我尝试运行代码,但是发现Keras
的合并层存在问题。我正在使用python 3和keras
2.2.4
这是代码的解码部分
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import LSTM, Embedding, TimeDistributed, Dense, RepeatVector, Merge, Activation
from keras.preprocessing import image, sequence
import cPickle as pickle
def create_model(self, ret_model = False):
image_model = Sequential()
image_model.add(Dense(EMBEDDING_DIM, input_dim = 4096, activation='relu'))
image_model.add(RepeatVector(self.max_length))
lang_model = Sequential()
lang_model.add(Embedding(self.vocab_size, 256, input_length=self.max_length))
lang_model.add(LSTM(256,return_sequences=True))
lang_model.add(TimeDistributed(Dense(EMBEDDING_DIM)))
model = Sequential()
model.add(Merge([image_model, lang_model], mode='concat'))
model.add(LSTM(1000,return_sequences=False))
model.add(Dense(self.vocab_size))
model.add(Activation('softmax'))
print ("Model created!")
这是错误消息
from keras.layers import LSTM, Embedding, TimeDistributed, Dense, RepeatVector, Merge, Activation
ImportError: cannot import name 'Merge' from 'keras.layers'
答案 0 :(得分:0)
Merge
。相反,您需要使用Concatenate
层:
merged = Concatenate()([x1, x2])
或等效的功能接口concatenate
(以小写的c
开头):
merged = concatenate([x1,x2])
如果您对其他合并形式感兴趣,例如加法,减法等,则可以使用相关图层。有关合并图层的详细列表,请参见documentation。