Attributes
Includes Deno configuration
Repository
Current version released
a year ago
Dependencies
deno.land/x
std
Netsaur
Powerful Machine Learning library for Deno
Backends
Examples
Maintainers
- Loading (@load1n9)
- CarrotzRule (@carrotzrule123)
Usage
import {
Activation,
Cost,
CPU,
DenseLayer,
Sequential,
setupBackend,
tensor2D,
} from "https://deno.land/x/netsaur/mod.ts";
await setupBackend(CPU);
const net = new Sequential({
size: [4, 2],
silent: true,
layers: [
DenseLayer({ size: [3], activation: Activation.Sigmoid }),
DenseLayer({ size: [1], activation: Activation.Sigmoid }),
],
cost: Cost.MSE,
});
const time = performance.now();
net.train(
[
{
inputs: tensor2D([
[0, 0],
[1, 0],
[0, 1],
[1, 1],
]),
outputs: tensor2D([[0], [1], [1], [0]]),
},
],
10000,
);
console.log(`training time: ${performance.now() - time}ms`);
console.log((await net.predict(tensor2D([[0, 0]]))).data);
console.log((await net.predict(tensor2D([[1, 0]]))).data);
console.log((await net.predict(tensor2D([[0, 1]]))).data);
console.log((await net.predict(tensor2D([[1, 1]]))).data);
Use the WASM Backend
import {
Activation,
Cost,
DenseLayer,
Sequential,
setupBackend,
tensor2D,
WASM,
} from "https://deno.land/x/netsaur/mod.ts";
await setupBackend(WASM);
const net = new Sequential({
size: [4, 2],
silent: true,
layers: [
DenseLayer({ size: [3], activation: Activation.Sigmoid }),
DenseLayer({ size: [1], activation: Activation.Sigmoid }),
],
cost: Cost.MSE,
});
const time = performance.now();
net.train(
[
{
inputs: tensor2D([
[0, 0],
[1, 0],
[0, 1],
[1, 1],
]),
outputs: tensor2D([[0], [1], [1], [0]]),
},
],
10000,
);
console.log(`training time: ${performance.now() - time}ms`);
console.log((await net.predict(tensor2D([[0, 0]]))).data);
console.log((await net.predict(tensor2D([[1, 0]]))).data);
console.log((await net.predict(tensor2D([[0, 1]]))).data);
console.log((await net.predict(tensor2D([[1, 1]]))).data);
Documentation
The full documentation for Netsaur can be found here.
License
Netsaur is licensed under the MIT License.