COMP9444代做、代写Python编程设计

日期：2024-07-03 10:23

COMP9444 Neural Networks and Deep Learning

Term 2, 2024

Assignment - Characters and Hidden Unit Dynamics

Due: Tuesday 2 July, 23:59 pm

Marks: 20% of final assessment

In this assignment, you will be implementing and training neural network models for three

different tasks, and analysing the results. You are to submit two Python files and , as well as

a written report (in format). kuzu.pycheck.pyhw1.pdfpdf

Provided Files

Copy the archive hw1.zip into your own filespace and unzip it. This should create a directory ,

subdirectories and , and eight Python files , , , , , , and .

hw1netplotkuzu.pycheck.pykuzu_main.pycheck_main.pyseq_train.pyseq_models.pyseq_plot.pyanb2n.py

Your task is to complete the skeleton files and and submit them, along with your report.

kuzu.pycheck.py

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 which computes a linear function of the pixels in the

image, followed by log softmax. Run the code by typing: 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 columnsindicate 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. NetLin

python3 kuzu_main.py --net lin

2. [1 mark] Implement a fully connected 2-layer network (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: 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. NetFull

python3 kuzu_main.py --net full

3. [2 marks] Implement a convolutional network called , 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: 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.

NetConv

python3 kuzu_main.py --net conv

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: Multi-Layer Perceptron

In Part 2 you will be exploring 2-layer neural networks (either trained, or designed by hand)

to classify the following data:

1. [1 mark] Train a 2-layer neural network with either 5 or 6 hidden nodes, using sigmoid

activation at both the hidden and output layer, on the above data, by typing: You may

need to run the code a few times, until it achieves accuracy of 100%. If the network

appears to be stuck in a local minimum, you can terminate the process with ⟨ctrl⟩-Cand start again. You are free to adjust the learning rate and the number of hidden

nodes, if you wish (see code for details). The code should produce images in the

subdirectory graphing the function computed by each hidden node () and the

network as a whole (). Copy these images into your report.

python3 check_main.py --act sig --hid 6

plothid_6_?.jpgout_6.jpg

2. [2 marks] Design by hand a 2-layer neural network with 4 hidden nodes, using the

Heaviside (step) activation function at both the hidden and output layer, which

correctly classifies the above data. Include a diagram of the network in your report,

clearly showing the value of all the weights and biases. Write the equations for the

dividing line determined by each hidden node. Create a table showing the activations

of all the hidden nodes and the output node, for each of the 9 training items, and

include it in your report. You can check that your weights are correct by entering them

in the part of where it says "Enter Weights Here", and typing: check.py

python3 check_main.py --act step --hid 4 --set_weights

3. [1 mark] Now rescale your hand-crafted weights and biases from Part 2 by multiplying

all of them by a large (fixed) number (for example, 10) so that the combination of

rescaling followed by sigmoid will mimic the effect of the step function. With these rescaled

weights and biases, the data should be correctly classified by the sigmoid

network as well as the step function network. Verify that this is true by typing: Once

again, the code should produce images in the subdirectory showing the function

computed by each hidden node () and the network as a whole (). Copy these images

into your report, and be ready to submit with the (rescaled) weights as part of your

assignment submission.

python3 check_main.py --act sig --hid 4 --set_weights

plothid_4_?.jpgout_4.jpgcheck.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 and . seq_train.pyseq_plot.py1. [2 marks] Train a Simple Recurrent Network (SRN) on the Reber Grammar prediction

task by typing This SRN has 7 inputs, 2 hidden units and 7 outputs. The trained

networks are stored every 10000 epochs, in the subdirectory. After the training

finishes, plot the hidden unit activations at epoch 50000 by typing 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.

python3 seq_train.py --lang reber

net

python3 seq_plot.py --lang reber --epoch 50

2. [1 mark] Train an SRN on the a

nb

n

language prediction task by typing The a

nb

n

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.

python3 seq_train.py --lang anbn

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 in the

range of 0.01 to 0.03. 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.Briefly explain how the a

nb

n

prediction task is achieved by the network, based on the

generated figure. 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.

3. [2 marks] Train an SRN on the a

nb

n

c

n language prediction task by typing 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 (and re-start, 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 to 0.03.

python3 seq_train.py --lang anbncn

After the training finishes, plot the hidden unit activations at epoch 200000 by typing

python3 seq_plot.py --lang anbncn --epoch 200

(you can choose a different epoch number, if you wish). This should produce three

images labeled , and also display an interactive 3D figure. Try to rotate the figure in 3

dimensions to get one or more good view(s) of the points in hidden unit space, save

them, and include them in your report. (If you can't get the 3D figure to work on your

machine, you can use the images anbncn_srn3_??.jpganbncn_srn3_??.jpg)

Briefly explain how the a

nb

n

c

n

prediction task is achieved by the network, based on

the generated figure. 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.

4. [3 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).

python3 seq_train.py --lang reber --embed True --model lstm --hid 4

Submission

You should submit by typing

give cs9444 hw1 kuzu.py check.py hw1.pdf

You can submit as many times as you like — later submissions will overwrite earlier ones.

You can check that your submission has been received by using the following command:

9444 classrun -check hw1

The submission deadline is Tuesday 2 July, 23:59pm. In accordance with UNSW-wide

policies, 5% penalty will be applied for every 24 hours late after the deadline, up to a

maximum of 5 days, after which submissions will not be accepted.

Additional information may be found in the FAQ and will be considered as part of the

specification for the project. You should check this page regularly.Plagiarism Policy

Group submissions will not be allowed for this assignment. Your code and report must be

entirely your own work. Plagiarism detection software will be used to compare all

submissions pairwise (including submissions for similar assignments from previous offering,

if appropriate) and serious penalties will be applied, particularly in the case of repeat

offences.

DO NOT COPY FROM OTHERS; DO NOT ALLOW ANYONE TO SEE YOUR CODE

Please refer to the UNSW Policy on Academic Integrity and Plagiarism if you require further

clarification on this matter.

Good luck!