merge - 如何在 tf.keras 中执行 Merge Sequential 获取“应在至少 2 个输入的列表上调用一个 `Concatenate` 层”

Question

我正在尝试合并 tf.keras 。什么是等价物。

model = tf.keras.Sequential()
model.add(tf.keras.layers.Concatenate()([sentrnn, qrnn]))

出现以下异常Concatenate应在至少 2 个输入的列表上调用层

试图转换我在其中一个站点上找到的代码

model.add(Merge([sentrnn, qrnn], mode='concat'))
model.add(Merge([sentrnn, qrnn], mode='sum'))


sentrnn = Sequential()
sentrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE, mask_zero=True))
sentrnn.add(RNN(SENT_HIDDEN_SIZE, return_sequences=False))
sentrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE, input_length=story_maxlen, mask_zero=True))
sentrnn.add(Dropout(0.3))

qrnn = Sequential()
qrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE))
qrnn.add(RNN(QUERY_HIDDEN_SIZE, return_sequences=False))
qrnn.add(Embedding(vocab_size, EMBED_HIDDEN_SIZE, input_length=query_maxlen))
qrnn.add(Dropout(0.3))
qrnn.add(RNN(EMBED_HIDDEN_SIZE, return_sequences=False))
qrnn.add(RepeatVector(story_maxlen))

model = Sequential()
model.add(Merge([sentrnn, qrnn], mode='concat'))
model.add(Merge([sentrnn, qrnn], mode='sum'))
model.add(RNN(EMBED_HIDDEN_SIZE, return_sequences=False))
model.add(Dropout(0.3))
model.add(Dense(vocab_size, activation='softmax'))

标签： mergetf.kerassequential