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import {  Cost,  DenseLayer,  Sequential,  setupBackend,  SigmoidLayer,  tensor1D,  tensor2D,  WASM,} from "../mod.ts";
await setupBackend(WASM);
Deno.bench(  { name: "xor 10000 epochs", permissions: "inherit" },  async () => {    const net = new Sequential({      size: [4, 2],      silent: true,      layers: [        DenseLayer({ size: [3] }),        SigmoidLayer(),        DenseLayer({ size: [1] }),        SigmoidLayer(),      ],      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(tensor1D([0, 0]))).data);    console.log((await net.predict(tensor1D([1, 0]))).data);    console.log((await net.predict(tensor1D([0, 1]))).data);    console.log((await net.predict(tensor1D([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);