问题描述
我正在尝试训练一个 TF 模型，该模型将查看客户关于食品类别的购物车价值，并预测客户接下来会对哪个食品类别感兴趣。
只有 5 种食物类别：
>> all_food_categories
['pizza','side','dessert','drink','dip']
购物车中已有的食品类别是字符串类型的 RaggedTensor。
for i in generator(): is # generator returns a tuple,(features,target)
print(i)
break
(
{'categories_of_assumed_cart_flattened': <tf.RaggedTensor [[b'<blank>'],[b'pizza',b'dessert'],b'side',b'dip',b'dessert',b'pizza'],[b'side'],b'pizza',[b'pizza'],b'side'],[b'side',b'drink',b'dip'],b'drink'],[b'drink'],[b'<blank>'],b'pizza']]>,'disposition_type': <tf.Tensor: shape=(32,),dtype=string,numpy=
array([b'collection',b'delivery',b'collection',b'collection'],dtype=object)>},array([0,4,1,3,2,1]))
型号代码：
class NextItemCategory(tf.keras.Model):
def __init__(self,vocab,mask_token = '',embed_dim=4,conv_kernels=[3,5],max_seq_len = 7):
super(NextItemCategory,self).__init__()
self.mask_token = mask_token
self.max_seq_len = max_seq_len
self.lookup = tf.keras.layers.experimental.preprocessing.StringLookup(vocabulary=vocab,mask_token=mask_token)
self.embed = tf.keras.layers.Embedding(len(self.lookup.get_vocabulary()),4)
self.model_layers = [tf.keras.layers.Conv2D(filters=1,kernel_size=[ck_i,embed_dim],padding='same') for ck_i in conv_kernels]
self.pool = tf.keras.layers.GlobalMaxPool2D()
self.dense = tf.keras.layers.Dense(5,activation='softmax')
def call(self,inputs):
inp = inputs["categories_of_assumed_cart_flattened"]
x = inp.to_tensor(default_value='',shape = (None,self.max_seq_len))
x = self.lookup(x)
x = self.embed(x)
x = tf.expand_dims(input=x,axis=-1)
z = []
for layer in self.model_layers:
y = layer(x)
y = self.pool(y)
z.append(y)
z = tf.concat(z,axis=-1)
z = self.dense(z)
return z
cart_model = NextItemCategory(all_food_categories)
cart_model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),optimizer='adam',metrics=['accuracy'])
train_data_generator = generator()
cart_model.fit(train_data_generator,verbose=1)
模型正在构建中。
Model: "next_item_category"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
string_lookup_20 (StringLook multiple 0
_________________________________________________________________
embedding_20 (Embedding) multiple 28
_________________________________________________________________
conv2d_60 (Conv2D) multiple 13
_________________________________________________________________
conv2d_61 (Conv2D) multiple 17
_________________________________________________________________
conv2d_62 (Conv2D) multiple 21
_________________________________________________________________
global_max_pooling2d_20 (Glo multiple 0
_________________________________________________________________
dense_20 (Dense) multiple 20
=================================================================
Total params: 99
Trainable params: 99
Non-trainable params: 0
但作为 fit() 的一部分，我收到此错误：
AttributeError Traceback (most recent call last)
------
<ipython-input-110-5d3dc4a7c2fa> in call(self,inputs)
12
13 inp = inputs["categories_of_assumed_cart_flattened"]
---> 14 x = inp.to_tensor(default_value='',self.max_seq_len))
15 x = self.lookup(x)
16 x = self.embed(x)
AttributeError: 'Tensor' object has no attribute 'to_tensor'
在卷积开始之前，我已经尝试将 to_tensor() 调用放在不同的代码位置，但同样的错误仍然存在。
从生成器的输出可以清楚地看出，inputs['categories_of_assumed_cart_flattened'] 始终属于 tf.RaggedTensor 类型。
我不知道问题是什么；非常感谢任何帮助！非常感谢！
解决方法
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