PROJECT CASE STUDY // TECHNICAL NARRATIVE
CNN Visualizer
Interactive Web Dashboard for Neural Network Feature Map & Weight Inspection
YOUR ROLE & SCOPECreator & ML Engineer
TIMELINE3 Weeks
CORE CONSTRAINTSZero-latency visual rendering during layer-by-layer feature map inspection.
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1. THE PROBLEM
Deep learning models are black boxes. Students and engineers struggle to visualize how convolutional layers extract feature hierarchies.
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2. TECHNICAL APPROACH & DECISIONS
1Dynamic Activation Extraction
✓ DECISION CHOSEN
Constructed Keras sub-models dynamically to output activation matrices for every intermediate Conv2D layer.
✕ REJECTED ALTERNATIVE
Pre-rendered static activation plots
Dynamic extraction enables users to upload custom images and inspect real-time feature transformations across arbitrary neural network architectures.
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3. TRADE-OFFS & HONEST REFLECTION
High-resolution feature map rendering consumes RAM on large images; implemented spatial downsampling on feature maps > 512x512.
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4. CONCRETE OUTCOME & METRICS
Built an interactive model interpretability tool used by classmates to understand CNN feature hierarchies.
Real-Time Feature InspectionInteractivity
TensorFlow & KerasFramework
Explore Project Sources
Review live deployment or inspect codebase on GitHub.