NeuroFlow is a JavaScript library that allows you to implement neural network layers and architectures from scratch, similar to the Linear layer in PyTorch. It provides a foundation for reimplementing arbitrary parts of other libraries like PyTorch and TensorFlow, and includes a host module to execute the code as part of a neural network.
This project started as a port of karpathy/micrograd to JavaScript for educational purposes. The API has been tweaked to prioritize readability and understanding, making it suitable for learning and experimentation. However, it does not take performance considerations into account and is not recommended for production applications. For production use cases, consider using libraries like TensorFlow.js instead.
Demos: https://neuroflow.andypai.me/
You can install NeuroFlow using npm:
npm install @andypai/neuroflowHere's an example of how you can train a simple neural network using the Layer and Sequential classes for a regression problem:
import { Sequential, Layer } from 'neuroflow'
// Specify the architecture
const layer1 = new Layer({ numOfInputs: 3, numOfNeurons: 4 })
const layer2 = new Layer({ numOfInputs: 4, numOfNeurons: 3 })
const layer3 = new Layer({
numOfInputs: 3,
numOfNeurons: 1,
activation: 'linear',
})
const model = new Sequential({
layers: [layer1, layer2, layer3],
})
// Training data
const xs = [
[2.0, 3.0, -1.0],
[3.0, -1.0, 0.5],
[0.5, 1.0, 1.0],
[1.0, 1.0, -1.0],
]
const ys = [1.0, -1.0, -1.0, 1.0]
// Training loop
const epochs = 15
for (let epoch = 0; epoch < epochs; epoch++) {
// Forward pass
const yPredictions = xs.map((x) => model.forward(x))
// Mean squared error (MSE) loss
const loss = ys.reduce(
(acc, yTarget, i) => yPredictions[i].sub(yTarget).pow(2).add(acc),
0,
)
// Backward pass
model.zeroGrad()
loss.backward()
// Update weights
const learningRate = 0.01
model.parameters().forEach((p) => {
p.data -= learningRate * p.grad
})
console.info(`Epoch: ${epoch + 1}, Loss: ${loss.data}`)
}
// Inference
model.forward([2.0, 3.0, -1.0]) // Output: 1The full example is available here
This project is licensed under the MIT License.