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Releases: keras-team/keras

v3.14.0

03 Apr 01:45
@sachinprasadhs sachinprasadhs
v3.14.0
050671c
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v3.14.0 Latest
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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:

Full Changelog: v3.13.2...v3.14.0

Contributors

cpuguy96, dagecko, and 44 other contributors
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v3.13.2

30 Jan 01:03
@sachinprasadhs sachinprasadhs
v3.13.2
e29d0ef
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v3.13.2

Security Fixes & Hardening

This release introduces critical security hardening for model loading and saving, alongside improvements to the JAX backend metadata handling.

  • Disallow TFSMLayer deserialization in safe_mode (#22035)

    • Previously, TFSMLayer could load external TensorFlow SavedModels during deserialization without respecting Keras safe_mode. This could allow the execution of attacker-controlled graphs during model invocation.
    • TFSMLayer now enforces safe_mode by default. Deserialization via from_config() will raise a ValueError unless safe_mode=False is explicitly passed or keras.config.enable_unsafe_deserialization() is called.

  • Fix Denial of Service (DoS) in KerasFileEditor (#21880)

    • Introduces validation for HDF5 dataset metadata to prevent "shape bomb" attacks.
    • Hardens the .keras file editor against malicious metadata that could cause dimension overflows or unbounded memory allocation (unbounded numpy allocation of multi-gigabyte tensors).

  • Block External Links in HDF5 files (#22057)

    • Keras now explicitly disallows external links within HDF5 files during loading. This prevents potential security risks where a weight file could point to external system datasets.
    • Includes improved verification for H5 Groups and Datasets to ensure they are local and valid.

Backend-specific Improvements (JAX)

  • Set mutable=True by default in nnx_metadata (#22074)
    • Updated the JAX backend logic to ensure that variables are treated as mutable by default in nnx_metadata.
    • This makes Keras 3.13.2 compatible with Flax 0.12.3 when the Keras NNX integration is enabled.

Saving & Serialization

  • Improved H5IOStore Integrity (#22057)
    • Refactored H5IOStore and ShardedH5IOStore to remove unused, unverified methods.
    • Fixed key-ordering logic in sharded HDF5 stores to ensure consistent state loading across different environments.

Contributors

We would like to thank the following contributors for their security reports and code improvements:
@0xManan, @HyperPS, @hertschuh, and @divyashreepathihalli.

Full Changelog: v3.13.1...v3.13.2

Contributors

hertschuh, 0xManan, and 2 other contributors
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v3.12.1

30 Jan 18:36
@sachinprasadhs sachinprasadhs
v3.12.1
f704c88
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v3.12.1

Security Fixes & Hardening

This release introduces critical security hardening for model loading and saving, alongside improvements to the JAX backend metadata handling.

  • Disallow TFSMLayer deserialization in safe_mode (#22035)

    • Previously, TFSMLayer could load external TensorFlow SavedModels during deserialization without respecting Keras safe_mode. This could allow the execution of attacker-controlled graphs during model invocation.
    • TFSMLayer now enforces safe_mode by default. Deserialization via from_config() will raise a ValueError unless safe_mode=False is explicitly passed or keras.config.enable_unsafe_deserialization() is called.

  • Fix Denial of Service (DoS) in KerasFileEditor (#21880)

    • Introduces validation for HDF5 dataset metadata to prevent "shape bomb" attacks.
    • Hardens the .keras file editor against malicious metadata that could cause dimension overflows or unbounded memory allocation (unbounded numpy allocation of multi-gigabyte tensors).

  • Block External Links in HDF5 files (#22057)

    • Keras now explicitly disallows external links within HDF5 files during loading. This prevents potential security risks where a weight file could point to external system datasets.
    • Includes improved verification for H5 Groups and Datasets to ensure they are local and valid.

Saving & Serialization

  • Improved H5IOStore Integrity (#22057)
    • Refactored H5IOStore and ShardedH5IOStore to remove unused, unverified methods.
    • Fixed key-ordering logic in sharded HDF5 stores to ensure consistent state loading across different environments.

