# Getting Started

Larq is an open-source Python library for training neural networks with extremely low-precision weights and activations, such as Binarized Neural Networks (BNNs)1.

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. The API of Larq is built on top of tf.keras and is designed to provide an easy to use, composable way to design and train BNNs (1 bit) and other types of Quantized Neural Networks (QNNs). It provides tools specifically designed to aid in BNN development, such as specialized optimizers, training metrics, and profiling tools. It is aimed at both researchers in the field of efficient deep learning and practitioners who want to explore BNNs for their applications. Furthermore, Larq makes it easier for beginners and students to get started with the field of efficient deep learning. 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.

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

For a detailed explanation checkout our user guide.

Larq is part of a family of libraries for BNN development; you can also check out Larq Zoo for pre-trained models and Larq Compute Engine for optimized deployment.

## Defining a simple BNN¶

A simple fully-connected BNN using the Straight-Through Estimator can be defined in just a few lines of code using either the Keras sequential, functional or model subclassing APIs:

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",
),
]
)

x = tf.keras.Input(shape=(28, 28, 1))
y = tf.keras.layers.Flatten()(x)
y = larq.layers.QuantDense(
512, kernel_quantizer="ste_sign", kernel_constraint="weight_clip"
)(y)
y = larq.layers.QuantDense(
10,
input_quantizer="ste_sign",
kernel_quantizer="ste_sign",
kernel_constraint="weight_clip",
activation="softmax",
)(y)
model = tf.keras.Model(inputs=x, outputs=y)

class MyModel(tf.keras.Model):
def __init__(self):
super().__init__()
self.flatten = tf.keras.layers.Flatten()
self.dense1 = larq.layers.QuantDense(
512, kernel_quantizer="ste_sign", kernel_constraint="weight_clip"
)
self.dense2 = larq.layers.QuantDense(
10,
input_quantizer="ste_sign",
kernel_quantizer="ste_sign",
kernel_constraint="weight_clip",
activation="softmax",
)

def call(self, inputs):
x = self.flatten(inputs)
x = self.dense1(x)
return self.dense2(x)

model = MyModel()


## Installation¶

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

pip install larq