This is a Neural Network written in Python by using the NumPy library.
The model implements backpropagation and Stochastic Gradient Descent (SGD) to learn simple datasets.
Important dependencies are NumPy, Scikit-Learn, Pandas and Matplotlib. The exact versions used in this repo are found in requirements.txt, however, this is an auto-generated file from conda on platform osx-arm64 (Mac M1).
The screenshots below are the results from a neural network with 64 nodes in one hidden layer.
| Accuracies |
Losses |
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| Predictions |
Target |
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| 5-fold Cross Validation |
Confusion Matrix |
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| Accuracies |
Losses |
 |
 |
| Predictions |
Target |
 |
 |
| 5-fold Cross Validation |
Confusion Matrix |
 |
 |
| Accuracies |
Losses |
 |
 |
| Predictions |
Target |
 |
 |
| 5-fold Cross Validation |
Confusion Matrix |
 |
 |
| Accuracies |
Losses |
 |
 |
| Predictions |
Target |
 |
 |
| 5-fold Cross Validation |
Confusion Matrix |
 |
 |
| Accuracies |
Losses |
 |
 |
| 5-fold Cross Validation |
Confusion Matrix |
 |
 |
