Boost Keras Model Training Speed with Mixed Precision in TensorFlow

This guide walks you through using mixed precision in Keras with TensorFlow to accelerate model training while reducing memory usage. By combining float16 or bfloat16 with float32 for key computations, you can achieve up to 3x faster performance on modern GPUs, TPUs, and Intel CPUs without sacrificing accuracy. The article covers hardware requirements, setting dtype policies, ensuring numerical stability, implementing loss scaling, and optimizing for GPU Tensor Cores—complete with code examples for both Model.fit and custom training loops.