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Keras Layer Stack to TensorFlow Serving Graph (2D)

2D companion lab: build a Keras-style layer stack, train it, then watch it export into a fused, optionally INT8-quantized TensorFlow serving graph — latency, node count and parameter count update live.

Machine Learning & Neural Networks2DModerate60 FPS📱 Mobile-adapted⇄ 3D version

This 2D companion drives the same deployment-pipeline model as the 3D version through a plain node-and-edge diagram: a side panel exposes hidden-layer depth and width, a toggle switches the graph between Keras eager mode — where every neuron and weight is its own node — and the fused TensorFlow serving graph, an INT8 quantization switch shrinks the fused blocks further, and a live readout tracks inference latency, node count, parameter count and a streaming training-loss curve while forward-pass pulses travel through whichever graph is currently selected.

⚙ Under the hood

2D layer-stack lab where depth and width sliders drive a Keras eager vs. fused TensorFlow serving-graph comparison, with INT8 quantization and a live latency / parameter-count / training-loss readout.

tensorflowkerasdeep-learningmodel-deploymentproduction-mlmachine-learning

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install