Pembelajaran Mesin JavaScript: Bina Model ML dalam Penyemak Imbas

npm init -y

npm install @tensorflow/tfjs

// Import TensorFlow.js library import * as tf from '@tensorflow/tfjs'; // Define input features and output values const inputFeatures = tf.tensor2d([[1], [2], [3], [4], [5]], [5, 1]); const outputValues = tf.tensor2d([[2], [4], [6], [8], [10]], [5, 1]); // Define the model architecture const model = tf.sequential(); model.add(tf.layers.dense({ units: 1, inputShape: [1] })); // Compile the model model.compile({ optimizer: 'sgd', loss: 'meanSquaredError' }); // Train the model model.fit(inputFeatures, outputValues, { epochs: 100 }).then(() => { // Make predictions const predictions = model.predict(inputFeatures); // Print predictions predictions.print(); });

Tensor [2.2019906], [4.124609 ], [6.0472274], [7.9698458], [9.8924646]]

// Import TensorFlow.js library import * as tf from '@tensorflow/tfjs'; import '@tensorflow/tfjs-node'; // Required for Node.js environment // Define training data const trainingData = [ { text: 'I love this product!', sentiment: 'positive' }, { text: 'This is a terrible experience.', sentiment: 'negative' }, { text: 'The movie was amazing!', sentiment: 'positive' }, // Add more training data... ]; // Prepare training data const texts = trainingData.map(item => item.text); const labels = trainingData.map(item => (item.sentiment === 'positive' ? 1 : 0)); // Tokenize and preprocess the texts const tokenizedTexts = texts.map(text => text.toLowerCase().split(' ')); const wordIndex = new Map(); let currentIndex = 1; const sequences = tokenizedTexts.map(tokens => { return tokens.map(token => { if (!wordIndex.has(token)) { wordIndex.set(token, currentIndex); currentIndex++; } return wordIndex.get(token); }); }); // Pad sequences const maxLength = sequences.reduce((max, seq) => Math.max(max, seq.length), 0); const paddedSequences = sequences.map(seq => { if (seq.length < maxLength) { return seq.concat(new Array(maxLength - seq.length).fill(0)); } return seq; }); // Convert to tensors const paddedSequencesTensor = tf.tensor2d(paddedSequences); const labelsTensor = tf.tensor1d(labels); // Define the model architecture const model = tf.sequential(); model.add(tf.layers.embedding({ inputDim: currentIndex, outputDim: 16, inputLength: maxLength })); model.add(tf.layers.flatten()); model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' })); // Compile the model model.compile({ optimizer: 'adam', loss: 'binaryCrossentropy', metrics: ['accuracy'] }); // Train the model model.fit(paddedSequencesTensor, labelsTensor, { epochs: 10 }).then(() => { // Make predictions const testText = 'This product exceeded my expectations!'; const testTokens = testText.toLowerCase().split(' '); const testSequence = testTokens.map(token => { if (wordIndex.has(token)) { return wordIndex.get(token); } return 0; }); const paddedTestSequence = testSequence.length < maxLength ? testSequence.concat(new Array(maxLength - testSequence.length).fill(0)) : testSequence; const testSequenceTensor = tf.tensor2d([paddedTestSequence]); const prediction = model.predict(testSequenceTensor); const sentiment = prediction.dataSync()[0] > 0.5 ? 'positive' : 'negative'; // Print the sentiment prediction console.log(`The sentiment of "${testText}" is ${sentiment}.`); });

Epoch 1 / 10 eta=0.0 ========================================================================> 14ms 4675us/step - acc=0.00 loss=0.708 Epoch 2 / 10 eta=0.0 ========================================================================> 4ms 1428us/step - acc=0.667 loss=0.703 Epoch 3 / 10 eta=0.0 ========================================================================> 5ms 1733us/step - acc=0.667 loss=0.697 Epoch 4 / 10 eta=0.0 ========================================================================> 4ms 1419us/step - acc=0.667 loss=0.692 Epoch 5 / 10 eta=0.0 ========================================================================> 6ms 1944us/step - acc=0.667 loss=0.686 Epoch 6 / 10 eta=0.0 ========================================================================> 5ms 1558us/step - acc=0.667 loss=0.681 Epoch 7 / 10 eta=0.0 ========================================================================> 5ms 1513us/step - acc=0.667 loss=0.675 Epoch 8 / 10 eta=0.0 ========================================================================> 3ms 1057us/step - acc=1.00 loss=0.670 Epoch 9 / 10 eta=0.0 ========================================================================> 5ms 1745us/step - acc=1.00 loss=0.665 Epoch 10 / 10 eta=0.0 ========================================================================> 4ms 1439us/step - acc=1.00 loss=0.659 The sentiment of "This product exceeded my expectations!" is positive.