Fixed Overfitting with Dropout Rate, Weight Decay, and on the fly data augmentation. #18
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Add Regularization and Data Augmentation
This PR adds regularization techniques and on-the-fly data augmentation to improve model training.
Changes
New Command-Line Arguments
--dropoutRate(default: 0.3) - Control dropout strength--weightDecay(default: 5e-4) - Control L2 regularization--seed(default: None) - Set random seed for reproducibility--require-gpu- Exit if GPU not availableDropout Improvements
UNetnow acceptsdropout_rateparameterdec1_dropout,dec2_dropout,dec3_dropout,dec4_dropout)bottleneck_dropout)Weight Decay
weight_decay=1e-5to configurableweight_decay=args.weightDecayOn-the-Fly Data Augmentation
augmentparameter toXPointPatchDatasetseedparameter toXPointPatchDatasetfor reproducible augmentation_apply_augmentation()method with:augment=True, validation usesaugment=FalseXPointDataset(..., rotateAndReflect=False)Reproducibility
set_seed()function sets seeds for Python, NumPy, and PyTorchGPU Checking
--require-gpuflag exits with error if CUDA unavailable