This assignment is due on Monday, May 02 2022 at 11:59pm PST.
Starter code containing Colab notebooks can be downloaded here.
- Q1: Multi-Layer Fully Connected Neural Networks
- Q2: Batch Normalization
- Q3: Dropout
- Q4: Convolutional Neural Networks
- Q5: PyTorch on CIFAR-10
- Q6: Network Visualization: Saliency Maps, Class Visualization, and Fooling Images
- Submitting your work
Please familiarize yourself with the recommended workflow before starting the assignment. You should also watch the Colab walkthrough tutorial below.
Note. Ensure you are periodically saving your notebook (
File -> Save) so that you don’t lose your progress if you step away from the assignment and the Colab VM disconnects.
While we don’t officially support local development, we’ve added a requirements.txt file that you can use to setup a virtual env.
Once you have completed all Colab notebooks except
collect_submission.ipynb, proceed to the submission instructions.
In this assignment you will practice writing backpropagation code, and training Neural Networks and Convolutional Neural Networks. The goals of this assignment are as follows:
- Understand Neural Networks and how they are arranged in layered architectures.
- Understand and be able to implement (vectorized) backpropagation.
- Implement various update rules used to optimize Neural Networks.
- Implement Batch Normalization and Layer Normalization for training deep networks.
- Implement Dropout to regularize networks.
- Understand the architecture of Convolutional Neural Networks and get practice with training them.
- Gain experience with a major deep learning framework, such as TensorFlow or PyTorch.
- Explore various applications of image gradients, including saliency maps, fooling images, class visualizations.
Q1: Multi-Layer Fully Connected Neural Networks
FullyConnectedNets.ipynb will have you implement fully connected
networks of arbitrary depth. To optimize these models you will implement several
popular update rules.
Q2: Batch Normalization
BatchNormalization.ipynb you will implement batch normalization, and use it to train deep fully connected networks.
Dropout.ipynb will help you implement dropout and explore its effects on model generalization.
Q4: Convolutional Neural Networks
In the notebook
ConvolutionalNetworks.ipynb you will implement several new layers that are commonly used in convolutional networks.
Q5: PyTorch on CIFAR-10
For this part, you will be working with PyTorch, a popular and powerful deep learning framework.
PyTorch.ipynb. There, you will learn how the framework works, culminating in training a convolutional network of your own design on CIFAR-10 to get the best performance you can.
Q6: Network Visualization: Saliency Maps, Class Visualization, and Fooling Images
Network_Visualization.ipynb will introduce the pretrained SqueezeNet model, compute gradients with respect to images, and use them to produce saliency maps and fooling images.
Submitting your work
Important. Please make sure that the submitted notebooks have been run and the cell outputs are visible.
Once you have completed all notebooks and filled out the necessary code, you need to follow the below instructions to submit your work:
collect_submission.ipynb in Colab and execute the notebook cells.
This notebook/script will:
- Generate a zip file of your code (
- Convert all notebooks into a single PDF file.
If your submission for this step was successful, you should see the following display message:
### Done! Please submit a2_code_submission.zip and a2_inline_submission.pdf to Gradescope. ###
2. Submit the PDF and the zip file to Gradescope.
Remember to download
a2_inline_submission.pdf locally before submitting to Gradescope.