目标是将每个索引转换为嵌入式向量，然后将所有向量平均为一个。 我必须忽略填充的零！
平均向量应转移到下一层。
这是我的代码：
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]
并收到此错误：
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)
任何人都可以协助吗？