Deep Learning and Neural Networks Assignment 3




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ECE4179/5179 – Deep Learning and Neural Networks
Assignment 3

General Comments.
• 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
your report.
• 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 Total
Points: 40 45 15 100
ECE4179/5179 Assignment3, Page 3 of 5 Due: 18:00, 26/9/2021
Data. In this assignment, we work with CNNs. The dataset we use is STL10. The dataset represents
10 object classes, namely “airplane”, “bird”, “car”, “cat”, “deer”, “dog”, “horse”, “monkey, “ship”, and
“truck”. Download the data for this assignment from this link (file is 350MB). Our dataset has 13,000 color
images of size 96 × 96. In this assignment, we use 8,000, 2,000, and 3,000 images for training, validation and
testing, respectively. You will use the training images to train your model. You can use validation set to
tune the hyper-parameters of your model (e.g., learning rate) or pick a model (e.g., after training for 100
epochs, the model that has the best accuracy on both training and validation images is chosen as the best
model). You will use testing images to report the accuracy of your trained model once you have picked a
model according to the above.
1. Shallow CNN. For this question, you design a shallow CNN and use it to classify STL images. Use
“hw3.ipynb” as a starter code and implement the following CNN.
image → conv1 → ReLU → conv2 → ReLU → conv3 → ReLU → maxpool → fc1 → ReLU → fc2
Table 1 below provides details of each layer. Please note that you need to flatten the output of the
maxpooling layer (using the view function in PyTorch) to connect the fc layers. Also, note that the
output of fc2 is fed to a softmax layer but since in PyTorch, the CrossEntropyLoss has an inbuilt
softmax function, the softmax layer is not shown here.
Table 1: Q1 – network structure
Name Type in out kernel size padding stride
conv1 Conv2D 3 96 7 × 7 0 2
conv2 Conv2D 96 64 5 × 5 0 2
conv3 Conv2D 64 128 3 × 3 0 2
fc1 Linear 1152 128 NA NA NA
fc2 Linear 128 10 NA NA NA
maxpool Pooling NA NA 3 × 3 0 3
1.1. [20 points] Train the network described above. In your report, plot the training and validation loss
(per epoch). Also, plot training, validation and test accuracy per epoch. Detail out the value of
hyperparameters, the optimizer used and all other relevant information in your report.
1.2. [5 points] Once training is done, pass all your validation images through the network and plot the
top five images correctly classified per class. That is, for each class, pick five images that are
correctly classified by your network and have the maximum softmax scores.
1.3. [5 points] Repeat the above but this time plot the top five images that are misclassified for each
class (i.e., your network is very confident about its decision but the decision is totally wrong).
1.4. [10 points] A confusion matrix is a table used to describe the performance of a classifier. It allows
easy identification of class confusions (e.g., one class might be mislabeled as the other more often).
Read more about the confusion matrix from the encyclopedia of machine learning (see the pdf file
Ting2010 ConfusionMatrix.pdf). Compute the confusion matrix of your training, validation, and
test data. Are they following a similar pattern?
ECE4179/5179 Assignment3, Page 4 of 5 Due: 18:00, 26/9/2021
2. Deep CNN. Use “hw3.ipynb” as a starter code for this question and develop a deep CNN to classify
STL images. Your network has 4 convolutional blocks. We denote a convolutional block by conv-blk
hereafter. The structure of a conv-blk is as follows;
Figure 1: Structure of the conv-blk.
The details of layers inside the conv-blk are depicted in Table 2. In essence, a convolutional block
receives an input x of size ci × Hi × Wi and processes it with 3 convolutional layers followed by ReLU
non-linearity. The first convolutional layer has co filters of size 3 ×3. With an stride of two and padding
of one, the first convolutional layer creates a feature map of size co × Hi/2 × Wi/2. This is further
processed by 1 × 1 and 3 × 3 convolutions (and non-linearity).
Table 2: Q2 – Details of the convolutional block
Name Type in out kernel size padding stride
Conv1 Conv2D ci co 3 × 3 1 2
Conv2 Conv2D co co 1 × 1 0 1
Conv3 Conv2D co co 3 × 3 1 1
Our deep CNN uses a stack of four of the aforementioned blocks. This will create a feature map of
spatial resolution 6 × 6 ( 96 blk1
−−→ 48 blk2
−−→ 24 blk3
−−→ 12 blk4
−−→ 6). The network then uses a Global Average
Pooling (GAP) layer followed by a linear layer. Put all together, the structure of the network reads as:
image → conv-blk1 → conv-blk2 → conv-blk3 → conv-blk4 → GAP → fc
The details of the conv-blks are depicted in Table 3.
Table 3: Q2 – Block structure
Name in out
conv-blk1 3 32
conv-blk2 32 64
conv-blk3 64 128
conv-blk3 128 192
fc1 192 10
2.1. [25 points] Implement and train the network described above. In your report, plot the training and
validation loss (per epoch). Also, plot training, validation and test accuracy per epoch. Detail out
the value of hyperparameters, the optimizer used and all other relevant information in your report.
2.2. [10 points] Use data augmentation and normalization techniques to improve the performance of your
network. In particular, check “torchvision.transforms”. Commonly used augmentation techniques
include random flip and random crop, while channel normalization (e.g., RGB) can be beneficial.
2.3. [10 points] You are allowed to add or decrease the number of blocks or their structures. Can you
design a better network as compared to the original structure suggested in question 2? Detail out
your design. A better network may have 1. less parameters, 2. faster convergence behaviour, 3.
better accuracy or all of them. It would be useful to pay attention to the confusion matrices over
the validation set, as it may suggest models that can outperform others for some particular classes.
ECE4179/5179 Assignment3, Page 5 of 5 Due: 18:00, 26/9/2021
3. [15 points] Occlusion Sensitivity. A way of visualizing a CNN is called “Occlusion Sensitivity”.
Basically, one hides a rectangular patch of the input of a CNN and measures the softmax score for
occluded input. If the patch occluds essential information about the input, the softmax score will drop
significantly. By sliding the patch over the input, one can identify and visualize which parts of the image
are more important for classification. To give an example, if your CNN classifies dogs, you expect by
applying the occlusion sensitivity, softmax scores of the class dog drop when the occlusion slides over the
face of the dog. This shows that your CNN is not picking up clues from the background to recognize dogs.
For more details, check part 4.2 of the paper “Visualizing and Understanding Convolutional Networks”
by Zeiler and Fergus. Implement the occlusion sensitivity and show the sensitivity heatmap for the
images from parts 1.2 and 1.3 in question 1. Discuss the effect of the size of rectangular patch.