如何加载使用 make_image_classifier 工具保存的 Tensorflow 模型

Question

I've made a custom image classifier model using a Tensorflow tool called make_image_classifier https://github.com/tensorflow/hub/tree/master/tensorflow_hub/tools/make_image_classifier

Now the model is exported into a .pb file and also 2 folders, assets and variables.

The question is how can I use this custom model to make predictions? I've gone through all TF documentation and have tried many different things over these days but found no solution.

Someone wrote about it when he found no clear information, so he created a guide but it also doesn't work for me. In "step 3" its all the code required to load the module and classify an image using the custom model. The problem with this is I need to know the name of the input and output node, and I don't have them. I've tried to find them using Netron but it didn't work. https://heartbeat.fritz.ai/automl-vision-edge-exporting-and-loading-tensorflow-saved-models-with-python-f4e8ce1b943a

import tensorflow as tf
export_path = '/Users/aayusharora/Aftershoot/backend/loadmodel/models/'
with tf.Session(graph=tf.Graph()) as sess:
  tf.saved_model.loader.load(sess, ['serve'], export_path)
path = '/Users/aayusharora/Aftershoot/backend/sampleImage.jpg'
with open(path, "rb") as img_file:
  y_pred = sess.run('tile:0', feed_dict={'normalised_input_image_tensor': [img_file.read()] })
print(y_pred)

Can someone please give me a clue about how to load a saved model and use it to make predictions?

Answer 1

From Save and load models | Tensorflow Core :

You can reload using a saved model:

new_model = tf.keras.models.load_model('<path-to-export-path>/my_model')

Assuming you have these files together ( assets , variables , the .pb file) which you did seem to have:

ls <path-to-export-path>/my_model

my_model
assets  saved_model.pb  variables

The new_model should be like the original. To check its architecture:

new_model.summary()