What’s new in TensorFlow 2.11?

Posted by the Tensor Flow Team

is a TensorFlow API for distributed processing that allows models to seamlessly move from data parallelism to single program multiple data (SPMD) based model parallelism, including spatial partitioning. It gives you tools to easily train models where the model weights or inputs are so large they don’t fit on a single device. We’ve made several updates in TensorFlow v2.11.

Keras

The new Keras Optimizers API is ready

In , tf.keras.optimizers.experimental, to provide a more unified and expanded catalog of built-in optimizers which can be more easily customized and extended. In TensorFlow 2.11, we’re happy to share that the Optimizer migration is complete, and the new optimizers are on by default.

The old Keras Optimizers are available under tf.keras.optimizers.legacy. These will never be deleted, but they will not see any new feature additions. New optimizers will only be implemented based on for possible issues, and the . Group Normalization divides the channels into groups and computes within each group the mean and variance for normalization. Empirically, its accuracy can be more stable than batch norm in a wide range of small batch sizes, if learning rate is adjusted linearly with batch sizes. See the API doc for more details, and try it out!