focal.mlp_model module

Main module for defining MLP training logic.

class focal.mlp_model.BuildMLPModel(cnn_model_path: str, train_ds: Any, test_ds: Any, num_classes: int)

Bases: CustomModel

MLP model for tension prediction using features from pre-trained CNN.

This class builds regression models that use features extracted from a CNN to predict optimal tension values for fiber cleaving.

compile_model(dense1: int, dense2: int, dropout1: float, dropout2: float, dropout3: float, param_shape: Tuple[int, ...], learning_rate: float = 0.001, metrics: List[str] | None = None) → Model

Compile MLP model for regression.

Parameters:

• dense1 – Number of neurons in first FC layer.

• dense2 – Number of neurons in second FC layer.

• dropout1 – Amount of dropout for CNN output

• dropout2 – Amount of dropout for feature input.

• dropout3 – Amount of dropout for concatenated image+features

• param_shape – Dimensions of numerical parameters

• learning_rate – Learning rate for optimization

• metrics – List of metrics to monitor

Returns:

Compiled regression model

Return type:

tf.keras.Model

create_checkpoints(checkpoint_filepath: str = './checkpoints/mlp_model.keras', monitor: str = 'val_mae', mode: str = 'min', save_best_only: bool = True) → ModelCheckpoint

Create model checkpoints for MLP model.

Parameters:

• checkpoint_filepath – Path to save model checkpoints

• monitor – Metric to monitor during training

• mode – Method to determine stopping point (min for regression)

• save_best_only – Whether to save only the best model

Returns:

Checkpoint callback

Return type:

tf.keras.callbacks.ModelCheckpoint

create_early_stopping(patience: int = 3, mode: str = 'min', monitor: str = 'val_mae') → EarlyStopping

Create early stopping callback for regression model.

Parameters:

• patience – Number of epochs to wait before stopping

• mode – Method to track monitor (min for regression)

• monitor – Metric to monitor during training

Returns:

Early stopping callback

Return type:

tf.keras.callbacks.EarlyStopping

static get_averages_from_kfold(kfold_histories: List[History], scaler: any) → None

Calculate and display average metrics from k-fold cross validation for MLP.

Parameters:

• kfold_histories – List of training histories from k-fold training

• scaler – Scaler used for tension values (for denormalization)

static train_kfold_mlp(datasets: List[Tuple], cnn_model_path: str, param_shape: Tuple[int, ...], learning_rate: float, num_classes: int, dense1: int, dense2: int, dropout1: float, dropout2: float, dropout3: float, checkpoints: ModelCheckpoint | None = None, epochs: int = 5, initial_epoch: int = 0, early_stopping: EarlyStopping | None = None, history_file: str | None = None, save_model_file: str | None = None) → Tuple[List[Model], List[History]]

Train MLP model using k-fold cross validation.

Parameters:

• datasets – List of (train_ds, test_ds) tuples for each fold

• cnn_model_path – Path to the pre-trained CNN model

• param_shape – Dimensions of numerical parameters

• learning_rate – Learning rate for optimization

• checkpoints – Checkpoint callback

• epochs – Number of training epochs

• initial_epoch – Starting epoch number

• early_stopping – Early stopping callback

• history_file – Base filename for saving training history

• model_file – Base filename for saving models

Returns:

Tuple of (list of trained models, list of training histories)