# Keras v3.14.0

- Product: Keras (https://whatsnew.fyi/product/keras)
- Vendor: Keras
- Date: 2026-04-03
- Version: v3.14.0
- Original notes: https://github.com/keras-team/keras/releases/tag/v3.14.0
- Permalink: https://whatsnew.fyi/product/keras/releases/v3.14.0

What's New is an index, not a publisher: every entry below links to the vendor's own release notes, which are the authoritative source. Entries are labelled where they are hand-curated sample data, pre-releases, or drawn from a secondary source such as a developer blog.

Reuse: the summaries, labels and curation here are © What's New. Quote freely with attribution and a link back; wholesale republication of the corpus is not permitted — terms: https://whatsnew.fyi/terms. The vendors' own release notes remain their publishers'.

---

- **added** — Added full support for Orbax checkpoints, including sharding, remote paths, and step recovery
- **added** — Added support for Activation-aware Weight Quantization (AWQ) and Asymmetric INT4 Sub-Channel Quantization
- **added** — Added batch renormalization feature to the BatchRenormalization layer
- **added** — Added ScheduleFreeAdamW optimizer
- **added** — Introduced optional Gated Attention support in MultiHeadAttention and GroupedQueryAttention layers
- **added** — Added NaN-aware NumPy operations: nanmin, nanmax, nanmean, nanmedian, nanvar, nanstd, nanprod, nanargmin, nanargmax, and nanquantile in keras.ops.numpy
- **added** — Added math and linear algebra operators: nextafter, ptp, view, sinc, fmod, i0, fliplr, flipud, rad2deg, geomspace, depth_to_space, space_to_depth, and fold
- **added** — Added Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing layer
- **added** — Preprocessing layers now support Python iterables in the adapt() method, allowing direct use of Grain datasets
- **added** — OpenVINO backend now supports NumPy operations: vander, trapezoid, corrcoef, correlate, flip, diagonal, cbrt, hypot, trace, kron, argpartition, logaddexp2, ldexp, select, round, vstack, hsplit, vsplit, tile, nansum, tensordot, exp2, trunc, gcd, unravel_index, inner, cumprod, searchsorted, hanning, diagflat, norm, histogram, lcm, allclose, real, imag, isreal, kaiser, shuffle, einsum, quantile, conj, randint, in_top_k, signbit, gamma, heaviside, var, std, inv, solve, cholesky_inverse, fft, fft2, ifft2, rfft, irfft, stft, istft, scatter, binomial, unfold, and QR decomposition
- **added** — OpenVINO backend now supports neural network operations: separable_conv, conv_transpose, adaptive_average_pool, adaptive_max_pool, RNN, LSTM, and GRU
- **added** — OpenVINO backend now supports control flow operations: cond, scan, associative_scan, map, switch, fori_loop, and vectorized_map
- **changed** — PyTorch backend now supports dynamic shapes in export and includes device selection improvements
- **changed** — JAX backend improved RNG handling in FlaxLayer and JaxLayer, variable jitting improvements, and direct JAX-to-ONNX export
- **changed** — NumPy backend now supports masking
- **fixed** — Fixed multiple symbolic shape bugs across layers like Conv1DTranspose, IndexLookup, and TextVectorization
- **fixed** — Fixed activity regularizer normalization by batch size
- **fixed** — Fixed CuDNN-based LSTM and GRU implementation in PyTorch backend
- **fixed** — Improved Sequential error messages for incompatible layers
- **fixed** — Minimized memory usage issues in sparse_categorical_crossentropy

##### Highlights

- **Orbax Checkpoint Integration**: Full support for Orbax checkpoints, including sharding, remote paths, and step recovery.
- **Quantization Upgrades**: Added support for Activation-aware Weight Quantization (AWQ) and Asymmetric INT4 Sub-Channel Quantization.
- **Batch Renormalization in BatchNorm**: Added batch renormalization feature to the `BatchRenormalization` layer.
- **New Optimizer**: Added `ScheduleFreeAdamW` optimizer.
- **Gated Attention**: Introduced optional Gated Attention support in `MultiHeadAttention` and `GroupedQueryAttention` layers.

