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 |
|---|---|
 |
 |
| 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 |
|---|---|
 |
 |
| Predictions | Target |
|---|---|
 |
 |
| 5-fold Cross Validation | Confusion Matrix |
|---|---|
 |
 |
| Accuracies | Losses |
|---|---|
 |
 |
| 5-fold Cross Validation | Confusion Matrix |
|---|---|
 |
 |