The new keras isn't distributed via pip yet — until the official Keras 3 release. Either use the keras_core package from pip or build the new keras from source (it's a single command)
@fchollet
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Last Sound: AI Voice Announcing Technical Difficulties
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The last sound you hear will be a deadpan generated female voice saying "we are experiencing minor technical difficulties"
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Keras contribution history preserved after repository swap
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Likewise, if you were a contributor to keras-team/keras before the swap, your contribution history has been fully preserved: the new commits have been reapplied on top of the old git tree.
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Keras-core contributors’ commits reapplied preserving authorship records
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If you were a contributor to keras-team/keras-core, your commits have been reapplied in keras-team/keras under your own authorship, so that your contribution record is fully preserved. It's really important to us that we acknowledge all contributions from our community.
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Keras 3 Development Moves to Official Repository with New Name
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Announcement: The development of Keras 3 has moved to https://
github.com/keras-team/ker
as
… (from keras-team/keras-core previously) and the project has been renamed "Keras" (from "Keras Core" previously). Keras Core has become Keras. -
Keras: Two versions, a new multi-backend and a legacy TensorFlow
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There will be 2 Keras versions: – The new one, distributed as `keras`, fully multi-backend
– The legacy, TF-only one, distributed as `tf-keras` tf.keras will be the new one by default, but can be set to the old one if you need. -
Keras Multi-Backend Support in TensorFlow with Backward Compatibility
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In the future, importing Keras from TF (`tf.keras`) will give you multi-backend Keras. However we will provide a way to revert back to the legacy tf-only Keras (exact same setup as before), so as to enable folks to upgrade their TF version without worrying about minor breakages.
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PyDataset in Keras Core: New Sequence Class for Data Loading
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There's a new Sequence class in Keras Core, it's called PyDataset. It's basically the same functionality (in particular parallel loading), no major improvement. I would still recommend using http://
tf.data for anything performance critical. -
Keras Core Adds Sparse Tensor Support for Recommender Systems
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Keras Core now has support for sparse tensors (with the TF backend only for now — we'll add JAX next). This makes it possible to use Keras Core to build recommender systems.
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Symbolic Plugin Revealed as Code Interpreter Plugin
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Following up on this: surprise surprise, it was a symbolic plugin (a code interpreter plugin).