Acknowledgments

Special thanks to the security researchers and contributors who reported these vulnerabilities and helped implement the fixes: @0xManan, @HyperPS, and @hertschuh.

Full Changelog: v3.12.0...v3.12.1

Contributors

hertschuh, 0xManan, and HyperPS
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v3.13.1

14 Jan 19:04
@sachinprasadhs sachinprasadhs
v3.13.1
8914427
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v3.13.1

Bug Fixes & Improvements

  • General
    • Removed a persistent warning triggered during import keras when using NumPy 2.0 or higher. (#21949)
  • Backends
    • JAX: Fixed an issue where CUDNN flash attention was broken when using JAX versions greater than 0.6.2. (#21970)
  • Export & Serialization
    • Resolved a regression in the export pipeline that incorrectly forced batch sizes to be dynamic. The export process now correctly respects static batch sizes when defined. (#21944)

Full Changelog: v3.13.0...v3.13.1

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v3.13.0

18 Dec 00:06
@sachinprasadhs sachinprasadhs
v3.13.0
986ff97
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v3.13.0

BREAKING changes

Starting with version 3.13.0, Keras now requires Python 3.11 or higher. Please ensure your environment is updated to Python 3.11+ to install the latest version.

Highlights

LiteRT Export

You can now export Keras models directly to the LiteRT format (formerly TensorFlow Lite) for on-device inference.
This changes comes with improvements to input signature handling and export utility documentation. The changes ensure that LiteRT export is only available when TensorFlow is installed, update the export API and documentation, and enhance input signature inference for various model types.

Example:

import keras
import numpy as np

# 1. Define a simple model
model = keras.Sequential([
    keras.layers.Input(shape=(10,)),
    keras.layers.Dense(10, activation="relu"),
    keras.layers.Dense(1, activation="sigmoid")
])

# 2. Compile and train (optional, but recommended before export)
model.compile(optimizer="adam", loss="binary_crossentropy")
model.fit(np.random.rand(100, 10), np.random.randint(0, 2, 100), epochs=1)

# 3. Export the model to LiteRT format
model.export("my_model.tflite", format="litert")

print("Model exported successfully to 'my_model.tflite' using LiteRT format.")

GPTQ Quantization

  • Introduced keras.quantizers.QuantizationConfig API that allows for customizable weight and activation quantizers, providing greater flexibility in defining quantization schemes.

  • Introduced a new filters argument to the Model.quantize method, allowing users to specify which layers should be quantized using regex strings, lists of regex strings, or a callable function. This provides fine-grained control over the quantization process.

  • Refactored the GPTQ quantization process to remove heuristic-based model structure detection. Instead, the model's quantization structure can now be explicitly provided via GPTQConfig or by overriding a new Model.get_quantization_layer_structure method, enhancing flexibility and robustness for diverse model architectures.

  • Core layers such as Dense, EinsumDense, Embedding, and ReversibleEmbedding have been updated to accept and utilize the new QuantizationConfig object, enabling fine-grained control over their quantization behavior.

  • Added a new method get_quantization_layer_structure to the Model class, intended for model authors to define the topology required for structure-aware quantization modes like GPTQ.

  • Introduced a new utility function should_quantize_layer to centralize the logic for determining if a layer should be quantized based on the provided filters.

  • Enabled the serialization and deserialization of QuantizationConfig objects within Keras layers, allowing quantized models to be saved and loaded correctly.

  • Modified the AbsMaxQuantizer to allow specifying the quantization axis dynamically during the __call__ method, rather than strictly defining it at initialization.

