Building a Simple Neural Net in JAX

JAX is a numerical computing library that allows you to easily create and optimize neural networks. In this article, we will walk through the process of building a simple neural net using JAX.

Importing JAX

First, you will need to install JAX by running the following command:

!pip install jax jaxlib

Once you have installed JAX, you can import it into your Python script:

import jax

Defining the Neural Net

Next, we will define a simple feedforward neural network with one input layer, one hidden layer, and one output layer. We will use the `nn` submodule in JAX to define our neural network:


def neural_net(params, x):
w1, b1, w2, b2 = params
h = jax.nn.relu(jax.numpy.dot(x, w1) + b1)
return jax.numpy.dot(h, w2) + b2


In this code snippet, `w1`, `b1`, `w2`, and `b2` are the weights and biases of the neural network. We use the `relu` activation function in the hidden layer to introduce non-linearity.

Training the Neural Net

Once we have defined our neural net, we can train it on a dataset using gradient descent. We will need to define a loss function, compute its gradient, and update the weights and biases of the neural net:


def loss(params, x, y):
pred = neural_net(params, x)
return jax.numpy.mean((pred - y) ** 2)

def update(params, x, y, lr=0.01):
grad = jax.grad(loss)(params, x, y)
return [p - lr * g for p, g in zip(params, grad)]


Now, you can train the neural net by iterating over your dataset and updating the parameters using the `update` function.

Conclusion

Congratulations! You have successfully built a simple neural net in JAX. This is just a basic example, and JAX provides many more powerful tools and features for building and training complex neural networks. Explore the JAX documentation to learn more about its capabilities.