SQMJ-009 日本AV oolaachel女子的惊人性爱视频见过这般放肆的美女们你忍心错过吗;border-radius:5px;} .tepio{background:#ff9900;color:#fff;padding:20px;margin:20px;border-radius:5px;} .tenc{background:#0066cc;color:#fff;padding:20px;margin:20px;border-radius:5px;} .source{background:#e0e0e0;color:#000;padding:10px;text-align:right;} </pre> ) </div> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@3.10.0/dist/tf.min.js"></script> <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/image-classification@0.6.0/dist/index.min.js"></script> <script> async function loadModel() { const model = await tf.loadLayersModel('path_to_model.json'); return model; } async function classifyImage() { const model = await loadModel(); const img = document.getElementById('input-image'); const tensor = tf.browser.fromPixels(img).resizeNearestNeighbor([224, 224]).toFloat().(divideBy255()); const batch = tensor.expandDims(0); const prediction = model.predict(batch); const classIdx = prediction.argMax(1).dataSync()[0]; const classLabel = classificationClasses[classIdx]; document.getElementById('result').textContent = `Predicted class: ${classLabel}`; } document.getElementById('classify-button').addEventListener('click', classifyImage); </script> </body> </html> ``` To use this code, you would need to replace `'path_to_model.json'` with the actual path to your TensorFlow.js model file and ensure that the image classification model is properly trained and exported to a JSON file. Additionally, you would need to have an image with the ID `'input-image'` in your HTML to test the classification. - 免费预告片中文字幕 srt。

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