Update index.html

index.html

@@ -326,7 +326,6 @@
     this.activations = [];
     this.details = {};
     this.debug = debug;
-    this.fewShotSamples = [];
   }
 
   // Add a new layer to the neural network
@@ -393,16 +392,6 @@
     }
   }
 
-  // Generate few-shot samples
-  generateFewShotSamples(trainSet, numSamples = 10) {
-    const fewShotSamples = [];
-    for (let i = 0; i < numSamples; i++) {
-      const randomIndex = Math.floor(Math.random() * trainSet.length);
-      fewShotSamples.push(trainSet[randomIndex]);
-    }
-    return fewShotSamples;
-  }
-
   // Positional Encoding
   positionalEncoding(input, maxLen) {
     const pe = new Array(maxLen).fill(0).map((_, pos) => {
@@ -451,6 +440,13 @@
     return output;
   }
 
+  // Layer Normalization
+  layerNormalization(input) {
+    const mean = input.reduce((sum, val) => sum + val, 0) / input.length;
+    const variance = input.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / input.length;
+    return input.map(val => (val - mean) / Math.sqrt(variance + 1e-5));
+  }
+
   // Train the neural network
   async train(trainSet, options = {}) {
     const {
@@ -471,10 +467,6 @@
     }
     let lastTrainLoss = 0;
     let lastTestLoss = null;
-    let lastFewShotLoss = null;
-
-    // Generate few-shot samples
-    this.fewShotSamples = this.generateFewShotSamples(trainSet);
 
     for (let epoch = 0; epoch < epochs; epoch++) {
       let trainError = 0;
@@ -549,23 +541,15 @@
         lastTestLoss = testError / testSet.length;
       }
 
-      // Evaluate on few-shot samples
-      let fewShotError = 0;
-      for (const data of this.fewShotSamples) {
-        const prediction = this.predict(data.input);
-        fewShotError += Math.abs(data.output[0] - prediction[0]);
-      }
-      lastFewShotLoss = fewShotError / this.fewShotSamples.length;
-
       if ((epoch + 1) % printEveryEpochs === 0 && this.debug === true) {
-        console.log(`Epoch ${epoch + 1}, Train Loss: ${lastTrainLoss.toFixed(6)}${testSet ? `, Test Loss: ${lastTestLoss.toFixed(6)}` : ''}, Few-Shot Loss: ${lastFewShotLoss.toFixed(6)}`);
+        console.log(`Epoch ${epoch + 1}, Train Loss: ${lastTrainLoss.toFixed(6)}${testSet ? `, Test Loss: ${lastTestLoss.toFixed(6)}` : ''}`);
       }
       if (callback) {
-        await callback(epoch + 1, lastTrainLoss, lastTestLoss, lastFewShotLoss);
+        await callback(epoch + 1, lastTrainLoss, lastTestLoss);
       }
       await new Promise(resolve => setTimeout(resolve, 0));
       if (lastTrainLoss < earlyStopThreshold) {
-        console.log(`We stopped at epoch ${epoch + 1} with train loss: ${lastTrainLoss.toFixed(6)}${testSet ? ` and test loss: ${lastTestLoss.toFixed(6)}` : ''} and few-shot loss: ${lastFewShotLoss.toFixed(6)}`);
+        console.log(`We stopped at epoch ${epoch + 1} with train loss: ${lastTrainLoss.toFixed(6)}${testSet ? ` and test loss: ${lastTestLoss.toFixed(6)}` : ''}`);
         break;
       }
     }
@@ -579,7 +563,6 @@
     const trainingSummary = {
       trainLoss: lastTrainLoss,
       testLoss: lastTestLoss,
-      fewShotLoss: lastFewShotLoss,
       parameters: totalParams,
       training: {
         time: end - start,