// cuteModel.js
const tf = require('@tensorflow/tfjs');
const model = tf.sequential();
model.add(tf.layers.conv2d(
inputShape: [224, 224, 3],
filters: 32,
kernelSize: 3,
));
model.add(tf.layers.maxPooling2d( poolSize: 2 ));
model.add(tf.layers.flatten());
model.add(tf.layers.dense(128, 'relu'));
model.add(tf.layers.dense(1));
model.compile( optimizer: tf.optimizers.adam(), loss: 'meanSquaredError' );
const cuteData = [
image: 'path/to/cute/image.jpg', label: 0.8 ,
image: 'path/to/not-cute/image.jpg', label: 0.2 ,
];
model.fit(cuteData.map((data) => data.image), cuteData.map((data) => data.label),
epochs: 100,
);
const calculateCutenessScore = (imagePath) =>
const img = tf.io.readFileSync(imagePath);
const tensor = tf.image.decodeImage(img, 3);
tensor = tf.image.resizeBilinear(tensor, [224, 224]);
tensor = tf.cast(tensor, 'float32');
const prediction = model.predict(tensor);
return prediction.dataSync()[0];
;
module.exports = calculateCutenessScore ;
If you want to try your hand at curating cuteness, here is a step-by-step guide:
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