---

##### New Features and Operations

###### Multi-Backend Operations
- **NaN-aware NumPy Operations**: Added support for `nanmin`, `nanmax`, `nanmean`, `nanmedian`, `nanvar`, `nanstd`, `nanprod`, `nanargmin`, `nanargmax`, and `nanquantile` in `keras.ops.numpy`.
- **New Math & Linear Algebra Operators**: Added `nextafter`, `ptp`, `view`, `sinc`, `fmod`, `i0`, `fliplr`, `flipud`, `rad2deg`, `geomspace`, `depth_to_space`, `space_to_depth`, and `fold`.

###### Preprocessing and Layers
- **CLAHE Layer**: Added Contrast Limited Adaptive Histogram Equalization preprocessing layer.
- **Adapt Support for Iterables**: Preprocessing layers now support Python iterables in the `adapt()` method, which allows the direct use of Grain datasets.

---

##### OpenVINO Backend Support

The OpenVINO backend received a massive update, implementing a wide array of NumPy and Neural Network operations to achieve feature parity with other backends:

- **NumPy Operations**: `vander`, `trapezoid`, `corrcoef`, `correlate`, `flip`, `diagonal`, `cbrt`, `hypot`, `trace`, `kron`, `argpartition`, `logaddexp2`, `ldexp`, `select`, `round`, `vstack`, `hsplit`, `vsplit`, `tile`, `nansum`, `tensordot`, `exp2`, `trunc`, `gcd`, `unravel_index`, `inner`, `cumprod`, `searchsorted`, `hanning`, `diagflat`, `norm`, `histogram`, `lcm`, `allclose`, `real`, `imag`, `isreal`, `kaiser`, `shuffle`, `einsum`, `quantile`, `conj`, `randint`, `in_top_k`, `signbit`, `gamma`, `heaviside`, `var`, `std`, `inv`, `solve`, `cholesky_inverse`, `fft`, `fft2`, `ifft2`, `rfft`, `irfft`, `stft`, `istft`, `scatter`, `binomial`, `unfold`, `QR decomposition`, `view`, and more.
- **Neural Network Operations**: Added support for `separable_conv`, `conv_transpose`, `adaptive_average_pool`, `adaptive_max_pool`, `RNN`, `LSTM`, and `GRU`.
- **Control Flow Operations**: Implemented `cond`, `scan`, `associative_scan`, `map`, `switch`, `fori_loop`, and `vectorized_map`.

---

##### Bug Fixes and Improvements

###### Backend Specific Improvements
- **PyTorch**: Dynamic shapes support in export, device selection improvements, and bug fixes to the CuDNN based LSTM and GRU implementation.
- **JAX**: Improved RNG handling in `FlaxLayer` and `JaxLayer`, variable jitting improvements, and direct JAX-to-ONNX export.
- **NumPy**: Enabled masking support for the NumPy backend.

###### Other Improvements
- Fixed multiple symbolic shape bugs across layers like `Conv1DTranspose`, `IndexLookup`, and `TextVectorization`.
- Fixed activity regularizer normalization by batch size.
- Improved `Sequential` error messages for incompatible layers.
- Minimized memory usage issues in `sparse_categorical_crossentropy`.

---

##### New Contributors
We would like to thank our new contributors for making their first contribution to the Keras project:
* @vaidik-gupta made their first contribution in https://github.com/keras-team/keras/pull/21939
* @HyperPS made their first contribution in https://github.com/keras-team/keras/pull/21880
* @calad0i made their first contribution in https://github.com/keras-team/keras/pull/21959
* @KarSri7694 made their first contribution in https://github.com/keras-team/keras/pull/21963
* @MarcosAsh made their first contribution in https://github.com/keras-team/keras/pull/21961
* @orbin123 made their first contribution in https://github.com/keras-team/keras/pull/21935
* @ayulocke

_[Truncated at 4000 characters — full notes: https://github.com/keras-team/keras/releases/tag/v3.14.0]_