在本地将keras模型加载到tensorflow.js

pip install tensorflowjs keras

from keras.models import Sequential
from keras.layers import Dense

# 构建模型
model = Sequential()
model.add(Dense(64, activation='relu', input_dim=100))
model.add(Dense(10, activation='softmax'))

# 编译和训练模型
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10)

# 保存模型
model.save('model.h5')

tensorflowjs_converter --input_format keras model.h5 tfjs_model

import * as tf from '@tensorflow/tfjs';

// 加载模型
const model = await tf.loadLayersModel('tfjs_model/model.json');

// 运行推理
const input = tf.tensor2d([[1, 2, 3, 4]]);
const output = model.predict(input);

// 打印结果
output.print();