我正在谷歌Colab中尝试查找表有问题如果我单独运行它，它也允许整数作为键，但在这个模型类中，我似乎无法将整数值作为键传递
class SimpleRecommender(tf.keras.Model):
def __init__(self, dummy_users, products,lenght_of_embedding):
super(SimpleRecommender, self).__init__()
self.products = tf.constant(products, dtype=tf.int32)
self.dummy_users = tf.constant(dummy_users, dtype=tf.string)
self.dummy_user_table = tf.lookup.StaticHashTable(tf.lookup.KeyValueTensorInitializer(self.dummy_users,range(len(dummy_users))), -1)
self.product_table = tf.lookup.StaticHashTable(tf.lookup.KeyValueTensorInitializer(tf.constant(self.products,dtype=tf.int32), range(len(products))), -1)
self.user_embedding = tf.keras.layers.Embedding(len(dummy_users), lenght_of_embedding)
self.product_embedding = tf.keras.layers.Embedding(len(products), lenght_of_embedding)
self.dot=tf.keras.layers.Dot(axes=-1)
def call(self,inputs):
user=inputs[0]
products=inputs[1]
user_embedding_index=self.dummy_user_table.lookup(user)
prod_embedding_index=self.dummy_user_table.lookup(products)
user_embedding_values=self.user_embedding(user_embedding_index)
prod_embedding_values=self.product_embedding(prod_embedding_index)
return self.dot([user_embedding_values,prod_embedding_values])
@tf.function
def call_item_item(self, product):
product_x = self.product_table.lookup(product)
pe = tf.expand_dims(self.product_embedding(product_x), 0)
all_pe = tf.expand_dims(self.product_embedding.embeddings, 0)#note this only works if the layer has been built!
scores = tf.reshape(self.dot([pe, all_pe]), [-1])
top_scores, top_indices = tf.math.top_k(scores, k=100)
top_ids = tf.gather(self.products, top_indices)
return top_ids, top_scores
供参考
dummy_users
array(['pmfkU4BNZhmtLgJQwJ7x', 'UDRRwOlzlWVbu7H8YCCi',
'QHGAef0TI6dhn0wTogvW', ..., 'lcORJ5hemOZc1iGo9z7k',
'5CqDquDAszqJp27P7AL8', 'SSPNYxJMfuKhoe1dg24m'], dtype='<U20')
和
products
array([ 8650774, 9306139, 9961521, ..., 12058614, 12058615, 11927550])
当我运行以下代码时
sr1=SimpleRecommender(dummy_users,products,15)
sr1([tf.constant([['lcORJ5hemOZc1iGo9z7k'],['QHGAef0TI6dhn0wTogvW']]),
tf.constant([[8650774, 9306139, 9961521],[12058614, 12058615, 11927550]])])
我收到这个错误
TypeErrorTraceback (most recent call last)<ipython-input-24-5cd8170aa0e4> in <module>()
1 sr1=SimpleRecommender(dummy_users,products,15)
2 sr1([tf.constant([['lcORJ5hemOZc1iGo9z7k'],['QHGAef0TI6dhn0wTogvW']]),
----> 3 tf.constant([[8650774, 9306139, 9961521],[12058614, 12058615, 11927550]])])
1 frames
<ipython-input-21-1cc8d8700b6c> in call(self, inputs)
15 products=inputs[1]
16 user_embedding_index=self.dummy_user_table.lookup(user)
---> 17 prod_embedding_index=self.dummy_user_table.lookup(products)
18
19 user_embedding_values=self.user_embedding(user_embedding_index)
TypeError: Exception encountered when calling layer "simple_recommender" (type SimpleRecommender).
Dtype of argument `keys` must be <dtype: 'string'>, received: <dtype: 'int32'>
Call arguments received:
• inputs=['tf.Tensor(shape=(2, 1), dtype=string)', 'tf.Tensor(shape=(2, 3), dtype=int32)']
任何帮助都将不胜感激感谢
我认为您在call方法中使用了错误的查找表。尝试将其替换为：
def call(self, inputs):
user = inputs[0]
products = inputs[1]
user_embedding_index = self.dummy_user_table.lookup(user)
prod_embedding_index = self.product_table.lookup(products)
user_embedding_values = self.user_embedding(user_embedding_index)
prod_embedding_values = self.product_embedding(prod_embedding_index)
return self.dot([user_embedding_values, prod_embedding_values])
整个工作示例：
import tensorflow as tf
class SimpleRecommender(tf.keras.Model):
def __init__(self, dummy_users, products,lenght_of_embedding):
super(SimpleRecommender, self).__init__()
self.products = tf.constant(products, dtype=tf.int32)
self.dummy_users = tf.constant(dummy_users, dtype=tf.string)
self.dummy_user_table = tf.lookup.StaticHashTable(tf.lookup.KeyValueTensorInitializer(self.dummy_users,range(len(dummy_users))), -1)
self.product_table = tf.lookup.StaticHashTable(tf.lookup.KeyValueTensorInitializer(tf.constant(self.products,dtype=tf.int32), range(len(products))), -1)
self.user_embedding = tf.keras.layers.Embedding(len(dummy_users), lenght_of_embedding)
self.product_embedding = tf.keras.layers.Embedding(len(products), lenght_of_embedding)
self.dot=tf.keras.layers.Dot(axes=-1)
def call(self,inputs):
user=inputs[0]
products=inputs[1]
user_embedding_index=self.dummy_user_table.lookup(user)
prod_embedding_index=self.product_table.lookup(products)
user_embedding_values=self.user_embedding(user_embedding_index)
prod_embedding_values=self.product_embedding(prod_embedding_index)
return self.dot([user_embedding_values,prod_embedding_values])
dummy_users = tf.constant(['lcORJ5hemOZc1iGo9z7k', 'UDRRwOlzlWVbu7H8YCCi','QHGAef0TI6dhn0wTogvW'])
products = tf.constant([ 8650774, 9306139, 9961521])
sr1=SimpleRecommender(dummy_users,products,15)
sr1([tf.constant([['lcORJ5hemOZc1iGo9z7k'],['QHGAef0TI6dhn0wTogvW']]),
tf.constant([[8650774, 9306139, 9961521],[8650774, 9306139, 9961521]])])