistent

to the graph shown in (c). If no graph is shown, answer in (d) is discounted.

(e) Discuss the issues that you experienced and the effort that you’ve taken to overcome them.

This could be non-convergence training or/and poor testing accuracy etc.

Remark: If your machine is slow in solving this question due to CPU or poor GPU, consider

Google Colab if you are using Python.

Question 2: Transfer Learning

Now we want to see whether the features of AlexNet developed in Question 1 can transfer to other

domain. Here we use imdb_wiki face dataset (subset) that consists of 100 subjects with 30

samples per subject as target dataset. Note this is the case where source and target data are

dissimilar and small in size, relative to CIFAR 10.

(a) First, based on the model that has been trained with CIFAR 10, re-train the last (softmax)

layer in the prebuilt network in question 1 with face training samples (20 samples per subject).

This is equivalent to you freeze the rest of the layers in the network and replace with a new

(linear) classifier for face. Note number of output nodes should change to 100 instead of 10.

This serves as a baseline model. Evaluate the baseline model with face test set, which

composed of 10 samples per subject.

(b) Then, try to fine tune/remove/retrain chosen convolution and/or FC layers. Create three models

with different trials. Fill in the table below. Evaluate the model with face test set.

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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 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