Home▸Machine Learning & Neural Networks▸TensorFlow and Keras: From Static Graphs to Production-Grade Deep Learning

🧠 TensorFlow and Keras: From Static Graphs to Production-Grade Deep Learning

Interactive 3D model-deployment pipeline where building a simulated Keras layer stack, training it, and exporting it shows the resulting TensorFlow serving graph optimized for production inference.

Machine Learning & Neural Networks3DModerate60 FPS

Build a Keras-style layer stack, train it, then watch it export into an optimized TensorFlow serving graph where operations are fused, pruned and optionally quantized for fast production inference.

🔬 What It Demonstrates

The same network is rendered two ways: as loose, individually-dispatched Keras ops (eager mode) and as fused FusedMatMul+Bias+ReLU blocks in the exported TensorFlow serving graph, with lower latency and fewer nodes.

🎮 How to Use

Adjust hidden layers and neurons per layer, switch the deployment stage between Keras eager and TensorFlow serving graph, toggle INT8 quantization, and press train to stream data pulses through the network while loss drops.

💡 Did You Know?

TensorFlow's Grappler graph optimizer performs operator fusion, constant folding and layout optimization automatically on export — the same trained model can run several times faster in production than it did in the training loop.

⚙ Under the hood

Interactive 3D model-deployment pipeline where building a simulated Keras layer stack, training it, and exporting it shows the resulting TensorFlow serving graph optimized for production inference.

tensorflowkerasdeep-learningmodel-deploymentproduction-mlmachine-learning

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install