我正在尝试使用tensorflow-recommenders库开发一个非常基本的检索模型。我的数据集包含userid，itemid，genre和value（我没有在检索模型中使用value特征）。流派特征是RaggedTensor，这意味着一个项目可以属于单个或多个长度可变的流派。这就是我的数据集的样子：
x1c 0d1x的数据
对于建模，我有以下结构：
用户型号：

class UserModel(tf.keras.Model):
    def __init__(self,vocab_user_ids,embedding_dimension):
        super().__init__()
        self.user_embeddings = tf.keras.Sequential([
            tf.keras.layers.StringLookup(vocabulary=vocab_user_ids, mask_token=None),
            tf.keras.layers.Embedding(len(vocab_user_ids) + 1, embedding_dimension),
        ])

    def call(self, userid):
        return self.user_embeddings(userid)

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项目型号：

class ItemModel(tf.keras.Model):
    def __init__(self, vocab_item_ids, vocab_genres, embedding_dimension):
        super().__init__()
        self.item_embeddings = tf.keras.Sequential([
            tf.keras.layers.StringLookup(vocabulary=vocab_item_ids, mask_token=None),
            tf.keras.layers.Embedding(len(vocab_item_ids) + 1, embedding_dimension),
        ])
        self.genre_embedding = tf.keras.Sequential([
            tf.keras.layers.StringLookup(vocabulary=vocab_genres, mask_token=None),
            tf.keras.layers.Embedding(len(vocab_genres) + 1, embedding_dimension),
        ])

    def call(self, inputs):
        item_embedding = self.item_embeddings(inputs['itemid'])
        genre_embedding = self.genre_embedding(inputs['genre'])
        if isinstance(genre_embedding, tf.RaggedTensor):
            genre_embedding=tf.reduce_mean(genre_embedding,axis=1)
        else:
            genre_embedding=tf.reduce_mean(genre_embedding,axis=0)
        combined_embedding = tf.keras.layers.Multiply()([item_embedding*0.4, genre_embedding*0.6])
        return combined_embedding

型
推荐型号：

#Recommender Model
class RecommenderModel(tf.keras.Model):

    def __init__(self,user_model,item_model, candidates):
        super().__init__()

        self.user_model=user_model
        self.item_model=item_model

        self.task = tfrs.tasks.Retrieval(
            metrics=tfrs.metrics.FactorizedTopK(
                candidates=candidates.batch(batch_size).map(self.item_model),
                ks=[1,5,10,50,100]
            ),
        )

    def call(self, inputs: Dict[Text, tf.Tensor],training=False):
        user_id_embedding = self.user_model(inputs['userid'])
        item_embedding = self.item_model(inputs)
        return self.task(user_id_embedding,item_embedding,compute_metrics= training)
        

    def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:
        # generate embedding for the user ids
        user_id_embedding = self.user_model(features['userid'])

        # generate embedding for the item ids
        item_embedding = self.item_model(features)

        return self.task(user_id_embedding,item_embedding)

型
模型编译和训练代码：

model = RecommenderModel(UserModel(vocab_user_ids,32),ItemModel(vocab_item_ids,unique_genres,32),tf_data) 
model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1),loss=loss)

# shuffle and split data: train, valid, test
# set seed
tf.random.set_seed(42)

# total data points
N = data.shape[0]

# total train data points
N_train = int(0.8 * N)

# total valid data points
#N_valid = int(0.2 * N)

# total test data points
N_test = N - (N_train)
#N_test = N - (N_train + N_valid)

# shuffle data
shuffled = tf_data.shuffle(N, seed=42, reshuffle_each_iteration=False)

# # split data
num_epochs = 50
train = shuffled.take(N_train)
test = shuffled.skip((N_train)).take(N_test)
cached_train = train.batch(batch_size).cache()
cached_valid = test.batch(batch_size).cache()

model.fit(cached_train,epochs=10,batch_size=batch_size)

型
但我得到以下错误：

TypeError                                 Traceback (most recent call last)
File <command-2894041669182423>:1
----> 1 model.fit(cached_train,epochs=10,batch_size=batch_size)

File /databricks/python/lib/python3.9/site-packages/mlflow/utils/autologging_utils/safety.py:435, in safe_patch.<locals>.safe_patch_function(*args, **kwargs)
    420 if (
    421     active_session_failed
    422     or autologging_is_disabled(autologging_integration)
   (...)
    429     # warning behavior during original function execution, since autologging is being
    430     # skipped
    431     with set_non_mlflow_warnings_behavior_for_current_thread(
    432         disable_warnings=False,
    433         reroute_warnings=False,
    434     ):
--> 435         return original(*args, **kwargs)
    437 # Whether or not the original / underlying function has been called during the
    438 # execution of patched code
    439 original_has_been_called = False

File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.9/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File /tmp/__autograph_generated_filex0pt7t50.py:15, in outer_factory.<locals>.inner_factory.<locals>.tf__train_function(iterator)
     13 try:
     14     do_return = True
---> 15     retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
     16 except:
     17     do_return = False

TypeError: in user code:

    File "/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.9/site-packages/keras/engine/training.py", line 1284, in train_function  *
        return step_function(self, iterator)
    File "/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.9/site-packages/keras/engine/training.py", line 1268, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.9/site-packages/keras/engine/training.py", line 1249, in run_step  **
        outputs = model.train_step(data)
    File "/local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.9/site-packages/keras/engine/training.py", line 1051, in train_step
        loss = self.compute_loss(x, y, y_pred, sample_weight)

    TypeError: compute_loss() takes from 2 to 3 positional arguments but 5 were given

型
我试图找出错误，但我不能。到目前为止，我可以看到，我准确地覆盖了compute_loss（）函数。有人能指出我在这里遗漏了什么或者做错了什么导致了这个错误吗？