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COMP9444 Neural Networks and Deep
Creation date：2024-05-30 17:16:10
Belonging category：计算机computer science
Neural Networks and Deep

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COMP9444 Neural

Networks and Deep
Learning
Term 2, 2023
Project 1 - Characters and Hidden Unit
Dynamics
Due: Wednesday 5 July, 23:59 pm
Marks: 20% of final assessment
In this assignment, you will be implementing and training
various neural network models for three different tasks,
and analysing the results.
You are to submit two Python files kuzu.py and
encoder.py, as well as a written report hw1.pdf (in pdf
format).
Provided Files
Copy the archive hw1.zip into your own filespace and
unzip it. This should create a directory hw1, subdirectory
net, and ten Python files kuzu.py, encoder.py,
kuzu_main.py, encoder_main.py, encoder_model.py,
seq_train.py, seq_models.py, seq_plot.py, reber.py and
anbn.py.
Your task is to complete the skeleton files kuzu.py and
encoder.py and submit them, along with your report.
Part 1: Japanese Character Recognition
For Part 1 of the assignment you will be implementing
networks to recognize handwritten Hiragana symbols. The
dataset to be used is Kuzushiji-MNIST or KMNIST for
short. The paper describing the dataset is available here.
It is worth reading, but in short: significant changes
occurred to the language when Japan reformed their
education system in 1868, and the majority of Japanese
today cannot read texts published over 150 years ago.
This paper presents a dataset of handwritten, labeled
examples of this old-style script (Kuzushiji). Along with
this dataset, however, they also provide a much simpler
one, containing 10 Hiragana characters with 7000
samples per class. This is the dataset we will be using.
Text from 1772 (left) compared to 1900 showing the standardization of written
Japanese.
1. [1 mark] Implement a model NetLin which computes
a linear function of the pixels in the image, followed
by log softmax. Run the code by typing:
python3 kuzu_main.py --net lin
Copy the final accuracy and confusion matrix into
your report. The final accuracy should be around
70%. Note that the rows of the confusion matrix
indicate the target character, while the columns
indicate the one chosen by the network. (0="o",
1="ki", 2="su", 3="tsu", 4="na", 5="ha", 6="ma",
7="ya", 8="re", 9="wo"). More examples of each
character can be found here.
2. [1 mark] Implement a fully connected 2-layer network
NetFull (i.e. one hidden layer, plus the output layer),
using tanh at the hidden nodes and log softmax at
the output node. Run the code by typing:
python3 kuzu_main.py --net full
Try different values (multiples of 10) for the number
of hidden nodes and try to determine a value that
achieves high accuracy (at least 84%) on the test
set. Copy the final accuracy and confusion matrix
into your report, and include a calculation of the total
number of independent parameters in the network.
3. [2 marks] Implement a convolutional network called
NetConv, with two convolutional layers plus one fully
connected layer, all using relu activation function,
followed by the output layer, using log softmax. You
are free to choose for yourself the number and size of
the filters, metaparameter values (learning rate and
momentum), and whether to use max pooling or a
fully convolutional architecture. Run the code by
typing:
python3 kuzu_main.py --net conv
Your network should consistently achieve at least
93% accuracy on the test set after 10 training
epochs. Copy the final accuracy and confusion
matrix into your report, and include a calculation of
the total number of independent parameters in the
network.
4. [4 marks] Briefly discuss the following points:
a. the relative accuracy of the three models,
b. the number of independent parameters in each
of the three models,
c. the confusion matrix for each model: which
characters are most likely to be mistaken for
which other characters, and why?
Part 2: Encoder Networks
In Part 2 you will be editing the file encoder.py to create a
dataset which, when run in combination with
encoder_main.py, produces the following image (which is
intended to be a stylized map of mainland China).
You should first run the code by typing
python3 encoder_main.py --target star16
Note that target is determined by the tensor star16 in
encoder.py, which has 16 rows and 8 columns, indicating
that there are 16 inputs and 8 outputs. The inputs use a
one-hot encoding and are generated in the form of an
identity matrix using torch.eye()
1. [2 marks] Create by hand a dataset in the form of a
tensor called ch34 in the file encoder.py which, when
run with the following command, will produce an
image essentially the same as the one shown above
(but possibly rotated or reflected).
python3 encoder_main.py --target ch34
The pattern of dots and lines must be topologically
identical. But, it is fine for it to be rotated or reflected,
compared to the image above. Note in particular the
five "anchor points" in the corners and on the edge
of the figure.
