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# Deep Learning and Neural Networks Assignment 2

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ECE4179/5179 – Deep Learning and Neural Networks
Assignment 2

• Please submit your report along with the code (Jupyter notebook is preferred) as a single zip file.
• Include your name and student number in the filename for both the zip file and PDF. Do not send
doc/docx files.
• Include your name and email address on the report and use a single column format when you prepare
• Please ensure all of your results are included in your report. Marking is based on what is in the report.
This includes plots, tables, code screen-shots etc.
• Make sure you answer questions in full and support any discussions with relevant additional information/experiments if necessary.
Late submission. Late submission of the assignment will incur a penalty of 10% for each day late. That is
with one day delay, the maximum mark you can get from the assignment is 90 out of 100, so if you score 99,
we will (sadly) give you 90. Assignments submitted with more than a week delay will not be assessed. Please
apply for special consideration for late submission as soon as possible (e.g., documented serious illness).
Note from ECE4179/5179 Team. The nature of assignments in ECE4179/5179 is different from many
other courses. Here, we may not have a single solution to a problem. Be creative in your work and feel free
to explore beyond questions. Creativity will be awarded by bonus points.
Good Luck
Question: 1 2 3 4 Total
Points: 35 15 35 15 100
Score:
ECE4179/5179 Assignment2, Page 3 of 6 Due: 18:00, 6/9/2021
1. Using “hw2 Q1.ipynb” as starter code, implement the following network:
R
4096 3 x → fc1 : Linear(4096 × n) → ReLU → fc2 : Linear(n × 4096) = xˆ .
That is, the input x which is a 4096D vector is first mapped to an n dimensional vector using the fc1
layer (which is a linear layer). The resulting vector goes through the ReLU nonlinearity followed by
another linear layer (fc2) to create a 4096 dimensional vector (which is called xˆ). For the sample x,
consider the loss of the network to be kx − xˆk
2
(check MSELoss in the PyTorch documents).
1.1. [15 points] Train the network described above for n = 16 and 64. Let W ∈ R
4096×n be the weight
matrix of fc1. For each network, save the weights of fc1 by reshaping every column to a 64 × 64
image during the course of training. For example and when n = 16, this should result in 16 images.
If you need 100 epochs to fully train your network, store the weights as images at regular intervals
(say every 10 epochs) starting from the random initialization (epoch 0) till the final epoch. In
your report, discuss the following points;
 state how you identified the value of the learning rate for the experiment.
 plot the final output for some samples for each network. Note that the output xˆ can be reshaped
to an image of size 64 × 64.
 plot the stored weights as images (e.g., every 10 epochs). Is this what you expected?
1.2. [10 points] Read the Wiki article on Eigenfaces at https://en.wikipedia.org/wiki/Eigenface.
Use the SVD method described under Computing the eigenvectors to obtain n Eigenfaces from
your data (remember each column of matrix X should be a face here). Discuss, if any, links between
the Eigenface method and your network.
1.3. [10 points] The network you have implemented in the above parts is a simple form of an AutoEncoders
(AEs). Do you think such a neural network can be used to denoise/recover contaminated face images? What will happen if you randomly zero out some pixels and then feed the image to your
Hint. You may want to use ADAM optimizer instead of SGD (see PyTorch documents). If you
opt to use ADAM optimizer, remember that usually the learning rate for the ADAM is smaller
than that of SGD.
ECE4179/5179 Assignment2, Page 4 of 6 Due: 18:00, 6/9/2021
2. Shallow MLP. Use “hw2 Q2 and Q3.ipynb” as the starter code for this question. We are interested
in designing an image classifier for Q2. The data file “hw2 Q2 and Q3 data.npz” contains images and
labels for training, validation and test purposes. In all the parts below, you should only use the training
images and their labels to train your model. You may use the validation set to pick a trained model.
For example, during training, you can test the accuracy of your model using the validation set every
epoch and pick the model that achieves the highest validation accuracy. You should then report your
results on the test set once you choose your model. Train a shallow MLP, consisting of just one hidden
layer to classify the images according to the following design;
R
784 3 x → fc1 : Linear(784 × n) → ReLU → fc2 : Linear(n × 10) = yˆ ,
where n = 32. In your report, study the following factors,
2.1. [10 points] Train your network exclusively with SGD optimizer and make sure that the network has
completely converged (i.e., till the training loss flattens out). Discuss the followings in your report;
• Plot the training loss, validation loss, and validation accuracy per epoch.
• As a data scientist, after training, you need to pick a model for deployment. To identify the best
model after training, you can choose a model that achieves the lowest training loss, the lowest
validation loss, or the highest validation accuracy. Use the test set and evaluate the trained
model for each of the aforementioned choices. Comment on how you would pick a model in
general based on your observations in this experiment.
2.2. [5 points] Learning Rate. When using SGD as an optimizer, it can be beneficial to reduce the
learning rate after a few epochs. For the network with 32 hidden units from the previous part,
complete the following study. First, use a fixed learning rate throughout training. Then by studying
the behaviour of the loss curve, try to identify when loss stops changing noticeably (hence network
stops learning) and decrease the learning rate accordingly. For example, if the loss becomes steady
after epoch 55, decrease the learning rate by a factor of 10 from that epoch and observe the behaviour
of the network. In your report, plot the training and validation accuracy and loss and discuss the
impact of decreasing the learning rate during training in your report.
ECE4179/5179 Assignment2, Page 5 of 6 Due: 18:00, 6/9/2021
3. Deep MLP. Use “hw2 Q2 and Q3.ipynb” as the starter code again. In this question, we are interested
in designing deep MLPs with three hidden layers to classify images.
3.1. [15 points] Design and train the following deep MLP.
R
784 3 x → fc1 : Linear(784 × 32) → ReLU → fc2 : Linear(32 × 64)
yˆ ← fc4 : Linear(32 × 10) ← ReLU ← fc3 : Linear(64 × 32) ← ReLU
In your report, plot the training and validation accuracies and losses per epoch. Compare the
performance of the deep MLP with the shallow MLP you trained in question 2.
3.2. [10 points] For this part, you train a wider deep MLP. Design and train the following deep MLP.
R
784 3 x → fc1 : Linear(784 × 128) → ReLU → fc2 : Linear(128 × 64)
yˆ ← fc4 : Linear(128 × 10) ← ReLU ← fc3 : Linear(64 × 128) ← ReLU
In your report, plot the training and validation accuracies and losses per epoch. Compare the
performance of the wide deep MLP with the one you trained in the part 3.1.
3.3. [5 points] Calculate the number of parameters for the MLPs you trained in part 3.1 and part 3.2.
3.4. [5 points] AI can benefit society in many ways but, given the energy needed to support the computing behind AI, these benefits can come at a high environmental price. Packages such as CarbonTracker can be used as tools for tracking and predicting the energy consumption and carbon
footprint of training deep learning models. Measure the carbon footprint of question 3.2.
Hint. If you do not have access to an Nvidia GPU, use Google Colab for this purpose.
ECE4179/5179 Assignment2, Page 6 of 6 Due: 18:00, 6/9/2021
4. When Training data can mislead you. Use “hw2 Q4.ipynb” as a starter code for this question. We
are interested in designing an image classifier for Q3 as well. The data file “hw2 Q4 data.npz” contains
images and labels for training and test purposes. You should only use the training images and their
labels to train your model. You do not have a validation set, so you pick your model once training is
completed.
4.1. [15 points] Based on your expertise from Q2, design a deep model with at least two hidden layers
and use ADAM optimizer to fully train your model. During the training, plot the test accuracy
after each epoch. If you observe a strange behaviour, discuss what might be the source of that.

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