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NEWS.md

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# keras3 (development version)
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## Added compatibility with Keras v3.7.0. User-facing changes:
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### New functions
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#### Activations
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- `activation_celu()`
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- `activation_glu()`
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- `activation_hard_shrink()`
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- `activation_hard_tanh()`
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- `activation_log_sigmoid()`
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- `activation_soft_shrink()`
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- `activation_squareplus()`
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- `activation_tanh_shrink()`
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#### Configuration
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- `config_disable_flash_attention()`
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- `config_enable_flash_attention()`
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- `config_is_flash_attention_enabled()`
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#### Layers and Initializers
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- `initializer_stft()`
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- `layer_max_num_bounding_boxes()`
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- `layer_stft_spectrogram()`
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#### Losses and Metrics
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- `loss_circle()`
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- `metric_concordance_correlation()`
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- `metric_pearson_correlation()`
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#### Operations
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- `op_celu()`
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- `op_exp2()`
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- `op_glu()`
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- `op_hard_shrink()`
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- `op_hard_tanh()`
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- `op_ifft2()`
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- `op_inner()`
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- `op_soft_shrink()`
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- `op_squareplus()`
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- `op_tanh_shrink()`
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#### New arguments
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* `callback_backup_and_restore()`: Added `double_checkpoint` argument to save a fallback checkpoint
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* `callback_tensorboard()`: Added support for `profile_batch` argument
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* `layer_group_query_attention()`: Added `flash_attention` and `seed` arguments
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* `layer_multi_head_attention()`: Added `flash_attention` argument
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* `metric_sparse_top_k_categorical_accuracy()`: Added `from_sorted_ids` argument
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### Performance improvements
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* Added native Flash Attention support for GPU (via cuDNN) and TPU (via Pallas kernel) in JAX backend
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* Added opt-in native Flash Attention support for GPU in PyTorch backend
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* Enabled additional kernel fusion via bias_add in TensorFlow backend
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* Added support for Intel XPU devices in PyTorch backend
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- `install_keras()` changes: if a GPU is available, the default is now to
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install a CPU build of TensorFlow and a GPU build of JAX. To use a GPU in the
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current session, call `use_backend("jax")`.

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