Need help with larq?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

larq
429 Stars 54 Forks Apache License 2.0 567 Commits 14 Opened issues

Description

An Open-Source Library for Training Binarized Neural Networks

Services available

!
?

Need anything else?

Contributors list

logo

Codecov PyPI - Python Version PyPI PyPI - License DOI Code style: black

Larq is an open-source deep learning library for training neural networks with extremely low precision weights and activations, such as Binarized Neural Networks (BNNs).

Existing deep neural networks use 32 bits, 16 bits or 8 bits to encode each weight and activation, making them large, slow and power-hungry. This prohibits many applications in resource-constrained environments. Larq is the first step towards solving this. It is designed to provide an easy to use, composable way to train BNNs (1 bit) and other types of Quantized Neural Networks (QNNs) and is based on the

tf.keras
interface. Note that efficient inference using a trained BNN requires the use of an optimized inference engine; we provide these for several platforms in Larq Compute Engine.

Larq is part of a family of libraries for BNN development; you can also check out Larq Zoo for pretrained models and Larq Compute Engine for deployment on mobile and edge devices.

Getting Started

To build a QNN, Larq introduces the concept of quantized layers and quantizers. A quantizer defines the way of transforming a full precision input to a quantized output and the pseudo-gradient method used for the backwards pass. Each quantized layer requires an

input_quantizer
and a
kernel_quantizer
that describe the way of quantizing the incoming activations and weights of the layer respectively. If both
input_quantizer
and
kernel_quantizer
are
None
the layer is equivalent to a full precision layer.

You can define a simple binarized fully-connected Keras model using the Straight-Through Estimator the following way:

model = tf.keras.models.Sequential(
    [
        tf.keras.layers.Flatten(),
        larq.layers.QuantDense(
            512, kernel_quantizer="ste_sign", kernel_constraint="weight_clip"
        ),
        larq.layers.QuantDense(
            10,
            input_quantizer="ste_sign",
            kernel_quantizer="ste_sign",
            kernel_constraint="weight_clip",
            activation="softmax",
        ),
    ]
)

This layer can be used inside a Keras model or with a custom training loop.

Examples

Check out our examples on how to train a Binarized Neural Network in just a few lines of code:

Installation

Before installing Larq, please install:

  • Python version
    3.6
    ,
    3.7
    or
    3.8
  • Tensorflow version
    1.14
    ,
    1.15
    ,
    2.0
    ,
    2.1
    ,
    2.2
    ,
    2.3
    , or
    2.4
    :
    shell
    pip install tensorflow  # or tensorflow-gpu
    

You can install Larq with Python's pip package manager:

pip install larq

About

Larq is being developed by a team of deep learning researchers and engineers at Plumerai to help accelerate both our own research and the general adoption of Binarized Neural Networks.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.