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Feed-Forward Neural Network (FFNN) for Regression Problems

Reference

Mathematical background: "Neural Networks and Deep Learning".
Datasets: UCI Machine Learning Repository.

Characteristics

The code has been written and tested in Python 3.7.7.
Usage: python test.py example.

Parameters

example Name of the example to run (wine, stock, wifi, pulsar.)

problem Defines the type of problem. Equal to C specifies logistic regression, anything else specifies linear regression. The default value is None.

Examples

There are four examples in test.py: wine, stock, wifi, pulsar. Since GDO is used, use_grad is set to True. For all examples init_weights is also set to True.

Single-label linear regression examples: wine

data_file = 'wine_dataset.csv'
n_features = 11
hidden_layers = [20]
split_factor = 0.7
L2 = 0.0
epochs = 50000
alpha = 0.2
d_alpha = 1.0
tolX = 1.e-7
tolF = 1.e-7

Original dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality.

The dataset has 11 features, 1 label, 4898 samples, 261 variables, and a layout of [11, 20, 1].

Correlation predicted/actual values: 0.708 (training), 0.601 (test).

Exit on epochs with tolX = 2.0e-4 and tolF = 1.1e-7.

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