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"Topic :: Scientific/Engineering",
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\n\n[](https://github.com/larq/larq/actions?workflow=Unittest) [](https://codecov.io/github/larq/larq?branch=master) [](https://pypi.org/project/larq/) [](https://pypi.org/project/larq/) [](https://github.com/larq/larq/blob/master/LICENSE) [](https://github.com/ambv/black) [](https://mybinder.org/v2/gh/larq/larq/master?filepath=docs%2Fexamples) [](https://spectrum.chat/larq)\n\nLarq is an open-source deep learning library for training neural networks with extremely low precision weights and activations, such as Binarized Neural Networks (BNNs).\n\nExisting 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.\n\n## Getting Started\n\nTo build a QNN, Larq introduces the concept of [quantized layers](https://larq.dev/api/layers/) and [quantizers](https://larq.dev/api/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.\n\nYou can define a simple binarized fully-connected Keras model using the [Straight-Through Estimator](https://larq.dev/api/quantizers/#ste_sign) the following way:\n\n```python\nmodel = tf.keras.models.Sequential([\n tf.keras.layers.Flatten(),\n larq.layers.QuantDense(512,\n kernel_quantizer=\"ste_sign\",\n kernel_constraint=\"weight_clip\"),\n larq.layers.QuantDense(10,\n input_quantizer=\"ste_sign\",\n kernel_quantizer=\"ste_sign\",\n kernel_constraint=\"weight_clip\",\n activation=\"softmax\")])\n```\n\nThis layer can be used inside a [Keras model](https://www.tensorflow.org/alpha/guide/keras/overview#sequential_model) or with a [custom training loop](https://www.tensorflow.org/alpha/guide/keras/overview#model_subclassing).\n\n## Examples\n\nCheck out our examples on how to train a Binarized Neural Network in just a few lines of code:\n\n- [Introduction to BNNs with Larq](https://larq.dev/examples/mnist/)\n- [BinaryNet on CIFAR10](https://larq.dev/examples/binarynet_cifar10/)\n- [BinaryNet on CIFAR10 (Advanced)](https://larq.dev/examples/binarynet_advanced_cifar10/)\n\n## Installation\n\nBefore installing Larq, please install:\n\n- [Python](https://python.org) version `3.6` or `3.7`\n- [Tensorflow](https://www.tensorflow.org/install) version `1.13`, `1.14`, `1.15` or `2.0.0`:\n ```shell\n pip install tensorflow # or tensorflow-gpu\n ```\n\nYou can install Larq with Python's [pip](https://pip.pypa.io/en/stable/) package manager:\n\n```shell\npip install larq\n```\n\n\n",
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