@xiaoyaoyang When serialising a model, tensorflow creates a strict function call signature based on the tracing the model. Before serialising you have passed in a dict containing.

{'PRICE': TensorSpec(shape=(None,), dtype=tf.float32, name='PRICE'), 'USER_ID': TensorSpec(shape=(None,), dtype=tf.string, name='USER_ID'), 'TRANS_COUNT': TensorSpec(shape=(None,), dtype=tf.int64, name='TRANS_COUNT'), 'SKU_KEY': TensorSpec(shape=(None, 1), dtype=tf.string, name='SKU_KEY'), 'SKU_DESC': TensorSpec(shape=(None,), dtype=tf.string, name='SKU_DESC'), 'CONTEXT_ID': TensorSpec(shape=(None, 5), dtype=tf.string, name='CONTEXT_ID')}

When you serialise your model tensorflow will create a call signature that expects all those inputs, even if your model doesn’t use them. So when you call it with just USER_ID, it will fail.

It’s best to ensure you only pass the required features into your model during training and evaluation. Alternatively you should be able to resolve this by calling the model once with an example record with only the required features before serialising. This will then result in another call signature that matches the input you expect to pass when serving.

I mean getting a prediction when I say call the model. After you index the brute_force layer, you need to run.

scores, identifiers = index(example_record)

Hi @patrickorlando,

Thank you so much for your help!! Now the prediction of the loaded ranking model finally works! But when I tried to predict using the loaded retrieval model, it showed a totally different error message. Can you also help me with that? Thank you!! My code:

def save_retrieval(model):

# Create a BruteForce layer as before for prediction
index = tfrs.layers.factorized_top_k.BruteForce(model.query_model)
#index = tfrs.layers.factorized_top_k.ScaNN(model.query_model)
index.index_from_dataset(
    tf.data.Dataset.zip((items.batch(100), items.batch(100).map(model.candidate_model)))
)

# Export the query model.
with tempfile.TemporaryDirectory() as tmp:
    path = os.path.join(tmp, "retrieval")

    # Save the index.
    tf.saved_model.save(
        index, 
        path,
        #options=tf.saved_model.SaveOptions(namespace_whitelist=["Scann"]),
    )

    # Load it back; can also be done in TensorFlow Serving.
    loaded = tf.saved_model.load(path)
    #print(type(loaded))
    time = datetime.datetime.strptime("2021", '%Y')
    time_input = datetime.datetime.timestamp(time)

    # Pass a customer id in, get top predicted product id back.
    scores, titles = loaded({
        "customer_id": np.array(["0001019648"]), 
        "customer_price_group": np.array(["KK-nicht verwenden"]), 
        "customer_type": np.array(["END-ACCOUNT"]),
        "customer_industry": np.array(["Power Utilities"]),       
        "companyname_gu": np.array(["Zweckverband kommunaler Anteilseigner der WEMAG"]),        
        "project_flag": np.array([0]),        
        "timestamp": np.array([time_input]),
    }).numpy()

    print(f"Recommendations: {titles[0][:3]}")

save_retrieval(model)

The error message shows as follows:

WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow_recommenders.layers.factorized_top_k.BruteForce object at 0x000001BF160BBAF0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow_recommenders.layers.factorized_top_k.BruteForce object at 0x000001BF160BBAF0>, because it is not built. WARNING:absl:Found untraced functions such as query_with_exclusions while saving (showing 1 of 1). These functions will not be directly callable after loading. INFO:tensorflow:Assets written to: C:\Users\z004f16b\AppData\Local\Temp\tmpz47edw4c\retrieval\assets INFO:tensorflow:Assets written to: C:\Users\z004f16b\AppData\Local\Temp\tmpz47edw4c\retrieval\assets

TypeError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_23596/1368832069.py in <module> 70 71 #save_ranking(model) —> 72 save_retrieval(model)

~\AppData\Local\Temp/ipykernel_23596/1368832069.py in save_retrieval(model) 25 26 # Pass a customer id in, get top predicted product id back. —> 27 scores, titles = loaded({ 28 “customer_id”: np.array([“0001019648”]), 29 “customer_price_group”: np.array([“KK-nicht verwenden”]),

TypeError: ‘_UserObject’ object is not callable

As always, @patrickorlando has the right answer. The key here is passing only the features you need (here, only the user features) into your model, not all features.