Netsaur
Powerful Machine Learning library for Deno.
Features
- Lightweight and easy-to-use neural network library for Deno.
- Blazingly fast and efficient.
- Provides a simple API for creating and training neural networks.
- Can run on both the CPU and the GPU (WIP).
- Allows you to simply run the code without downloading any prior dependencies.
- Perfect for serverless environments.
- Allows you to quickly build and deploy machine learning models for a variety of applications with just a few lines of code.
- Suitable for both beginners and experienced machine learning practitioners.
Backends
Examples
Maintainers
- Dean Srebnik (@load1n9)
- CarrotzRule (@carrotzrule123)
QuickStart
This example shows how to train a neural network to predict the output of the XOR function our speedy CPU backend written in rust.
import {
Cost,
CPU,
DenseLayer,
Sequential,
setupBackend,
SigmoidLayer,
tensor1D,
tensor2D,
} from "https://deno.land/x/netsaur/mod.ts";
/**
* Setup the CPU backend. This backend is fast but doesn't work on the Edge.
*/
await setupBackend(CPU);
/**
* Creates a sequential neural network.
*/
const net = new Sequential({
/**
* The number of minibatches is set to 4 and the output size is set to 2.
*/
size: [4, 2],
/**
* The silent option is set to true, which means that the network will not output any logs during trainin
*/
silent: true,
/**
* Defines the layers of a neural network in the XOR function example.
* The neural network has two input neurons and one output neuron.
* The layers are defined as follows:
* - A dense layer with 3 neurons.
* - sigmoid activation layer.
* - A dense layer with 1 neuron.
* -A sigmoid activation layer.
*/
layers: [
DenseLayer({ size: [3] }),
SigmoidLayer(),
DenseLayer({ size: [1] }),
SigmoidLayer(),
],
/**
* The cost function used for training the network is the mean squared error (MSE).
*/
cost: Cost.MSE,
});
const time = performance.now();
/**
* Train the network on the given data.
*/
net.train(
[
{
inputs: tensor2D([
[0, 0],
[1, 0],
[0, 1],
[1, 1],
]),
outputs: tensor2D([[0], [1], [1], [0]]),
},
],
/**
* The number of iterations is set to 10000.
*/
10000,
);
console.log(`training time: ${performance.now() - time}ms`);
/**
* Predict the output of the XOR function for the given inputs.
*/
const out1 = (await net.predict(tensor1D([0, 0]))).data;
console.log(`0 xor 0 = ${out1[0]} (should be close to 0)`);
const out2 = (await net.predict(tensor1D([1, 0]))).data;
console.log(`1 xor 0 = ${out2[0]} (should be close to 1)`);
const out3 = (await net.predict(tensor1D([0, 1]))).data;
console.log(`0 xor 1 = ${out3[0]} (should be close to 1)`);
const out4 = (await net.predict(tensor1D([1, 1]))).data;
console.log(`1 xor 1 = ${out4[0]} (should be close to 0)`);Use the WASM Backend
By changing the CPU backend to the WASM backend we sacrifice some speed but this allows us to run on the edge.
import {
Cost,
DenseLayer,
Sequential,
setupBackend,
SigmoidLayer,
tensor1D,
tensor2D,
WASM,
} from "https://deno.land/x/netsaur/mod.ts";
/**
* Setup the WASM backend. This backend is slower than the CPU backend but works on the Edge.
*/
await setupBackend(WASM);
/**
* Creates a sequential neural network.
*/
const net = new Sequential({
/**
* The number of minibatches is set to 4 and the output size is set to 2.
*/
size: [4, 2],
/**
* The silent option is set to true, which means that the network will not output any logs during trainin
*/
silent: true,
/**
* Defines the layers of a neural network in the XOR function example.
* The neural network has two input neurons and one output neuron.
* The layers are defined as follows:
* - A dense layer with 3 neurons.
* - sigmoid activation layer.
* - A dense layer with 1 neuron.
* -A sigmoid activation layer.
*/
layers: [
DenseLayer({ size: [3] }),
SigmoidLayer(),
DenseLayer({ size: [1] }),
SigmoidLayer(),
],
/**
* The cost function used for training the network is the mean squared error (MSE).
*/
cost: Cost.MSE,
});
const time = performance.now();
/**
* Train the network on the given data.
*/
net.train(
[
{
inputs: tensor2D([
[0, 0],
[1, 0],
[0, 1],
[1, 1],
]),
outputs: tensor2D([[0], [1], [1], [0]]),
},
],
/**
* The number of iterations is set to 10000.
*/
10000,
);
console.log(`training time: ${performance.now() - time}ms`);
/**
* Predict the output of the XOR function for the given inputs.
*/
const out1 = (await net.predict(tensor1D([0, 0]))).data;
console.log(`0 xor 0 = ${out1[0]} (should be close to 0)`);
const out2 = (await net.predict(tensor1D([1, 0]))).data;
console.log(`1 xor 0 = ${out2[0]} (should be close to 1)`);
const out3 = (await net.predict(tensor1D([0, 1]))).data;
console.log(`0 xor 1 = ${out3[0]} (should be close to 1)`);
const out4 = (await net.predict(tensor1D([1, 1]))).data;
console.log(`1 xor 1 = ${out4[0]} (should be close to 0)`);Documentation
The full documentation for Netsaur can be found here.
License
Netsaur is licensed under the MIT License.