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netsaur_cpu.ts
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netsaur_cpu.ts
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import {
Activation,
Cost,
CPU,
DenseLayer,
NeuralNetwork,
setupBackend,
tensor2D,
} from "../mod.ts";
await setupBackend(CPU);
Deno.bench(
{ name: "xor 10000 epochs", permissions: "inherit" },
async () => {
const net = new NeuralNetwork({
size: [4, 2],
silent: true,
layers: [
DenseLayer({ size: [3], activation: Activation.Sigmoid }),
DenseLayer({ size: [1], activation: Activation.Sigmoid }),
],
cost: Cost.MSE,
});
net.train(
[
{
inputs: tensor2D([
[0, 0],
[1, 0],
[0, 1],
[1, 1],
]),
outputs: tensor2D([[0], [1], [1], [0]]),
},
],
10000,
);
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);
},
);
// const net = new NeuralNetwork({
// 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);