CNN Visualizer
Interactive Web Dashboard for Neural Network Feature Map & Weight Inspection
1. THE PROBLEM
Deep learning models are notoriously black boxes. Students and engineers struggle to visualize how convolutional layers extract low-level edges and high-level semantic features.
2. TECHNICAL APPROACH & DECISIONS
1Dynamic Intermediate Activation Extraction
Constructed Keras sub-models dynamically to output activation matrices for every intermediate Conv2D and MaxPooling layer.
Pre-rendering static activation plots
Dynamic extraction enables users to upload custom images and inspect real-time feature transformations across arbitrary neural network architectures.
3. TRADE-OFFS & HONEST REFLECTION
High-resolution feature map rendering consumes RAM on large images. Implemented spatial downsampling on feature maps > 512x512 to preserve browser performance.
4. CONCRETE OUTCOME & METRICS
Built an interactive model interpretability tool used by classmates to understand CNN feature hierarchies.