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EECE 568 – Assignment 2

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EECE 568 – Assignment 2
1 Image Classification using CNNs – 6 points
In this question, we want to use convolutional neural networks (CNNs) to do image classification on the
MNIST dataset, a collection of 28 × 28 images of handwritten digits from 0 to 9. Our CNN takes one image
as input and predicts the digit it represents. I.e, each digit is a class. This question is mostly a tutorial on
neural networks in PyTorch, which is organized into five subsections.
1.1 Dataset and Dataloader – 0 points
PyTorch has an efficient and user-friendly API for loading and parsing data. Get familiar with the PyTorch
dataloader as it will be an inseparable part of your assignments and projects. For clarity, the dataloaders
return a python dictionary throughout the assignment.
1.2 Train, Validation, Test Split – 3 points
MNIST does not provide a validation set. split the training data into a train set and a validation set. You
can use the validation set for hyperparameter tuning. Use the test set only for reporting the final results.
1.3 Network – 0 points
The jupyter notebook file provides two sample networks, one using a pre-defined network called ResNet, and
one implemented from scratch. Try to use both of them and get familiar with neural networks in PyTorch.
The networks expect a python dictionary as input and return a dictionary as output, to be consistent with
other modules such as dataloaders and the training script.
1.4 Training – 0 points
We have provided you with the script for training the network, calculating the loss, backpropagating the
gradients, and transferring the data between CPU and GPU. Please read this part of the code thoroughly.
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Think about the loss function and compare it with other classification losses. Also, you can change hyperparameters like batch size and learning rate. Train the network for at least five epochs to see meaningful
changes in the loss, but avoid training for too long as you have limited time and GPU resources.
1.5 Evaluation – 3 points
Now we want to evaluate our trained network. We are only interested in the accuracy of the network on the
test set. But for comparison, report the accuracy of the model on train, validation, and test sets. Also, plot
the input images and output of your network for a few samples in the test set. You can use the function
plot images() provided for you in the Helper Functions section. Note that you should use your testing
data only for evaluation, not for training or hyperparameter tuning.
2 Anomaly Detection using CNNs – 12 points
In this question, given three images, we want to detect the image representing a different digit. The CNN
takes three different images from the MNIST dataset as inputs, where two of them have the same digit and
the other one has a different digit. The network predicts the index of the image with a different digit. For
example in Fig 1, the first image (from left to right) has a different digit. Therefore, the label of this sample
is 0, which is the index of the first image. Note that we do not have access to the digit of each image. We
only know the index of the different image. Also, in this question, we are considering only digits 0 to 4.
Figure 1: Sample input for question 2. The figure contains three images (from left to right): [(digit:2,
index:0), (digit:1, index:1), (digit:1, index:2)]. The label for this data is 0, which is the index of the image
with a different digit.
2.1 Dataset and Dataloader – 0 points
The dataloader returns three randomly chosen images and the index of the image with a different digit. It
is guaranteed that two and only two of the images represent the same digits. The batch size is set to 128,
but feel free to change it.
2.2 Train, Validation, Test Split – 1 point
Split the data into train, validation, and test sets. Print the number of samples in each dataset.
2.3 Network – 6 points
Implement your CNN from scratch. Do not use pre-defined networks. The network should take a
python dictionary as input and return a dictionary as output. More details in the jupyter notebook.
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2.4 Training – 2 points
The training script is provided to you similar to question 1. But you should define a suitable loss function
that matches the output of your network and helps the network to learn.
2.5 Evaluation – 3 points
Same as question 1, report the accuracy of the model and plot the input images and output of your network
for a few samples in the test set.
3 Sentiment Analysis using RNNs – 12 points
The IMDB dataset contains 50000 movie reviews. Each review is labeled as either positive (1) or negative
(0). We want to implement a recurrent neural network (RNN) that takes each review (series of words) as
input and predicts the sentiment behind it, i.e, whether it is a positive or a negative review.
3.1 Load and Tokenize Data – 0 points
We use a preprocessed version of the IMDB dataset done by Affandy Fahrizain, which is attached to your
assignment folder. We then assign a unique token (number) to each word, so we can apply mathematical
operations to them.
3.2 Dataloader – 0 points
The dataloader returns a python dictionary containing reviews and their labels. Also, the data is split into
the train, validation, and test sets.
3.3 Network – 6 points
Implement your network based on Long Short-Term Memory (LSTM) blocks. The network should take a
python dictionary as input and return a dictionary as output. More details in the jupyter notebook.
3.4 Training – 3 points
The training script is provided to you similar to question 1. But you should define a suitable loss function
that matches the output of your network and helps the network to learn. It usually takes more epochs for
RNNs than CNNs to converge. Train the model for at least 10 epochs.
3.5 Evaluation – 3 points
Report the accuracy of the model on the train, validation, and test sets. Also, print the input review and
output of your network for one positive example and one negative example in the test set. Choose qualitative
examples that show the effectiveness of your network.
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