keras：连接两个图像作为输入（DeepVO）

Question

我正在尝试实现本文中描述的模型。
我遇到的一个问题是设置输入，它应该是两个图像堆叠，这意味着，我有一组连续的（i

python prettyprint-override">import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Concatenate, Activation, Conv2D, MaxPooling2D, Flatten, Input
from keras.klayers import Embedding,LSTM 

inp1 = Input((2048,2048,1))
inp2 = Input((2048,2048,1))
deepVO = Sequential()
deepVO.add(Concatenate(inp1,inp2,-1))
deepVO.add(Conv2D(64,(2,2)))
deepVO.add(Activation('relu'))
#....continue to add other layers

我在deepVO_CNN. add（Concatenate（inp1， inp2，-1））得到的错误是：

TypeError：__init__（）需要1到2个位置参数，但给出了4个。




Answer 1

试试keras api模式，如下所示：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Input, concatenate

from keras.models import Model

inp1 = Input((2048,2048,1))
inp2 = Input((2048,2048,1))

deepVO = concatenate([inp1, inp2],axis=-1)
deepVO = Conv2D(64,(2,2))(deepVO)
deepVO = Activation('relu')(deepVO)
...

...
outputs = Dense(num_classes, activation='softmax')(deepVO)
deepVO = Model([inp1, inp2], outputs)
#deepVO.summary()