Playground - 코드 에디터

TensorFlow.js머신러닝 모델 학습 & 추론

model.js

// TensorFlow.js - Linear Regression
// Predicting y = 2x + 1

console.log("TensorFlow.js Linear Regression");
console.log("================================\n");

// Training data: y = 2x + 1
const xs = tf.tensor2d([0, 1, 2, 3, 4, 5, 6, 7], [8, 1]);
const ys = tf.tensor2d([1, 3, 5, 7, 9, 11, 13, 15], [8, 1]);

console.log("Training data: y = 2x + 1");
console.log("x: [0, 1, 2, 3, 4, 5, 6, 7]");
console.log("y: [1, 3, 5, 7, 9, 11, 13, 15]\n");

// Build model
const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [1] }));
model.compile({
  optimizer: tf.train.sgd(0.01),
  loss: 'meanSquaredError'
});

console.log("Training model (200 epochs)...");
const start = performance.now();

await model.fit(xs, ys, {
  epochs: 200,
  callbacks: {
    onEpochEnd: (epoch, logs) => {
      if ((epoch + 1) % 50 === 0) {
        console.log(`  Epoch ${epoch + 1}: loss = ${logs.loss.toFixed(6)}`);
      }
    }
  }
});

const elapsed = ((performance.now() - start) / 1000).toFixed(2);
console.log(`\nTraining completed in ${elapsed}s\n`);

// Predictions
console.log("Predictions:");
const testValues = [10, 20, 50, 100];
for (const x of testValues) {
  const pred = model.predict(tf.tensor2d([x], [1, 1]));
  const value = pred.dataSync()[0];
  console.log(`  x = ${x} → y = ${value.toFixed(2)} (expected: ${2 * x + 1})`);
}

// Get learned weights
const weights = model.getWeights();
console.log(`\nLearned: w = ${weights[0].dataSync()[0].toFixed(4)}, b = ${weights[1].dataSync()[0].toFixed(4)}`);
console.log("Expected: w = 2, b = 1");

Preview

Console

TensorFlow.jsmodel.jsUTF-8

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