Example:

  1. Default Quantization (Int8)
    Applies the default AbsMaxQuantizer to both weights and activations.
model.quantize("int8")
  1. Weight-Only Quantization (Int8)
    Disable activation quantization by setting the activation quantizer to None.
from keras.quantizers import Int8QuantizationConfig, AbsMaxQuantizer

config = Int8QuantizationConfig(
    weight_quantizer=AbsMaxQuantizer(axis=0),
    activation_quantizer=None 
)

model.quantize(config=config)
  1. Custom Quantization Parameters
    Customize the value range or other parameters for specific quantizers.
config = Int8QuantizationConfig(
    # Restrict range for symmetric quantization
    weight_quantizer=AbsMaxQuantizer(axis=0, value_range=(-127, 127)),
    activation_quantizer=AbsMaxQuantizer(axis=-1, value_range=(-127, 127))
)

model.quantize(config=config)

Adaptive Pooling layers

Added adaptive pooling operations keras.ops.nn.adaptive_average_pool and keras.ops.nn.adaptive_max_pool for 1D, 2D, and 3D inputs. These operations transform inputs of varying spatial dimensions into a fixed target shape defined by output_size by dynamically inferring the required kernel size and stride. Added corresponding layers:

  • keras.layers.AdaptiveAveragePooling1D
  • keras.layers.AdaptiveAveragePooling2D
  • keras.layers.AdaptiveAveragePooling3D
  • keras.layers.AdaptiveMaxPooling1D
  • keras.layers.AdaptiveMaxPooling2D
  • keras.layers.AdaptiveMaxPooling3D

New features

  • Add keras.ops.numpy.array_splitop a fundamental building block for tensor parallelism.
  • Add keras.ops.numpy.empty_like op.
  • Add keras.ops.numpy.ldexp op.
  • Add keras.ops.numpy.vander op which constructs a Vandermonde matrix from a 1-D input tensor.
  • Add keras.distribution.get_device_count utility function for distribution API.
  • keras.layers.JaxLayer and keras.layers.FlaxLayer now support the TensorFlow backend in addition to the JAX backed. This allows you to embed flax.linen.Module instances or JAX functions in your model. The TensorFlow support is based on jax2tf.

OpenVINO Backend Support:

  • Added numpy.digitize support.
  • Added numpy.diag support.
  • Added numpy.isin support.
  • Added numpy.vdot support.
  • Added numpy.floor_divide support.
  • Added numpy.roll support.
  • Added numpy.multi_hot support.
  • Added numpy.psnr support.
  • Added numpy.empty_like support.

Bug fixes and Improvements

  • NNX Support: Improved compatibility and fixed tests for the NNX library (JAX), ensuring better stability for NNX-based Keras models.
  • MultiHeadAttention: Fixed negative index handling in attention_axes for MultiHeadAttention layers.
  • Softmax: The update on Softmax mask handling, aimed at improving numerical robustness, was based on a deep investigation led by Jaswanth Sreeram, who prototyped the solution with contributions from others.
  • PyDataset Support: The Normalization layer's adapt method now supports PyDataset objects, allowing for proper adaptation when using this data type.

TPU Test setup

Configured the TPU testing infrastructure to enforce unit test coverage across the entire codebase. This ensures that both existing logic and all future contributions are validated for functionality and correctness within the TPU environment.

New Contributors

Full Changelog: v3.12.0...v3.13.0

Contributors

mattjj, erahulkulkarni, and 13 other contributors
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Keras 3.12.0

27 Oct 20:22
@fchollet fchollet
v3.12.0
adbfd13
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Keras 3.12.0

Highlights

Keras has a new model distillation API!

You now have access to an easy-to-use API for distilling large models into small models while minimizing performance drop on a reference dataset -- compatible with all existing Keras models. You can specify a range of different distillation losses, or create your own losses. The API supports multiple concurrent distillation losses at the same time.

Example:

# Load a model to distill
teacher = ...
# This is the model we want to distill it into
student = ...

# Configure the process
distiller = Distiller(
    teacher=teacher,
    student=student,
    distillation_losses=LogitsDistillation(temperature=3.0),
)
distiller.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

# Train the distilled model
distiller.fit(x_train, y_train, epochs=10)

Keras supports GPTQ quantization!

GPTQ is now built into the Keras API. GPTQ is a post-training, weights-only quantization method that compresses a model to int4 layer by layer. For each layer, it uses a second-order method to update weights while minimizing the error on a calibration dataset.

