What’s new in TensorFlow 2.10?

Causal attention: All three layers now support a use_causal_mask argument to call (Attention and AdditiveAttention used to take a causal argument to __init__).

Implicit masking: Keras Attention, AdditiveAttention, and MultiHeadAttention layers now support to see if any API used in your workflow has changed. If you decide to keep using the old optimizer, please explicitly change your optimizer to corresponding tf.keras.optimizers.legacy.Optimizer.

You can also find more details about new Keras Optimizers in this .

For unseeded initializers (seed=None), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds). An unseeded initializer will raise a warning if it is reused (called) multiple times. This is because it would produce the same values each time, which may not be intended.

BackupAndRestore checkpoints with step level granularity

In the previous release, Tensorflow 2.9, the tf.keras.callbacks.BackupAndRestore Keras callback would backup the model and training state at epoch boundaries. In Tensorflow 2.10, the callback can also backup the model every N training steps. However, keep in mind that when BackupAndRestore is used with tf.distribute.MultiWorkerMirroredStrategy, the distributed dataset iterator state will be reinitialized and won't be restored when restoring the model. More information and code examples can be found in the , to easily generate audio classification datasets from directories of .wav files. Just sort your audio files into one different directory per file class, and a single line of code will get you a labeled tf.data.Dataset you can pass to a Keras model. You can find an example function is the swiss army knife of linear algebra. It can efficiently and explicitly describe a wide variety of operations. The (ACL) with TensorFlow through and the . To enable model training on DirectX 12-capable GPUs from vendors such as AMD, Intel, NVIDIA, and Qualcomm, install the plug-in alongside standard TensorFlow CPU packages on native Windows or currently supports a limited number of basic machine learning models, with a goal to increase model coverage in the future. You can view the open-source code and leave feedback at the ).

Sharing tf.data service with concurrent trainers

If you run multiple trainers concurrently using the same training data, it could save resources to cache the data in one tf.data service cluster and share the cluster with the trainers. For example, if you use . Once a job has consumed the data, the data remains in the cache and is re-used by jobs with different trainer_ids. Requests with the same trainer_id do not re-use data. For example:

 tf.data service uses a sliding-window cache to store shared data. When one trainer consumes data, the data remains in the cache. When other trainers need data, they can get data from the cache instead of repeating the expensive computation. The cache has a bounded size, so some workers may not read the full dataset. To ensure all the trainers get sufficient training data, we require the input dataset to be infinite. This can be achieved, for example, by repeating the dataset and performing random augmentation on the training instances.

TensorFlow Decision Forests 1.0

In conjunction with the release of Tensorflow 2.10, and established more comprehensive testing to make sure that TF-DF is ready for professional environments.

The new release of TF-DF also offers a first look at the APIs for inference of TF-DF models. While these APIs are still in beta, we are actively looking for feedback for them. TF-DF 1.0 . Oblique splits allow decision trees to express more complex patterns by conditioning on multiple features at the same time – for more information. To stay up to date, you can read the TensorFlow , or subscribe to . For feedback, please file an issue on . Thank you!