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Model Architecture Description
🧠 Dual CNN Architecture for Interpretable Music Genre Classification
Model 1: Batch Normalization CNN (bn_model)
Architecture Overview:
• Base Model: ResNet152V2 pre-trained on ImageNet
• Input Shape: (300, 300, 3) RGB images
• Transfer Learning: Feature extraction + fine-tuning approach
Layer Architecture:
1. Feature Extractor: ResNet152V2 (frozen initially, then fine-tuned from conv5_block3_preact_bn)
2. Global Pooling: GlobalMaxPooling2D for spatial dimension reduction
3. Dense Layer 1: 512 neurons + ReLU + L1 regularization (0.01)
4. Dropout 1: 30% dropout for regularization
5. Dense Layer 2: 256 neurons + ReLU + L1 regularization (0.01)
6. Dropout 2: 30% dropout for regularization
7. Dense Layer 3: 128 neurons + ReLU + L1 regularization (0.01)
8. Dropout 3: 30% dropout for regularization
9. Output Layer: 3 neurons + Softmax for multi-class classification
Model 2: Concept Whitening CNN (cw_model)
Architecture Overview:
• Base Model: ResNet152… -
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