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Assignment 2 Visualizing a CNN with CIFAR10

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ELEC 576 / COMP 576
Assignment 2
Due: https://elec576.rice.edu/assignments via Canvas
Submission Instructions
Every student must submit their work in a zip file in the following format: netid-assignment2.zip.
You should also provide intermediate and final results as well as any necessary code. No
temporary files such as Tensorboard logs should be included in the zip.
1 Visualizing a CNN with CIFAR10
In this problem we will train a CNN on CIFAR10. After the network is trained, you will
visualize the weights of the first convolutional layer to see what filters it learned. In addition, we will see how the activations look like by using the test images.
a) CIFAR10 Dataset: CIFAR10 is a dataset composed of natural images that represent 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.
The dataset will be provided, and each image is grayscale and of size 28 x 28. Originally
CIFAR10’s images are RGB, and the original resolution is 32 x 32. We provided some
additional preprocessing.
In Assignment 1, for MNIST it was easy to call a function to import the dataset.
Here you will have perform this action. With trainCifarStarterCode.py and the zipped
folder, CIFAR10 , you will import all the images and perform additional preprocessing, such
as dividing by 255. You should use one-hot encoding for labels.
b) Train LeNet5 on CIFAR10: In this part you will implement a LeNet5 and train
it on CIFAR10. Here is the configuration of LeNet5:
• Convolutional layer with kernel 5 x 5 and 32 filter maps followed by ReLU
• Max Pooling layer subsampling by 2
ELEC 576 / COMP 576 – Fall 2022 – Assignment 2 2
• Convolutional layer with kernel 5 x 5 and 64 filter maps followed by ReLU
• Max Pooling layer subsampling by 2
• Fully Connected layer that has input 7*7*64 and output 1024
• Fully Connected layer that has input 1024 and output 10 (for the classes)
• Softmax layer (Softmax Regression + Softmax Nonlinearity)
Plot train/test accuracy and and train loss. In addition, try to search for good hyperparameters (e.g. training methods, learning rate, momentum values …). Hint: Run a few
epochs for each set of hyper-parameters and see how train/test accuracy and train loss
change.
c) Visualize the Trained Network: Visualize the first convolutional layer’s weights.
They should look like Gabor filters (edge detectors). Figure 1 shows examples of these
filters. Notice that since you train your networks using gray-scale images, your filters will
look slightly different. Also, show the statistics of the activations in the convolutional
layers on test images.
2 Visualizing and Understanding Convolutional Networks
Read the paper Visualizing and Understanding Convolutional Networks by Matthew D.
Zeiler and Rob Fergus. Summarize the key ideas of the paper.
(Optional) Visualization of features in a fully trained model: Apply one of the
techniques discussed in the Visualizing and Understanding Convolutional Networks paper
on the convnet trained in Problem 1. In the paper they used a validation data set, but in
your case you would use the test set. Show your curiosity here 🙂
3 Build and Train an RNN on MNIST
An RNN is well suited for tasks on time-series or sequential data. However, in this problem
we will see how we can use RNN for object recognition and compare it to Assignment 1
where we used a CNN.
a) Setup an RNN: With a CNN, we would send in full images, yet with the RNN,
inputs to the network are 28 pixel chunks of the images. For example, each row of the image
will be sent to the network at each iteration. The starter code, lstmMNISTStarterCode.py,
provided will detail how it splits the data so that it can work for the RNN. You should
modify the following parameters in the starter code.
ELEC 576 / COMP 576 – Fall 2022 – Assignment 2 3
Figure 1: Filters learned in the first convolutiuonal layer
• Number of nodes in the hidden layer
• Learning rate
• Number of iterations
• Cost (hint use softmax cross entropy with logits)
ELEC 576 / COMP 576 – Fall 2022 – Assignment 2 4
• Optimizer
b) How about using an LSTM or GRU: change line 35 in the starter code (see
below) to use LSTM and GRU instead of RNN.
rnn_cell.BasicRNNCell(nHidden)
Plot the train/test accuracies and the train loss. What do you notice and are the LSTM
or GRU better? Also, change the number of hidden units and see how that affects the loss
and accuracy.
c) Compare against the CNN: Compare with training using convnet in assignment
1 and describe any similarities or differences.
ELEC 576 / COMP 576 – Fall 2022 – Assignment 2 5

Assignment 2 Visualizing a CNN with CIFAR10
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