我正在尝试使用AdditiveAttention Keras层在TensorFlow 2.0中创建分层注意力。我得到的错误：
ValueError：图已断开连接：无法在“ question_input”层获取张量Tensor（“ question_input：0”，shape =（None，None），dtype = float32）的值。可以顺利访问以下先前的图层：[]
有人可以告诉我我做错了什么吗？
def get_text_model(self, embedding):
print("Text Input")
text_input = Input(shape=(None,), name="text_input")
text_embedding = embedding(text_input)
cnn_1d = Conv1D(128, 4, padding="same", activation="relu", strides=1)(text_embedding)
output = cnn_1d
model = Model(text_input, output)
return model
def get_sentence_attention_model(self, sentence_input, encoded_question, sentence_model):
encoded_sentence = sentence_model(sentence_input)
sentence_attention = AdditiveAttention()([encoded_sentence, encoded_question])
output = Concatenate()([sentence_attention, encoded_question])
model = Model(sentence_input, output)
return model
def get_section_model(self, encoded_question, sentence_model):
section_input = Input(shape=(None, None), name="section_input")
section_encoded = TimeDistributed(sentence_model)([self.question_input, section_input])
cnn_1d = Conv1D(128, 4, padding="same", activation="relu", strides=1)(section_encoded)
output = cnn_1d
section_attention_output = AdditiveAttention()([output, encoded_question])
model = Model(section_input, section_attention_output)
return model
def get_document_model(self, encoded_question, section_model):
document_input = Input(shape=(None, None, None), name="document_input")
document_encoded = TimeDistributed(section_model)(document_input)
cnn_1d = Conv1D(128, 4, padding="same", activation="relu", strides=1)(document_encoded)
document_attention = AdditiveAttention()([cnn_1d, encoded_question])
model = Model(document_input, document_attention)
return model
def get_model(self):
self.vocabulary_size = self.vectorizer.get_vocabulary_size()
self.embedding_matrix = self.vectorizer.get_embedding_matrix()
embedding = Embedding(self.vocabulary_size, self.embedding_size, mask_zero=True, trainable=True,
weights=None if self.embedding_matrix is None else [self.embedding_matrix])
self.question_input = Input(shape=(None,), name="question_input")
self.sentence_input = Input(shape=(None,), name="sentence_input")
self.question_model = self.get_text_model(embedding)
self.sentence_model = self.get_text_model(embedding)
self.encoded_question = self.question_model(self.question_input)
self.sentence_attention_model = self.get_sentence_attention_model(self.sentence_input, self.encoded_question, self.sentence_model)
self.section_model = self.get_section_model(self.encoded_question, self.sentence_attention_model)
self.document_model = self.get_document_model(self.encoded_question, self.section_model)
optimizer = Adadelta()
loss_metrics = "binary_crossentropy"
self.document_model.compile(loss=loss_metrics, optimizer=optimizer, metrics=[loss_metrics])
self.document_model.summary()