如何使用 tensorflow saved_model.load

Question

I am following this official tensorflow tutorial to build a text classification model

I am exporting the trained model as such

serving_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(tf.feature_column.make_parse_example_spec([embedded_text_feature_column]))
export_path = estimator.export_saved_model("./models/sentiment", serving_input_fn)

I was not sure how to pass a sample sentence (eg "it was a great movie") to do prediction when loading.

imported = tf.saved_model.load(b'./models/sentiment/1586848142')
infer = imported.signatures["serving_default"]

Answer 1

This is what you need to load the model

imported = tf.saved_model.load(export_path)


def predict(x):
    example = tf.train.Example()
    example.features.feature["sentence"].bytes_list.value.extend([x])
    out = imported.signatures["predict"](examples=tf.constant([example.SerializeToString()]))['probabilities']
    return out

x = b"I am happy"
predict(x)