我正在尝试训练搜索和项目编码器，这是我拥有的模型
input_search = Input(shape=(40,), dtype='int64', name='input_search')
input_title = Input(shape=(40,), dtype='int64', name='input_title')
input_desc = Input(shape=(40,), dtype='int64', name='input_desc')
input_brand = Input(shape=(40,), dtype='int64', name='input_brand')
embedding = Embedding(input_dim=20000, output_dim=50, input_length=40)
s_emb = embedding(input_search)
t_emb = embedding(input_title)
d_emb = embedding(input_desc)
b_emb = embedding(input_brand)
s = GlobalMaxPool1D()(s_emb)
t = GlobalMaxPool1D()(t_emb)
d = GlobalMaxPool1D()(d_emb)
b = GlobalMaxPool1D()(b_emb)
concat = concatenate([t, d, b])
concat = Dense(128)(concat)
s = Dense(128, name='vec')(s)
similarity = Lambda(cos_sim)([s, concat])
model = Model(inputs=[input_search, input_desc, input_brand, input_title], outputs=similarity)
_______________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_search (InputLayer) [(None, 40)] 0
__________________________________________________________________________________________________
input_title (InputLayer) [(None, 40)] 0
__________________________________________________________________________________________________
input_desc (InputLayer) [(None, 40)] 0
__________________________________________________________________________________________________
input_brand (InputLayer) [(None, 40)] 0
__________________________________________________________________________________________________
embedding_1 (Embedding) (None, 40, 50) 1526100 input_search[0][0]
input_title[0][0]
input_desc[0][0]
input_brand[0][0]
__________________________________________________________________________________________________
global_max_pooling1d_5 (GlobalM (None, 50) 0 embedding_1[1][0]
__________________________________________________________________________________________________
global_max_pooling1d_6 (GlobalM (None, 50) 0 embedding_1[2][0]
__________________________________________________________________________________________________
global_max_pooling1d_7 (GlobalM (None, 50) 0 embedding_1[3][0]
__________________________________________________________________________________________________
global_max_pooling1d_4 (GlobalM (None, 50) 0 embedding_1[0][0]
__________________________________________________________________________________________________
concatenate_5 (Concatenate) (None, 150) 0 global_max_pooling1d_5[0][0]
global_max_pooling1d_6[0][0]
global_max_pooling1d_7[0][0]
__________________________________________________________________________________________________
search (Dense) (None, 128) 6528 global_max_pooling1d_4[0][0]
__________________________________________________________________________________________________
product (Dense) (None, 128) 19328 concatenate_5[0][0]
__________________________________________________________________________________________________
lambda (Lambda) (None,) 0 search[0][0]
product[0][0]
==================================================================================================
Total params: 1,551,956
Trainable params: 1,551,956
Non-trainable params: 0
__________________________________________________________________________________________________
项目具有3个功能-标题，描述和品牌。我想在所有4个输入之间使用相同的嵌入层，以在相同的向量空间中生成嵌入。但是，对于预测时间，我想删除该项目的3个输入，并使模型的输出成为名为“ vec”的密集层的嵌入向量，以存储特征向量。这是我尝试过的：
search_model = Model(model.inputs[0], model.layers[-2].output)
item_model = Model(inputs=model.inputs[1:], outputs=model.layers[-1].output)
但是我得到了错误
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_brand_1:0", shape=(None, 40), dtype=int64) at layer "input_brand". The following previous layers were accessed without issue: []
我是否可以在搜索和项目特征之间共享嵌入层权重，从而使我可以修剪某些层来推断时间？还是创建2个单独的嵌入层，其中1个通过搜索运行，另一个通过该商品的3个功能运行，但以某种方式使两层的权重保持不变？