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COMP9444 Neural Networks and Deep Learning
创建日期：2024-06-05 14:20:58
标签： COMP9444
Neural Networks and Deep Learning

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COMP9444 Neural Networks and Deep Learning

Assignment - Characters and Hidden Unit Dynamics
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 kuzu.py and check.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, subdirectories net and plot, and eight Python files kuzu.py, check.py, kuzu_main.py, check_main.py, seq_train.py, seq_models.py, seq_plot.py and
anb2n.py.
Your task is to complete the skeleton files kuzu.py and check.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: 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:
python3 check_main.py --act sig --hid 6
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⟩-C and 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 plot subdirectory graphing the function computed by each hidden node (hid_6_?.jpg) and the network as a whole (out_6.jpg). Copy these
images into your report.
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. 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 check.py where it says "Enter Weights Here", and typing:
python3 check_main.py --act step --hid 4 --set_weights

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