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pytorch代寫-COMP9444
時間:2021-07-12
COMP9444 Neural Networks and Deep Learning
Term 2, 2021
Project 1 - Characters, Spirals and Hidden Unit Dynamics
Due: Friday 16 July, 23:59 pm
Marks: 30% of final assessment
In this assignment, you will be implementing and training various neural network models for four different tasks, and
analysing the results.
You are to submit three Python files kuzu.py, rect.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 with the data file
rect.csv, subdirectories plot and net, as well as eleven Python files kuzu.py, rect.py, encoder.py, kuzu_main.py,
rect_main.py, endoder_main.py, encoder_model.py, seq_train.py, seq_plot.py, reber.py and anbn.py.
Your task is to complete the skeleton files kuzu.py, rect.py, 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.
3. [1 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.
4. [3 marks] Briefly discuss the following points:
a. the relative accuracy of the three models,
b. the confusion matrix for each model: which characters are most likely to be mistaken for which other
characters, and why?
Part 2: Rectangular Spirals Task
For Part 2 you will be training a network to distinguish two intertwined rectangular spirals. The supplied code
rect_main.py loads the training data from rect.csv, applies the specified model and produces a graph of the resulting
function, along with the data. For this task there is no test set as such, but we instead judge the generalization by plotting
the function computed by the network and making a visual assessment.
1. [2 marks] Provide code for a Pytorch Module called Network which is initialized with two parameters layer and
hid.
If layer == 1 the network should only have one hidden layer, with hid units. If layer == 2 it should have two
(fully connected) hidden layers, each with hid units. The tanh activation function should be applied at each hidden
layer, and sigmoid at the output layer.
2. [2 marks] Using graph_output() as a guide, write a method called graph_hidden(net, layer, node) which plots
the activation (after applying the tanh function) of the hidden node with the specified number (node) in the
specified layer (1 or 2). Specifically, it should show where the activation is positive and where it is negative.
Hint: you might need to modify forward() so that the hidden unit activations are retained, i.e. replace hid1 =
torch.tanh(...) with self.hid1 = torch.tanh(...)
3. [1 mark] Train a network with one hidden layer by typing:
python3 rect_main.py --layer 1 --hid 10
Try to determine a number of hidden nodes close to the mininum required for the network to be trained successfully
(although, it need not be the absolute minimum). You may need to run the network several times before finding a set
of initial weights which allows it to converge. (If it trains for a minute or so and seems to be stuck in a local
minimum, kill it with ?cntrl?-c and run it again). You are free to adjust the learning rate and initial weight size, if you
want to. The graph_output() method will generate a picture of the function computed by your Network and store it
in the plot subdirectory with a name like out?_?.png. You should include this picture in your report. Your
graph_hidden() method should generate plots of all the hidden nodes, which you should also include in your report.
4. [1 mark] Train a network with two hidden layers by typing:
python3 rect_main.py --layer 2 --hid 10
As before, try to determine a number of hidden nodes close to the mininum required for the network to be trained
successfully. You should include the graphs of the output and the hidden nodes in your report.
5. [3 marks] Briefly discuss the following points:
a. a qualitative description of the functions computed by the different layers of the two networks,
b. the qualitative difference, if any, between the overall function (i.e. output as a function of input) computed by
the two networks.
Part 3: Encoder Networks
In Part 3 you will be editing the file encoder.py to create a dataset which, when run in combination with
encoder_main.py, produces the stylized map of Australia shown below.
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 aust26 in the file encoder.py which, when run
with the following command, will produce an image essentially the same as the stylized map of Australia shown
above (but possibly rotated or reflected).
python3 encoder_main.py --target aust26
The pattern of dots and lines must be identical, except for the possible rotation or reflection. Note in particular the six
"anchor points" in the corners and on the edge of the figure.
Your tensor should have 26 rows and 20 columns. Include the final image in your report, and include the tensor
aust26 in your file encoder.py
Part 4: Hidden Unit Dynamics for Recurrent Networks
In Part 4 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. [3 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 in the range of 0.01 or 0.02. 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. [2 marks] With reference to the figure you generated in Question 2, briefly describe how the hidden unit activations
change as the 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) 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
python3 seq_plot.py --lang anbncn --epoch 200
Rotate the figure in 3 dimensions to get one or more good view(s) of the points in hidden unit space.
5. [2 marks] 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
python3 seq_train.py --lang reber --embed True --model lstm --hid 4
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.
Submission
You should submit by typing
give cs9444 hw1 kuzu.py rect.py encoder.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
The submission deadline is Friday 16 July, 23:59. 15% penalty will be applied to the (maximum) mark for every 24 hours
late after the deadline.
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 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!

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