Define model 1 CV and Monitoring strategy

[RaymondBrien]

Training options to consider:

• batch_size: try [16, 32, 64, 128]
• learning_rate scheduling: reduce_lr_on_plateau or cosine decay
• early stopping patience: try [5, 10, 15] epochs
• validation_split: try [0.1, 0.2, 0.3]
• class_weights for imbalanced dataset

Example usage with different input sizes:

Smaller input: model = create_powdery_mildew_classifier(input_shape=(160, 160, 3))

Larger input: model = create_powdery_mildew_classifier(input_shape=(299, 299, 3))

Systematic Tuning Approach:

Start with architecture modifications (layer sizes, depth) Then tune regularization parameters (dropout, batch norm) Finally adjust optimization parameters (learning rate, batch size)

Cross-Validation Strategy: pythonCopyfrom sklearn.model_selection import KFold k_fold = KFold(n_splits=5, shuffle=True)

Use this to validate hyperparameter choices

Monitoring Tips:

Watch validation loss for overfitting Monitor precision/recall trade-offs Use TensorBoard for visualization of metrics