在 python 中保存 Keras/Sklearn 并在 tensorflow.js 中加载保存的模型

Question

I have a trained sklearn SVM model in .pkl format and a Keras .h5 model. Can I load these models using tensorflow.js on a browser? I do most of my coding in python and not sure how to work with tensorflow.js My model saving code looks like this

from sklearn.externals import joblib 

joblib.dump(svc,'model.pkl') 
model = joblib.load('model.pkl')  
prediction = model.predict(X_test) 

#------------------------------------------------------------------

from keras.models import load_model

model.save('model.h5')  
model = load_model('my_model.h5')

Answer 1

In order to deploy your model with tensorflow-js, you need to use the tensorflowjs_converter , so you also need to install the tensorflowjs dependency.

You can do that in python via pip install tensorflowjs .

Next, you convert your trained model via this operation, according to your custom names: tensorflowjs_converter --input_format=keras /tmp/model.h5 /tmp/tfjs_model , where the last path is the output path of the conversion result.

Note that, after the conversion you will get a model.json (architecture of your model) and a list of N shards (weights split in N shards).

Then, in JavaScript, you need to us the function tf.loadLayersModel(MODEL_URL) , where MODEL_URL is the url pointing to your model.json. Ensure that, at the same location with the model.json, the shards are also located.

Since this is an asynchronous operation(you do not want your web-page to get blocked while your model is loading), you need to use the JavaScript await keyword; hence await tf.loadLayersModel(MODEL_URL)

Please have a look at the following link to see an example: https://www.tensorflow.org/js/guide/conversion