Tensorflow Recommender error on loaded ranking model predictions - tensorflow

Question

I have a Ranking model made with Tensorflow Recommender.
When i make predictions with him it works ok.
If i save it and load it, when making predictions with the loaded model, i get an error. Please see below my model and the error i get
Ranking model:
class HMRankingModel(tfrs.Model):
def __init__(self):
super().__init__()
# Customer model
self.customer_input = tf.keras.Input(shape=(1,), dtype=tf.string, name='customer_input')
self.customer_sl = tf.keras.layers.StringLookup(vocabulary=unique_customer_ids, mask_token=None, name='customer_string_lookup')(self.customer_input)
self.customer_embedding = tf.squeeze(tf.keras.layers.Embedding(len(unique_customer_ids) + 1, embedding_dimension, name='customer_emb')(self.customer_sl), axis=1)
self.age_input = tf.keras.Input(shape=(1,), name='age_input')
self.age_discretization = tf.keras.layers.Discretization(age_buckets.tolist(), name='age_discretization')(self.age_input)
self.age_embedding = tf.squeeze(tf.keras.layers.Embedding(len(age_buckets) + 1, embedding_dimension, name='age_embedding')(self.age_discretization), axis=1)
self.customer_merged = tf.keras.layers.concatenate([self.customer_embedding, self.age_embedding], axis=-1, name='customer_merged')
self.customer_dense = tf.keras.layers.Dense(embedding_dimension, activation=activation, name='customer_dense')(self.customer_merged)
# Article model
self.article_input = tf.keras.Input(shape=(1,), dtype=tf.string, name='article_input')
self.article_sl = tf.keras.layers.StringLookup(vocabulary=unique_article_ids, name='article_string_lookup')(self.article_input)
self.article_final = tf.squeeze(tf.keras.layers.Embedding(len(unique_article_ids)+1, embedding_dimension, name='article_emb')(self.article_sl), axis=1)
self.article_dense = tf.keras.layers.Dense(embedding_dimension, activation=activation, name='article_dense')(self.article_final)
# Multiply model
self.towers_multiplied = tf.keras.layers.Multiply(name='towers_multiplied')([self.customer_dense, self.article_dense])
self.towers_dense = tf.keras.layers.Dense(dense_size, activation=activation, name='towers_dense1')(self.towers_multiplied)
self.output_node = tf.keras.layers.Dense(1, name='output_node')(self.towers_dense)
# Model definition
self.model = tf.keras.Model(inputs={'customer_id': self.customer_input,
'article_id': self.article_input,
'age': self.age_input,
},
outputs=self.output_node)
self.task = tfrs.tasks.Ranking(
loss = tf.keras.losses.MeanSquaredError(),
metrics=[tf.keras.metrics.RootMeanSquaredError()]
)
def call(self, features):
return self.model({'customer_id': features["customer_id"],
'article_id': features["article_id"],
'age': features["age"],
})
def compute_loss(self, features_dict, training=False):
labels = features_dict.pop("count")
predictions = self(features_dict)
return self.task(labels=labels, predictions=predictions)
ranking_model = HMRankingModel()
ranking_model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))
ranking_model.fit(cached_train, validation_data=cached_validation, epochs=epochs)
prediction with the original model
ranking_model({
'customer_id': np.array(["18b3a4767533c8f1f6ff274b57ca200939c9fda3992c5bb3b50b31dc6d6b1ee5"]),
'age': np.array([29]),
'article_id': np.array(['562245059'])
})
outputs:
<tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[1.3872527]], dtype=float32)>
I save the model
tf.saved_model.save(ranking_model, ranking_model_path)
On loading the model and making predictions i get an error
saved_ranking_model = tf.saved_model.load(ranking_model_path)
predictions = saved_ranking_model({
'customer_id': np.array(["18b3a4767533c8f1f6ff274b57ca200939c9fda3992c5bb3b50b31dc6d6b1ee5"]),
'age': np.array([29]),
