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generate custom image classifier model

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Files changed (3) hide show
  1. nodejs/generate_model.js +63 -0
  2. nodejs/package-lock.json +2248 -0
  3. nodejs/package.json +18 -0
nodejs/generate_model.js ADDED
@@ -0,0 +1,63 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ const tf = require('@tensorflow/tfjs-node');
2
+ const fs = require('fs');
3
+ const path = require('path');
4
+
5
+ async function loadImages(folder) {
6
+ const files = fs.readdirSync(folder);
7
+ const images = [];
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+ const labels = [];
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+ for (const file of files) {
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+ const label = path.basename(folder);
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+ const imageBuffer = fs.readFileSync(path.join(folder, file));
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+ const imageTensor = tf.node.decodeImage(imageBuffer, 3)
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+ .resizeNearestNeighbor([128, 128])
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+ .toFloat()
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+ .div(tf.scalar(255.0));
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+ images.push(imageTensor);
17
+ labels.push(label);
18
+ }
19
+ return { images: tf.stack(images), labels };
20
+ }
21
+
22
+ async function loadDataset(basePath) {
23
+ const folders = fs.readdirSync(basePath);
24
+ const data = [];
25
+ const labelMap = {};
26
+ folders.forEach((folder, index) => labelMap[folder] = index);
27
+
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+ for (const folder of folders) {
29
+ const { images, labels } = await loadImages(path.join(basePath, folder));
30
+ data.push({ images, labels: labels.map(label => labelMap[label]) });
31
+ }
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+
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+ return {
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+ images: tf.concat(data.map(d => d.images)),
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+ labels: tf.oneHot(tf.tensor1d(data.flatMap(d => d.labels), 'int32'), Object.keys(labelMap).length),
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+ labelMap
37
+ };
38
+ }
39
+
40
+ async function trainModel() {
41
+ const basePath = './training_images'; // Folder with labeled subfolders
42
+ const dataset = await loadDataset(basePath);
43
+ const { images, labels } = dataset;
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+
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+ const model = tf.sequential();
46
+ model.add(tf.layers.conv2d({
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+ inputShape: [128, 128, 3],
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+ filters: 32,
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+ kernelSize: 3,
50
+ activation: 'relu'
51
+ }));
52
+ model.add(tf.layers.flatten());
53
+ model.add(tf.layers.dense({ units: Object.keys(dataset.labelMap).length, activation: 'softmax' }));
54
+ model.compile({ optimizer: 'adam', loss: 'categoricalCrossentropy', metrics: ['accuracy'] });
55
+
56
+ await model.fit(images, labels, { epochs: 5 });
57
+ await model.save('file://./saved-model'); // Save in TensorFlow.js format
58
+ console.log('Model saved as TensorFlow.js format');
59
+ }
60
+
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+ (async () => {
62
+ await trainModel();
63
+ })();
nodejs/package-lock.json ADDED
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