QKerasV3 Documentation

QKerasV3 is a Keras 3–compatible continuation of QKeras, focused on quantization-aware training and model compression.

Important

QKerasV3 supports TensorFlow, JAX and PyTorch as backend (default is TensorFlow). Set the backend before importing:

export KERAS_BACKEND=tensorflow/jax/torch

In the current version v1.2.x AutoQKeras and Pruning are not working. There will be an update in the the future to support this feature again.

QKerasV3 Layer Backend Support Matrix

The following matrix tracks multi-backend framework support for quantization-aware training (QAT) layers in qkerasV3.

Layer Support Matrix

Layer Name

TensorFlow

JAX

PyTorch

Implementation Notes & Constraints

QDense

✅

✅

✅

QConv1D

✅

✅

✅

QConv2D

✅

✅

✅

QDepthwiseConv2D

✅

✅

✅

QSeparableConv1D

✅

✅

✅

QSeparableConv2D

✅

✅

✅

QMobileNetSeparableConv2D

✅

✅

✅

MobileNet-specific; explicitly quantizes activation values immediately after the depthwise step. TODO: needs a test.

QConv2DTranspose

✅

✅

✅

QActivation

✅

✅

✅

QAdaptiveActivation

✅

✅

✅

QAveragePooling2D

✅

✅

⚠️

Combines AveragePooling2D with a QActivation layer. PyTorch lacks native asymmetric padding (padding="same") for all shapes.

QBatchNormalization / QConv2DBatchnorm

⚠️

⚠️

⚠️

Experimental Stage: Stochastic activation functions often offset its regularization needs. JAX/Torch rely on Keras 3 epoch variable updates.

QOctaveConv2D

✅

⚠️

⚠️

Multi-frequency feature extraction relies on complex tensor splitting and slicing across backends. TODO: needs a test.

QSimpleRNN / QSimpleRNNCell

✅

✅

✅

QLSTM / QLSTMCell

✅

✅

✅

QGRU / QGRUCell

✅

✅

✅

QBidirectional

✅

✅

✅

Legend:

  • ✅ Supported: Tested and functions smoothly natively across the backend via Keras 3.

  • ⚠️ Partial / Experimental / Conditional: Functions, but exhibits structural constraints, layout edge cases, or relies on features currently in testing.

QKerasV3 Activation Function Backend Support Matrix

The following matrix tracks multi-backend framework support for quantization activation functions in qkerasV3.

Activation Support Matrix

Activation Function

TensorFlow

JAX

PyTorch

Implementation Notes & Constraints

smooth_sigmoid(x)

✅

✅

✅

hard_sigmoid(x)

✅

✅

✅

binary_sigmoid(x)

✅

✅

✅

binary_tanh(x)

✅

✅

✅

smooth_tanh(x)

✅

✅

✅

hard_tanh(x)

✅

✅

✅

quantized_bits(bits=8, integer=0, symmetric=0, keep_negative=1)(x)

✅

✅

✅

bernoulli(alpha=1.0)(x)

✅

✅

✅

stochastic_ternary(alpha=1.0, threshold=0.33)(x)

✅

✅

✅

ternary(alpha=1.0, threshold=0.33)(x)

✅

✅

✅

stochastic_binary(alpha=1.0)(x)

✅

✅

✅

binary(alpha=1.0)(x)

✅

✅

✅

quantized_relu(bits=8, integer=0, use_sigmoid=0, negative_slope=0.0)(x)

✅

✅

✅

quantized_ulaw(bits=8, integer=0, symmetric=0, u=255.0)(x)

✅

✅

✅

quantized_tanh(bits=8, integer=0, symmetric=0)(x)

✅

✅

✅

quantized_po2(bits=8, max_value=-1)(x)

✅

✅

✅

quantized_relu_po2(bits=8, max_value=-1)(x)

✅

✅

✅

Legend:

  • ✅ Supported: Tested and functions smoothly natively across the backend via Keras 3.

  • ⚠️ Partial / Experimental / Conditional: Functions, but exhibits structural constraints, layout edge cases, or relies on features currently in testing.

Getting started

Guides

Reference

Unsupported Keras 3 Layers & Activations

  • ``MultiHeadAttention`` / ``GroupQueryAttention`` (Layer)

  • ``ConvLSTM1D`` / ``ConvLSTM2D`` / ``ConvLSTM3D`` (Layer)

  • ``LayerNormalization`` / ``GroupNormalization`` / ``RMSNormalization`` (Layer)

  • ``PReLU`` / ``ELU`` / ``LeakyReLU`` (Layer)

  • ``AlphaDropout`` / ``GaussianNoise`` / ``GaussianDropout`` (Layer)

  • ``mish(x)`` (Activation)

  • ``swish(x)`` / ``gelu(x)`` (Activation)

  • ``exponential(x)`` (Activation)

  • ``silu(x)`` (Activation)