From fcef1dd33328b69f2d975a3ac57e9716dbdff144 Mon Sep 17 00:00:00 2001 From: AbhijithPai Date: Mon, 31 Mar 2025 20:04:38 +0530 Subject: [PATCH] Fixed Step Fusing link and corrected typos in Keras guide --- site/en/guide/keras.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/site/en/guide/keras.md b/site/en/guide/keras.md index 3dee7be3aa1..18205af41c8 100644 --- a/site/en/guide/keras.md +++ b/site/en/guide/keras.md @@ -77,14 +77,14 @@ The `tf.keras.Model` class features built-in training and evaluation methods: These methods give you access to the following built-in training features: -* [Callbacks](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks). +* [Callbacks](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks): You can leverage built-in callbacks for early stopping, model checkpointing, and [TensorBoard](https://www.tensorflow.org/tensorboard) monitoring. You can also [implement custom callbacks](https://www.tensorflow.org/guide/keras/writing_your_own_callbacks). -* [Distributed training](https://www.tensorflow.org/guide/keras/distributed_training). +* [Distributed training](https://www.tensorflow.org/guide/keras/distributed_training): You can easily scale up your training to multiple GPUs, TPUs, or devices. -* Step fusing. With the `steps_per_execution` argument in +* [Step fusing](https://www.tensorflow.org/api_docs/python/tf/keras/Model#compile): With the `steps_per_execution` argument in `tf.keras.Model.compile`, you can process multiple batches in a single `tf.function` call, which greatly improves device utilization on TPUs.