基础教程部分，参考：Recommending movies: retrieval ?|? TensorFlow Recommenders
在召回模型训练阶段涉及到
task = tfrs.tasks.Retrieval(metrics=tfrs.metrics.FactorizedTopK(
movies.batch(5).map(movie_model),k=3
)
其作用有两个，一个是返回定义召回效果评估度量标准FactorizedTopK，另一个是定义了损失函数的计算方法，并对每一个批次返回对应的损失计算结果。
首先介绍损失函数部分Retrieval对应的call方法（Retrieval方法只适合双塔模型），
def call(self,
query_embeddings: tf.Tensor,
candidate_embeddings: tf.Tensor,
sample_weight: Optional[tf.Tensor] = None,
candidate_sampling_probability: Optional[tf.Tensor] = None,
candidate_ids: Optional[tf.Tensor] = None,
compute_metrics: bool = True) -> tf.Tensor:
"""Computes the task loss and metrics.
The main argument are pairs of query and candidate embeddings: the first row
of query_embeddings denotes a query for which the candidate from the first
row of candidate embeddings was selected by the user.
The task will try to maximize the affinity of these query, candidate pairs
while minimizing the affinity between the query and candidates belonging
to other queries in the batch.
Args:
query_embeddings: [num_queries, embedding_dim] tensor of query
representations.
candidate_embeddings: [num_queries, embedding_dim] tensor of candidate
representations.
sample_weight: [num_queries] tensor of sample weights.
candidate_sampling_probability: Optional tensor of candidate sampling
probabilities. When given will be be used to correct the logits to
reflect the sampling probability of negative candidates.
candidate_ids: Optional tensor containing candidate ids. When given
enables removing accidental hits of examples used as negatives. An
accidental hit is defined as an candidate that is used as an in-batch
negative but has the same id with the positive candidate.
compute_metrics: Whether to compute metrics. Set this to False
during training for faster training.
Returns:
loss: Tensor of loss values.
"""
#此处将两个向量的乘积结果作为用户和实体向量的相似度值，参考https://www.cnblogs.com/daniel-D/p/3244718.html
scores = tf.linalg.matmul(
query_embeddings, candidate_embeddings, transpose_b=True)
num_queries = tf.shape(scores)[0]
num_candidates = tf.shape(scores)[1]
#根据结果构造对角函数，作为预期结果矩阵
labels = tf.eye(num_queries, num_candidates)
metric_update_ops = []
if compute_metrics:
if self._factorized_metrics:
metric_update_ops.append(
self._factorized_metrics.update_state(query_embeddings,
candidate_embeddings))
if self._batch_metrics:
metric_update_ops.extend([
batch_metric.update_state(labels, scores)
for batch_metric in self._batch_metrics
])
if self._temperature is not None:
#
scores = scores / self._temperature
if candidate_sampling_probability is not None:
scores = layers.loss.SamplingProbablityCorrection()(
scores, candidate_sampling_probability)
if candidate_ids is not None:
scores = layers.loss.RemoveAccidentalHits()(labels, scores, candidate_ids)
if self._num_hard_negatives is not None:
scores, labels = layers.loss.HardNegativeMining(self._num_hard_negatives)(
scores,
labels)
loss = self._loss(y_true=labels, y_pred=scores, sample_weight=sample_weight)
if not metric_update_ops:
return loss
with tf.control_dependencies(metric_update_ops):
return tf.identity(loss)- 关于score和labels的计算已经在代码中进行了注解
- 关于温度淬火法参数的解释self._temperature参考：深度学习中的temperature parameter是什么 - 知乎
- 关于损失函数，默认的损失函数为类别交叉熵损失函数（对应ont-hot表示法），如果是integer表示法，可以换为
SparseCategoricalCrossentropy
tf.keras.losses.CategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.SUM)
?对应的调用样例参考：
>> y_true = [[0, 1, 0], [0, 0, 1]]
>>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> cce = tf.keras.losses.CategoricalCrossentropy()
>>> cce(y_true, y_pred).numpy()
1.177
>>> # Calling with 'sample_weight'.
>>> cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()
0.814
>>> # Using 'sum' reduction type.
>>> cce = tf.keras.losses.CategoricalCrossentropy(
... reduction=tf.keras.losses.Reduction.SUM)
>>> cce(y_true, y_pred).numpy()
2.354
关于度量标准部分?，此处重点理解TopKCategoricalAccuracy
查看源码解释既可明白，其是对每一个用户的topk推荐结果（是数值topk而非位置）是否包含了预期的结果，统计所有用户的对应情况占比。
>>> m = tf.keras.metrics.TopKCategoricalAccuracy(k=1)
>>> m.update_state([[0, 0, 1], [0, 1, 0]],
... [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
>>> m.result().numpy()
#1/2
0.5
>>> m.reset_state()
>>> m.update_state([[0, 0, 1], [0, 1, 0]],
... [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
... sample_weight=[0.7, 0.3])
>>> m.result().numpy()
#0*0.7+1*0.3
0.3在召回模型使用阶段，
?如果候选实体比较少的时候，可以使用暴力求解法：
index = tfrs.layers.factorized_top_k.BruteForce(model.user_model)
index.index_from_dataset(
movies.batch(100).map(lambda title: (title, model.movie_model(title))))
# Get some recommendations.
_, titles = index(np.array(["42"]))
print(f"Top 3 recommendations for user 42: {titles[0, :3]}")如果候选实体比较大的时候，可以使用近似ScaNN方法，tfrs.layers.factorized_top_k.ScaNN。关于ScaNN部分，也可以通过类似milvas等向量数据库的查询方法得以实现
