CS 725 (Autumn 2020): Predicting Year Release of songs

This is due by 11.55 pm on November 8, 2020. For each additional day after Nov 8th until Nov 10th, there will be a 10% reduction in marks. The submission portal on Moodle will close at 11.55 pm on Nov 10th.

Implement a Feedforward Neural Network using NumPy

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.

Dataset Information

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

Part 1

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

Part 1.A (25 Points)

Implement the functions definitions given in nn.py to create and train a neural network. Run mini-batch 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.

Initialization of Weights and Biases (for both Part 1 and Part 2)

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

What to submit in Part 1.A?

For Part 1.A, only code needs to be submitted in the file nn_1.py.

Part 1.B (15 Points)

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

Learning Rate	No. of hidden layers	Size of each hidden layer	λ(regulariser)
0.001	1	64	0
0.001	1	64	5
0.001	1	128	0
0.001	1	128	5
0.001	2	64	0
0.001	2	64	5
0.001	2	128	0
0.001	2	128	5
0.01	1	64	0
0.01	1	64	5
0.01	1	128	0
0.01	1	128	5
0.01	2	64	0
0.01	2	64	5
0.01	2	128	0
0.01	2	128	5

What to submit in Part 1.B?

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

Part 2 (10 points)

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.

For example:

Name	Value
`learning_rate`	0.003
`batch_size`	48
`dropout`	0.15

Tips to improve your rank on leaderboard

You can explore following techniques to get better generalization performance

mohit-iitb / Predicting-Release-Year-of-Songs