/ML-NeuralNet

An implementation of a neural network mini-library and a complete regression neural network.

Primary LanguagePython

Artificial Neural Networks Coursework

This repository contains:

  1. An implementation of a neural network mini-library.
  2. A complete regression neural network trained on the "1990 California Housing" dataset.

This coursework was part of COMP70050 - Introduction to Machine Learning (Autumn 2022), according to specification.

Usage Guide for DoC Lab Machines

Part 1 - Neural Network Mini-Library

In order to run part 1 of the coursework solution, invoke:

python3 part1_nn_lib.py

Running the above, performs a simple test on the mini-library.

In order to carry out further testing, a test script was written to ensure all layers of the mini-library functioned accordingly. This can be invoked by running:

python3 part_tests.py

Part 2 - Trained Regression Neural Network

In order to run the main entrypoint of part 2 of the coursework solution, invoke:

python3 part2_house_value_regression.py

This regression neural network is trained using training and validation sets. An evaluation using the test set is then performed.

The regressor is initialised with the optimum parameters, obtained through hyperparameter tuning.

Using GridSearchCV from scikit-learn, an exhaustive search over specified parameter values was performed on the model.

Flags

You will see these available flags:

Neural Networks and Evaluation Metrics

positional arguments:
  PATH_TO_DATASET       The relative path to the dataset
  OUTPUT_LABEL          The label for the output feature

optional arguments:
  -h, --help            show this help message and exit
  -cv, --validation     Use this flag to turn on cross validation
  -es, --early_stopping
                        Use this flag to enable early stopping
  -est EARLY_STOPPING_TOLERANCE, --early_stopping_tolerance EARLY_STOPPING_TOLERANCE
                        Tolerance before early stop kicks in
  -e EPOCHS, --epochs EPOCHS
                        The number of epochs to run the training for
  -lr LEARNING_RATE, --learning_rate LEARNING_RATE
                        The rate at which learning is completed
  -b BATCH_SIZE, --batch_size BATCH_SIZE
                        The size for each batch of mini-batched learning
  -d DROPOUT, --dropout DROPOUT
                        Provide a fraction of neurons to drop in training
  -n NEURONS [NEURONS ...], --neurons NEURONS [NEURONS ...]
                        Provide the hidden layers neurons
  -p, --plot_loss       Use this flag to plot the loss over time
  -x SEED, --seed SEED  Provide a seed for shuffling the dataset
  -s, --save            Save the resulting model to pickle file

Example Use

You can run:

python3 part2_house_value_regression.py housing.csv median_house_value -cv -es -est 200 -e 100 -lr 0.01 -b 100 -d 0.1  -n 5 5 -p -x 123 -s

Explanation:

  • housing.csv run the regression neural network on housing.csv dataset
  • median_house_value the label for output feature
  • -cv enable cross-validation
  • -es enable early stopping
  • -est 200 set early stopping tolerance to 200
  • -e 100 set epochs to 100
  • -lr 0.01 set learning rate to 0.01
  • -b 100 set batch-size to 100
  • -d 0.1 set dropout value to 0.1
  • -n 5 5 set neurons to [5, 5]
  • -p enable plotting of loss over time
  • -x 123 set random shuffling seed to 123
  • -s saves model as pickel file

Part 2 - Hyperparameter Tuning

A external script was written to fine tune and test hyperparameters:

python3 hyperparameter_tuning.py housing.csv median_house_value

Flags

You will see these available flags:

Hyperparameter Tuning

positional arguments:
  PATH_TO_DATASET       The relative path to the dataset
  OUTPUT_LABEL          The label for the output feature

optional arguments:
  -h, --help            show this help message and exit
  -es, --early_stopping
                        Use this flag to enable early stopping
  -est, EARLY_STOPPING_TOLERANCE, --early_stopping_tolerance EARLY_STOPPING_TOLERANCE
                        Tolerence before early stop kicks in
  -e EPOCHS, --epochs EPOCHS
                        The number of epochs to run the training for
  -lr LEARNING_RATE [LEARNING_RATE ...], --learning_rate LEARNING_RATE [LEARNING_RATE ...]
                        The rates at which learning is completed
  -b BATCH_SIZE [BATCH_SIZE ...], --batch_size BATCH_SIZE [BATCH_SIZE ...]
                        The sizes for each batch of mini-batched learning
  -d DROPOUT [DROPOUT ...], --dropout DROPOUT [DROPOUT ...]
                        Provide a list of fractions of neurons to drop in training
  -n NEURONS [NEURONS ...], --neurons NEURONS [NEURONS ...]
                        Provide the hidden layers neurons choices. Please separate choices with a , (e.g 5 5 : 10 10)
  -x SEED, --seed SEED  Provide a seed for shuffling the dataset
  -k FOLDS, --folds FOLDS
                        Provide the number of folds for cross validation
  -s, --save            Save the resulting model to pickle file

Example Use

You can run:

python3 hyperparameter_tuning.py housing.csv median_house_value -es -est 50 200 -e 10 -lr 0.01 0.1 -b 100 -d 0.1 0.9 -n 5 5 : 10 10

Explanation:

  • housing.csv run the regression neural network on housing.csv dataset
  • median_house_value the label for output feature
  • -es enable early stopping
  • -est set early stopping tolerance to [50, 200]
  • -e 10 set epochs to 10
  • -lr 0.01 0.1 set learning rate to [0.01, 0.1]
  • -b 100 set batch-size to 100
  • -d 0.1 set dropout value to 0.1
  • -n 5 5: 10 10 set neurons to [5, 5], [10, 10]

Extras

  • iris.dat was used to test the neural network mini-library.
  • housing.csv is a subdataset the "1990 California Housing" dataset, used to train the regression neural network.
  • part2_model.pickle was created using the regression neural network, trained on the entire dataset with fine-tuned hyperparameters.