Your tensor should have 34 rows and 23 columns.
Include the final image in your report, and include the
tensor ch34 in your file encoder.py
Part 3: Hidden Unit Dynamics for Recurrent
Networks
In Part 3 you will be investigating the hidden unit
dynamics of recurrent networks trained on language
prediction tasks, using the supplied code seq_train.py
and seq_plot.py.
1. [2 marks] Train a Simple Recurrent Network (SRN) on
the Reber Grammar prediction task by typing
python3 seq_train.py --lang reber
This SRN has 7 inputs, 2 hidden units and 7 outputs.
The trained networks are stored every 10000 epochs,
in the net subdirectory. After the training finishes,
plot the hidden unit activations at epoch 50000 by
typing
python3 seq_plot.py --lang reber --epoch 50
The dots should be arranged in discernable clusters
by color. If they are not, run the code again until the
training is successful. The hidden unit activations are
printed according to their "state", using the colormap
"jet":
Based on this colormap, annotate your figure (either
electronically, or with a pen on a printout) by drawing
a circle around the cluster of points corresponding to
each state in the state machine, and drawing arrows
between the states, with each arrow labeled with its
corresponding symbol. Include the annotated figure
in your report.
2. [1 mark] Train an SRN on the anbn language
prediction task by typing
python3 seq_train.py --lang anbn
The anbn language is a concatenation of a random
number of A's followed by an equal number of B's.
The SRN has 2 inputs, 2 hidden units and 2 outputs.
Look at the predicted probabilities of A and B as the
training progresses. The first B in each sequence and
all A's after the first A are not deterministic and can
only be predicted in a probabilistic sense. But, if the
training is successful, all other symbols should be
correctly predicted. In particular, the network should
predict the last B in each sequence as well as the
subsequent A. The error should be consistently
below 0.01. If the network appears to have learned
the task successfully, you can stop it at any time
using ⟨cntrl⟩-c. If it appears to be stuck in a local
minimum, you can stop it and run the code again until
it is successful.
After the training finishes, plot the hidden unit
activations by typing
python3 seq_plot.py --lang anbn --epoch 100
Include the resulting figure in your report. The states
are again printed according to the colormap "jet".
Note, however, that these "states" are not unique but
are instead used to count either the number of A's we
have seen or the number of B's we are still expecting
to see.
3. [1 mark] Briefly explain how the anbn prediction task
is achieved by the network, based on the figure you
generated in Question 2. Specifically, you should
describe how the hidden unit activations change as
the string is processed, and how it is able to correctly
predict the last B in each sequence as well as the
following A.
4. [1 mark] Train an SRN on the anbncn language
prediction task by typing
python3 seq_train.py --lang anbncn
The SRN now has 3 inputs, 3 hidden units and 3
outputs. Again, the "state" is used to count up the A's
and count down the B's and C's. Continue training
(re-starting, if necessary) for 200k epochs, or until
the network is able to reliably predict all the C's as
well as the subsequent A, and the error is
consistently in the range of 0.01 or 0.02.
After the training finishes, plot the hidden unit
activations by typing
Rotate the figure in 3 dimensions to get one or more
good view(s) of the points in hidden unit space.
5. [1 mark] Briefly explain how the anbncn prediction
task is achieved by the network, based on the figure
you generated in Question 4. Specifically, you should
describe how the hidden unit activations change as
the string is processed, and how it is able to correctly
predict the last B in each sequence as well as all of
the C's and the following A.
6. [4 marks] This question is intended to be more
challenging. Train an LSTM network to predict the
Embedded Reber Grammar, by typing
You can adjust the number of hidden nodes if you
wish. Once the training is successful, try to analyse
the behavior of the LSTM and explain how the task is
accomplished (this might involve modifying the code
so that it returns and prints out the context units as
well as the hidden units).
Case category
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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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