嵌入层中的Keras -掩蔽零填充

Question

目标是将每个索引转换为嵌入向量，然后将所有向量平均为单个向量。我必须忽略填充零！

平均向量应转移到下一层。

这是我的密码：

from keras.layers import Embedding,Input,AveragePooling1D
from keras.models import Model
from keras.preprocessing.text import Tokenizer as Keras_Tokenizer
from keras.preprocessing.sequence import pad_sequences
import numpy as np


embedding_size = 4
vocab_size = 9 + 1
max_sequence_length = 5 


my_input = Input(shape=(max_sequence_length,), name='input')
embedding_layer = Embedding(output_dim=embedding_size, input_dim=vocab_size,input_length=1, mask_zero=True,name='my_embedding')
embedding = embedding_layer(my_input)



avg=AveragePooling1D(pool_size=max_sequence_length)(embedding)#Calc average off all embedding vectors
model = Model(inputs= [my_input], outputs=avg)
model.get_weights() 



aa = np.array([[0,0,2,4]])#sanity checks
model.predict(aa)[0][0] 

并得到这个错误：

TypeError: Layer average_pooling1d_1 does not support masking, but was passed an input_mask: Tensor("my_embedding_9/NotEqual:0", shape=(?, 5), dtype=bool)

有人能帮忙吗？

Answer 1

您的用例与此one密切相关。

它将使您编写一个自定义层，以支持GlobalAveragePooling1D或AveragePooling1D中的掩蔽。