This assignment is due by 11.55 pm on November 1, 2020. For each additional day after Nov 1st until Nov 3rd, there will be a 10% reduction in marks. The submission portal on Moodle will close at 11.55 pm on Nov 3rd.

Please read the following important instructions before getting started on the assignment.

1. This assignment should be completed individually.
2. This assignment is entirely programming-based. A correponding Kaggle task is also hosted here. Please signup on Kaggle using your IITB LDAP email accounts, with Kaggle Display Name = <your_roll_number> . Instructions to join the Kaggle competition will be shared on Moodle.
3. Your final submission should be a .tar.gz bundle of a directory organized exactly as described in the Submission Instructions. Submissions that do not strictly adhere to this structure will be penalized.
4. Successful completion of the assignment would include: (A) Submitting .tar.gz on Moodle and (B) Having your roll number appear on the Kaggle leaderboard.

This assignment will familiarize you with training and evaluating feedforward neural networks. You will work on a regression task where you will have to predict the release year of a song from a set of timbre-based audio features extracted from the song. This consists of a year range between 1922 to 2011. (More details about this corpus are available here.) Click here to download the training, development and test sets from Kaggle.

• The dataset contains three files train.csv, dev.csv, and test.csv
• Each row in the __.csv file contains timbre-based audio features extracted from a song.
• The dataset has 90 features: 12 timbre average values and 78 timbre covariance values. Each column denotes a feature.
• train.csv and dev.csv contains following columns:

1. label - Year of release of the song in the range [1922, 2011]
2. TimbreAvg1
3. TimbreAvg2
.
.
13. TimbreAvg12
14. TimbreCovariance1
15. TimbreCovariance2
.
.
91. TimbreCovariance78

• test.csv contains same features except label

In Part 1, you will implement the neural network, train it using train data and report its performance on dev data.

Implement the functions definitions given in nn.py to create and train a neural network. Run stochastic gradient descent on Mean Squared Error (MSE) loss function.

For both Part 1.A and Part 1.B, use fixed values of following hyper-parameters:

- Number of epochs: 50
- Batch size for training: 128
- Seed for numpy: 42
- Use ReLU activation function for all HIDDEN layers.

Initialize Weights and Biases using uniform distribution in the range [-1, 1].

For Part 1.A, only code needs to be submitted.

Report Root Mean Squared Error (RMSE) on training and dev data using following hyper-parameter configurations.

Fill the table in part_1b.csv file given in this repository.

In Part 2, you will evaluate your network's performance on test data given in test.csv.

In this part, there is no restriction on any hyper-parameter values. You are also allowed to explore various hyper-parameter tuning and cross-validation techniques.

You are also free to create any wrapper functions over given functinos in nn.py

Submit your predictions on test data on Kaggle competition in a <roll_number>.csv file in the following format:

Id,Predictions
1.0,2000.0
2.0,2000.0
3.0,2000.0
.
.
10000.0,2000.0

In a CSV file, write the name of the hyper-parameter and the value you used.

Create a two-column csv file part_2.csv and write the name of hyper-parameter in first column and value in the second column.

For example:

You can explore following techniques to get better generalization performance

• Feature Scaling
• Feature Selection
• Dropout
• Batch Normalization
• Early Stopping

• Your submission should contain four files: (i) nn.py, (ii)part_1b.pdf, (iii)part_2.csv, and (iii)Readme.txt.

• Use Readme.txt to describe any other information needed to run your code successfully.

• Add these files to directory <your_roll_number>.

• Compress the directory <your_roll_number> in .tgz format using following command:

  tar -czf <your_roll_number>.tar.gz <your_roll_number>

• Submit the <your_roll_number>.tar.gz file.