QKeras 0.9.0

Major Features

• qtools energy support for global_average_pooling layer.

• Added layers for sequence model, LSTM, RNN, GRU.

• Added activation and weight compression notebook.

• Added QSeparableConv2D class

  • Renamed previous QSeparableConv2D layer to QMobileNetSeparableConv2D
  • It is more consistent with Keras SeparableConv2D API

• Bugfix of QDepthwiseConv2D.

• Added an experimental QAdaptiveActivation layer to learn quantizer integer bits from activation values.

• Added weight sparsity calculation to model qstats.

• Enabled AutoQKeras to use custom Keras Tuners.

• Fixed various bugs in AutoQKeras.

Thanks to our contributors

This release contains contributions from many people at Google and CERN.

QKeras 0.8.0

Major Features

• Automatic quantization using QKeras;

• Stochastic behavior (including stochastic rounding) is disabled during inference;

• LeakyReLU for quantized_relu;

• Qtools for estimating effort to perform inference;

  • Qtools will estimate the sizes and types of operations to perform inference, with its data sizes compatible with high-level synthesis datatypes. For example, quantized_bits and quantized_relu bits and int_bits from Qtools will match exactly ac_fixed datatypes (if you rely on QKeras alone, the correct datatype should be ac_fixed<bits, int_bits+is_negative, is_negative>, where is_negative has to be inferred from the other parameters of the quantizer.

• Other bug fixes and enhancement.

Thanks to our contributors

This release contains contributions from many people at Google and CERN.

QKeras 0.7.4

Major Features

A patch with better weight initialization for https://github.com/google/qkeras/releases/tag/v0.7.0

QKeras 0.7.0

Major Features

• Enhancement of binary and ternary quantization as well as their stochastic counterparts for parameters and activation.
• Add auto scaling for low-bitwidth quantization.
• Add jupyter notebook.

Thanks to our Contributors

This release contains contributions from many people at Google.

QKeras 0.6.0

Major Features

• Use Tensorflow 2.1+ and tf.keras.
  • QKeras does not support the standalone Keras anymore.
  • Use Python 3.
• Support APIs of pruning and PrunableLayer from tensorflow_model_optimization for model sparsity.
• Add QBatchNormalization layer.

Thanks to our Contributors

This release contains contributions from many people at Google and CERN.

QKeras 0.5.0

QKeras 0.5.0 uses Tensorflow version < 2 and standalone Keras as backend.

Major Features

This is the first release of QKeras.

Notes

In the next release, we will support TensorFlow 2+ and tf.keras.

Thanks to our Contributors

This release contains contributions from many people at Google.