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值，并预测客户接下来会感兴趣的食品类别。
食物种类只有5种：
>> all_food_categories
['pizza', 'side', 'dessert', 'drink', 'dip']
购物车中已有的食物类别是RaggedTensor字符串类型。
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'pizza', b'side', b'side', b'dip', b'dip', b'dessert', b'dessert'],
[b'pizza', b'pizza'], [b'side'], [b'pizza', b'pizza', b'pizza', b'side', b'pizza'], [b'pizza'], [b'pizza', b'pizza', b'pizza', b'side'], [b'side', b'drink', b'side', b'dessert'], [b'side'], [b'pizza', b'side', b'side', b'dessert', b'dip', b'dip'], [b'pizza', b'pizza', b'pizza', b'pizza'], [b'pizza', b'drink', b'dip', b'dip'], [b'pizza', b'side', b'side'], [b'pizza', b'pizza', b'side'], [b'pizza', b'side'], [b'side'], [b'side', b'drink'], [b'pizza', b'side'], [b'pizza', b'side', b'side', b'dessert', b'drink'], [b'drink'], [b'drink'], [b'pizza', b'pizza', b'pizza'], [b'side', b'dip'], [b'side', b'side', b'side'], [b'<blank>'], [b'pizza', b'dip', b'dip', b'side'], [b'pizza', b'side', b'dessert'], [b'pizza'], [b'<blank>'], [b'pizza', b'pizza', b'side', b'side', b'dip', b'dip'], [b'pizza', b'pizza']]>, 'disposition_type': <tf.Tensor: shape=(32,), dtype=string, numpy=
array([b'collection', b'delivery', b'collection', b'collection',
b'delivery', b'delivery', b'collection', b'delivery', b'delivery',
b'delivery', b'delivery', b'delivery', b'delivery', b'delivery',
b'delivery', b'delivery', b'collection', b'delivery', b'delivery',
b'delivery', b'delivery', b'collection', b'collection',
b'delivery', b'delivery', b'delivery', b'delivery', b'delivery',
b'delivery', b'delivery', b'delivery', b'collection'], dtype=object)>}, array([0, 0, 4, 1, 3, 3, 4, 1, 1, 1, 0, 4, 1, 2, 2, 1, 0, 0, 2, 0, 0, 0,
4, 0, 0, 0, 1, 1, 1, 0, 1, 1]))
型号代码：
class NextItemCategory(tf.keras.Model):
def __init__(self, vocab, mask_token = '', embed_dim=4, conv_kernels=[3,4,5], 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
但也作为fit()我收到此错误的一部分：
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='', shape = (None, 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。
从生成器输出可以清楚地看出，inputs['categories_of_assumed_cart_flattened']始终是类型tf.RaggedTensor。
我不知道问题是什么；非常感谢任何帮助！提前谢谢了！