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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,82 +1,82 @@ | ||
| import { MLP } from "./NeuralNetwork/mlp"; | ||
| import { Value } from "./NeuralNetwork/value"; | ||
|
|
||
| // Example usage | ||
| const xs: number[][] = [ | ||
| [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 yt: number[] = [1.0, -1.0, -1.0, 1.0]; | ||
|
|
||
| // Create MLP | ||
| const n = new MLP({ | ||
| inputSize: 1, | ||
| layers: [4, 4, 1], | ||
| activations: ['tanh', 'tanh', 'tanh'] | ||
| }); | ||
|
|
||
| // Hyperparameters | ||
| const learningRate = 0.01; | ||
| const iterations = 100; | ||
| const batchSize = 2; | ||
|
|
||
| // Training loop | ||
| for (let iteration = 0; iteration < iterations; iteration++) { | ||
| let totalLoss = new Value(0); | ||
|
|
||
| // Mini-batch training | ||
| for (let i = 0; i < xs.length; i += batchSize) { | ||
| const batchXs = xs.slice(i, i + batchSize); | ||
| const batchYt = yt.slice(i, i + batchSize); | ||
|
|
||
| const ypred = batchXs.map(x => n.forward(x.map(val => new Value(val)))[0]); | ||
|
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| const loss = ypred.reduce((sum, ypred_el, j) => { | ||
| const target = new Value(batchYt[j]); | ||
| const diff = ypred_el.sub(target); | ||
| const squaredError = diff.mul(diff); | ||
| return sum.add(squaredError); | ||
| }, new Value(0)); | ||
|
|
||
| // Accumulate total loss | ||
| totalLoss = totalLoss.add(loss); | ||
|
|
||
| // Backward pass | ||
| n.zeroGrad(); | ||
| loss.backward(); | ||
|
|
||
| // Update parameters | ||
| n.parameters().forEach(p => { | ||
| p.data -= learningRate * p.grad; | ||
| }); | ||
|
|
||
| // Inside the training loop, after calculating the loss | ||
| console.log("Loss function tree:"); | ||
| console.log(loss.toDot()); | ||
| } | ||
|
|
||
| // Log average loss for the iteration | ||
| console.log(`Iteration ${iteration + 1}, Average Loss: ${totalLoss.data / xs.length}`); | ||
|
|
||
| // Early stopping (optional) | ||
| if (totalLoss.data / xs.length < 0.01) { | ||
| console.log(`Converged at iteration ${iteration + 1}`); | ||
| break; | ||
| } | ||
| } | ||
|
|
||
| // Evaluation | ||
| function evaluate(x: number[]): number { | ||
| const result = n.forward(x.map(val => new Value(val))); | ||
| return result[0].data; | ||
| } | ||
|
|
||
| console.log("Evaluation:"); | ||
| xs.forEach((x, i) => { | ||
| console.log(`Input: [${x}], Predicted: ${evaluate(x).toFixed(4)}, Actual: ${yt[i]}`); | ||
| }); | ||
| // import { MLP } from "./NeuralNetwork/mlp"; | ||
| // import { Value } from "./NeuralNetwork/value"; | ||
|
|
||
| // // Example usage | ||
| // const xs: number[][] = [ | ||
| // [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 yt: number[] = [1.0, -1.0, -1.0, 1.0]; | ||
|
|
||
| // // Create MLP | ||
| // const n = new MLP({ | ||
| // inputSize: 1, | ||
| // layers: [4, 4, 1], | ||
| // activations: ['tanh', 'tanh', 'tanh'] | ||
| // }); | ||
|
|
||
| // // Hyperparameters | ||
| // const learningRate = 0.01; | ||
| // const iterations = 100; | ||
| // const batchSize = 2; | ||
|
|
||
| // // Training loop | ||
| // for (let iteration = 0; iteration < iterations; iteration++) { | ||
| // let totalLoss = new Value(0); | ||
|
|
||
| // // Mini-batch training | ||
| // for (let i = 0; i < xs.length; i += batchSize) { | ||
| // const batchXs = xs.slice(i, i + batchSize); | ||
| // const batchYt = yt.slice(i, i + batchSize); | ||
|
|
||
| // const ypred = batchXs.map(x => n.forward(x.map(val => new Value(val)))[0]); | ||
|
|
||
| // const loss = ypred.reduce((sum, ypred_el, j) => { | ||
| // const target = new Value(batchYt[j]); | ||
| // const diff = ypred_el.sub(target); | ||
| // const squaredError = diff.mul(diff); | ||
| // return sum.add(squaredError); | ||
| // }, new Value(0)); | ||
|
|
||
| // // Accumulate total loss | ||
| // totalLoss = totalLoss.add(loss); | ||
|
|
||
| // // Backward pass | ||
| // n.zeroGrad(); | ||
| // loss.backward(); | ||
|
|
||
| // // Update parameters | ||
| // n.parameters().forEach(p => { | ||
| // p.data -= learningRate * p.grad; | ||
| // }); | ||
|
|
||
| // // Inside the training loop, after calculating the loss | ||
| // console.log("Loss function tree:"); | ||
| // console.log(loss.toDot()); | ||
| // } | ||
|
|
||
| // // Log average loss for the iteration | ||
| // console.log(`Iteration ${iteration + 1}, Average Loss: ${totalLoss.data / xs.length}`); | ||
|
|
||
| // // Early stopping (optional) | ||
| // if (totalLoss.data / xs.length < 0.01) { | ||
| // console.log(`Converged at iteration ${iteration + 1}`); | ||
| // break; | ||
| // } | ||
| // } | ||
|
|
||
| // // Evaluation | ||
| // function evaluate(x: number[]): number { | ||
| // const result = n.forward(x.map(val => new Value(val))); | ||
| // return result[0].data; | ||
| // } | ||
|
|
||
| // console.log("Evaluation:"); | ||
| // xs.forEach((x, i) => { | ||
| // console.log(`Input: [${x}], Predicted: ${evaluate(x).toFixed(4)}, Actual: ${yt[i]}`); | ||
| // }); | ||
|
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