Keras lets you build a deep learning model as a readable stack of layers, run eagerly for fast iteration during research. Before that model can serve real traffic, TensorFlow compiles it into an optimized serving graph: consecutive operations (matrix multiply, bias add, activation) are fused into single kernels, dead branches are pruned, and weights can be quantized to lower precision — all to cut inference latency without materially harming accuracy.
FusedMatMul block, shrinking node count and latency.
TensorFlow's SavedModel export runs a graph-optimization pass
(Grappler) that performs operator fusion, constant folding and layout
optimization automatically — the same model can run several times faster
in production than it did in the eager Python loop it was trained in.
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.
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.
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.
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.