COMP9444 Python

Java Python 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