Bump keras from 3.12.0 to 3.13.0 in /demos

[dependabot]

Bumps keras from 3.12.0 to 3.13.0.

Release notes

Sourced from keras's releases.

v3.13.0

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")
</tr></table> 

... (truncated)

Commits

• 986ff97 Update release version and comment orbax checkpoint (#21934)
• ca23fce Refactors AbsMaxQuantizer to accept axis in call (#21931)
• 1a9893f Adds Serialization Support for QuantizationConfig based quantized models (#21...
• 86bfab4 More OpenVINO Numpy Operations (#21925)
• f48f480 Add adaptive pooling (1D, 2D, 3D) support across JAX, NumPy, TensorFlow, and ...
• 0771c80 Fix ops.tile shape inference issue on TensorFlow backend (#21860)
• 024c96d Extended fix OOM Issue #21634 on Keras side (#21755)
• 71f4997 Introduces QuantizationConfig for fine-grained quantization control (#21896)
• 3989d64 Fix fake quant gradient output shape and use jax.grad for tests. (#21927)
• 4870b9f OpenVino device_scope and data adapters tests (#21922)
• Additional commits viewable in compare view

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