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README.md |
README.md
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.