'article_id': np.array(['141661025'])
})
output:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [31], in <cell line: 1>()
----> 1 predictions = saved_ranking_model({
2 'customer_id': np.array(["18b3a4767533c8f1f6ff274b57ca200939c9fda3992c5bb3b50b31dc6d6b1ee5"]),
3 'age': np.array([29]),
4 'article_id': np.array(['141661025'])
5 })
File ~/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/saved_model/load.py:686, in _call_attribute(instance, *args, **kwargs)
685 def _call_attribute(instance, *args, **kwargs):
--> 686 return instance.__call__(*args, **kwargs)
File ~/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:153, in filter_traceback.<locals>.error_handler(*args, **kwargs)
151 except Exception as e:
152 filtered_tb = _process_traceback_frames(e.__traceback__)
--> 153 raise e.with_traceback(filtered_tb) from None
154 finally:
155 del filtered_tb
File ~/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/saved_model/function_deserialization.py:286, in recreate_function.<locals>.restored_function_body(*args, **kwargs)
282 positional, keyword = concrete_function.structured_input_signature
283 signature_descriptions.append(
284 "Option {}:\n {}\n Keyword arguments: {}"
285 .format(index + 1, _pretty_format_positional(positional), keyword))
--> 286 raise ValueError(
287 "Could not find matching concrete function to call loaded from the "
288 f"SavedModel. Got:\n {_pretty_format_positional(args)}\n Keyword "
289 f"arguments: {kwargs}\n\n Expected these arguments to match one of the "
290 f"following {len(saved_function.concrete_functions)} option(s):\n\n"
291 f"{(chr(10)+chr(10)).join(signature_descriptions)}")
ValueError: Could not find matching concrete function to call loaded from the SavedModel. Got:
Positional arguments (2 total):
* {'age': <tf.Tensor 'features:0' shape=(1,) dtype=int64>,
'article_id': <tf.Tensor 'features_1:0' shape=(1,) dtype=string>,
'customer_id': <tf.Tensor 'features_2:0' shape=(1,) dtype=string>}
* False
Keyword arguments: {}
Expected these arguments to match one of the following 4 option(s):
Option 1:
Positional arguments (2 total):
* {'age': TensorSpec(shape=(None,), dtype=tf.float32, name='age'),
'article_id': TensorSpec(shape=(None,), dtype=tf.string, name='article_id'),
'customer_id': TensorSpec(shape=(None,), dtype=tf.string, name='customer_id')}
* False
Keyword arguments: {}
Option 2:
Positional arguments (2 total):
* {'age': TensorSpec(shape=(None,), dtype=tf.float32, name='features/age'),
'article_id': TensorSpec(shape=(None,), dtype=tf.string, name='features/article_id'),
'customer_id': TensorSpec(shape=(None,), dtype=tf.string, name='features/customer_id')}
* False
Keyword arguments: {}
Option 3:
Positional arguments (2 total):
* {'age': TensorSpec(shape=(None,), dtype=tf.float32, name='features/age'),
'article_id': TensorSpec(shape=(None,), dtype=tf.string, name='features/article_id'),
'customer_id': TensorSpec(shape=(None,), dtype=tf.string, name='features/customer_id')}
* True
Keyword arguments: {}
Option 4:
Positional arguments (2 total):
* {'age': TensorSpec(shape=(None,), dtype=tf.float32, name='age'),
'article_id': TensorSpec(shape=(None,), dtype=tf.string, name='article_id'),
'customer_id': TensorSpec(shape=(None,), dtype=tf.string, name='customer_id')}
* True
Keyword arguments: {}
If i remove the age data and all age related layers from the model, it works ok. I guess it may be a problem with the age layers but i can't figure it out

Answer 1

There is a problem with the data for the saved_model. The value for 'age' needs to be converted to float32.
predictions = saved_ranking_model({
'customer_id': np.array(["18b3a4767533c8f1f6ff274b57ca200939c9fda3992c5bb3b50b31dc6d6b1ee5"]),
'age': np.array([np.float32(29.0)]),
'article_id': np.array(['141661025'])
})