使用VGG16进行其他模型的特征提取 . 进入VGG16的输入来自输出灰度图像的神经网络 . 因此，我将VGG的输入替换为输入，接受1个通道图像，然后是连接层，使输入3个通道，以便卷积层可以对图像进行操作 .

vgg_16 = vgg16.VGG16（include_top = False，weights ='imagenet'，input_tensor =无）#vgg_16.layers中的图层训练时冻结VGG_16：layer.trainable = False

vgg_16.layers.pop(0)

inputs = Input(shape=(312, 312, 1))
concat = keras.layers.Concatenate(axis=3)([inputs] * 3)
outputs = vgg_16(concat)

vgg_first2 = Model(inputs, outputs)
vgg_first2.summary()

keras打印的摘要显示连接输出是预期的3个通道，

Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_4 (InputLayer)            (None, 312, 312, 1)  0                                            
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 312, 312, 3)  0           input_4[0][0]                    
                                                                 input_4[0][0]                    
                                                                 input_4[0][0]                    
__________________________________________________________________________________________________
vgg16 (Model)                   multiple             14714688    concatenate_1[0][0]              
==================================================================================================

最后我得到一个错误ValueError：输入通道的数量与过滤器的相应尺寸不匹配，9！= 3 .

最后一点，我使用keras和tensorflow后端

keras