Table 2 Chosen hyperparameters with brief description. Hyperparameter sweep went through a realizable range for each hyperparameter and individual values were chosen to optimize training ability or to minimize overfitting, depending on the parameter
Hyperparameter (Range) | Hyperparameter Type | Interpretation | Chosen Value |
|---|---|---|---|
Rotation (0–90) | Data Augmentation | Range for a random rotation | 20 |
Zoom (0–1) | Data Augmentation | Range for a random zoom | 0.5 |
Shear (0–1) | Data Augmentation | Range for a random shear | 0.3 |
Vertical/Horizontal Flip (Yes/No) | Data Augmentation | Random chance of flip in respective direction | Yes/Yes |
Momentum (0–1) | Optimizer Parameter | Accelerates or dampens oscillations in given direction. | 0.3 |
Regularization (0–1) | Optimizer Parameter | Penalty applied to large image weights | 0 |
Decay (0–1) | Optimizer Parameter | Learning Rate decay over each update. | 1e-5 |
Dropout (0–1) | Fully-connected Layer | Percent of weights dropped out between dense layers in the FC layer. | 0.95 |
Learning Rate (0–1) | Training Parameter | Importance attributed to weight updates. | 1e-3 |
Epochs (Integer) | Training Parameter | Number of epochs performed | 1000 |
Batch Size (2n any n) | Training Parameter | Number of samples per gradient update | 16 |
Image Size (Minimum 224) | Training Parameter | Input image size in pixels | 224 |
nLayersRetrain (Fully Connected only – All Layers) | Training Parameter | Number of layers allowed to have their weights altered. | All Layers (173) |