Learn how to use it in this guide.

Example:

model = keras_hub.models.Gemma3CausalLM.from_preset("gemma3_1b")
gptq_config = keras.quantizers.GPTQConfig(
    dataset=calibration_dataset,
    tokenizer=model.preprocessor.tokenizer,
    weight_bits=4,
    group_size=128,
    num_samples=256,
    sequence_length=256,
    hessian_damping=0.01,
    symmetric=False,
    activation_order=False,
)
model.quantize("gptq", config=gptq_config)
outputs = model.generate(prompt, max_length=30)

Better support for Grain datasets!

  • Add Grain support to keras.utils.image_dataset_from_directory and keras.utils.text_dataset_from_directory. Specify format="grain" to return a Grain dataset instead of a TF dataset.
  • Make almost all Keras preprocessing layers compatible with Grain datasets.

New features

  • Add keras.layers.ReversibleEmbedding layer: an embedding layer that can also also project backwards to the input space. Use it with the reverse argument in call().
  • Add argument opset_version in model.export(). Argument specific to format="onnx"; specifies the ONNX opset version.
  • Add keras.ops.isin op.
  • Add keras.ops.isneginf, keras.ops.isposinf ops.
  • Add keras.ops.isreal op.
  • Add keras.ops.cholesky_inverse op and add upper argument in keras.ops.cholesky.
  • Add keras.ops.image.scale_and_translate op.
  • Add keras.ops.hypot op.
  • Add keras.ops.gcd op.
  • Add keras.ops.kron op.
  • Add keras.ops.logaddexp2 op.
  • Add keras.ops.view op.
  • Add keras.ops.unfold op.
  • Add keras.ops.jvp op.
  • Add keras.ops.trapezoid op.
  • Add support for over 20 news ops with the OpenVINO backend.

Breaking changes

  • Layers StringLookup & IntegerLookup now save vocabulary loaded from file. Previously, when instantiating these layers from a vocabulary filepath, only the filepath would be saved when saving the layer. Now, the entire vocabulary is materialized and saved as part of the .keras archive.

Security fixes

New Contributors

Full Changelog: v3.11.0...v3.12.0

Contributors

WIgor, SamKnightGit, and 14 other contributors
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Keras 3.11.3

22 Aug 17:48
@rtg0795 rtg0795
v3.11.3
b491c86
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Keras 3.11.3

What's Changed

Full Changelog: v3.11.2...v3.11.3

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rtg0795
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Keras 3.11.2

11 Aug 21:12
@laxmareddyp laxmareddyp
v3.11.2
251ac34
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Keras 3.11.2

What's Changed

New Contributors

Full Changelog: v3.11.1...v3.11.2

Contributors

laxmareddyp
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Keras 3.11.1

31 Jul 22:02
@rtg0795 rtg0795
v3.11.1
0e11071
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Keras 3.11.1

What's Changed

Full Changelog: v3.11.0...v3.11.1

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rtg0795
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Keras 3.11.0

29 Jul 23:59
@fchollet fchollet
v3.11.0
7bf852c
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Keras 3.11.0

What's Changed

  • Add int4 quantization support.
  • Support Grain data loaders in fit()/evaluate()/predict().
  • Add keras.ops.kaiser function.
  • Add keras.ops.hanning function.
  • Add keras.ops.cbrt function.
  • Add keras.ops.deg2rad function.
  • Add keras.ops.layer_normalization function to leverage backend-specific performance optimizations.
  • Various bug fixes and performance optimizations.

Backend-specific changes

JAX backend

  • Support NNX library. It is now possible to use Keras layers and models as NNX modules.
  • Support shape -1 for slice op.

TensorFlow backend

  • Add support for multiple dynamic dimensions in Flatten layer.

OpenVINO backend

  • Add support for over 30 new backend ops.

New Contributors

Full Changelog: v3.10.0...v3.11.0

Contributors

iazzi, timovdk, and 6 other contributors
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