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Developer tools · #keras
Learn how to train a CIFAR‑10 CNN with a custom Keras training loop, tf.GradientTape for gradients, manual metric logging, and best‑weight checkpointing.
Learn how to embed data preprocessing directly into a Keras model using preprocessing layers, ensuring consistent transforms at training and serving time.
Learn how to choose between tf.data.Dataset and Keras Sequence to eliminate data starvation and maximize GPU utilization in TensorFlow training pipelines.
Keras mixed precision is a one-line change that can meaningfully speed up GPU training — but only with the right hardware, a float32 output layer, and loss scaling. Here's how to enable it and verify the win.
Enable mixed_float16 in Keras, keep the output layer in float32, handle loss scaling correctly for custom loops, and verify against a float32 baseline with clear recovery options.
Stop wasting compute and prevent overfitting in Keras. Learn how to use the EarlyStopping callback to automatically terminate training at the peak of model performance.
Developers need a way to run tf.keras.Model.predict concurrently from multiple threads without wrapping each call in an external lock, while keeping latency predictable under load. The current documentation notes that predict is not thread‑safe and recommends manual locking or duplicate models, but it does not specify whether a built‑in thread‑safe mode coul
tensorflow