使用带有两个模型输入的Keras®cam

为了理解卷积神经网络的注意机制，我遵循了（grad-CAM-vanilla，guided，rectived）学习方法。博客使用预先训练过的ResNet来演示示例

我有一个自定义模型，它接受两个输入图像，并决定图像是相同的还是不同的。其中，上述博客文章模型只有一个输入（用于分类）

我面临传递多个输入的问题

我的模型架构如下所示：

Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, 256, 256, 3)  0                                            
__________________________________________________________________________________________________
input_2 (InputLayer)            (None, 256, 256, 3)  0                                            
__________________________________________________________________________________________________
encoder (Sequential)            (None, 7, 7, 256)    3752704     input_1[0][0]                    
                                                                 input_2[0][0]                    
__________________________________________________________________________________________________
Merged_feature_map (Concatenate (None, 7, 7, 512)    0           encoder[1][0]                    
                                                                 encoder[2][0]                    
__________________________________________________________________________________________________
mnet_conv1 (Conv2D)             (None, 7, 7, 1024)   2098176     Merged_feature_map[0][0]         
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 7, 7, 1024)   4096        mnet_conv1[0][0]                 
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 7, 7, 1024)   0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
mnet_pool1 (MaxPooling2D)       (None, 3, 3, 1024)   0           activation_1[0][0]               
__________________________________________________________________________________________________
mnet_conv2 (Conv2D)             (None, 3, 3, 2048)   8390656     mnet_pool1[0][0]                 
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 3, 3, 2048)   8192        mnet_conv2[0][0]                 
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 3, 3, 2048)   0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
mnet_pool2 (MaxPooling2D)       (None, 1, 1, 2048)   0           activation_2[0][0]               
__________________________________________________________________________________________________
reshape_1 (Reshape)             (None, 1, 2048)      0           mnet_pool2[0][0]                 
__________________________________________________________________________________________________
fc1 (Dense)                     (None, 1, 256)       524544      reshape_1[0][0]                  
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 1, 256)       1024        fc1[0][0]                        
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 1, 256)       0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 1, 256)       0           activation_3[0][0]               
__________________________________________________________________________________________________
fc2 (Dense)                     (None, 1, 128)       32896       dropout_1[0][0]                  
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 1, 128)       512         fc2[0][0]                        
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 1, 128)       0           batch_normalization_4[0][0]      
__________________________________________________________________________________________________
dropout_2 (Dropout)             (None, 1, 128)       0           activation_4[0][0]               
__________________________________________________________________________________________________
fc3 (Dense)                     (None, 1, 64)        8256        dropout_2[0][0]                  
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 1, 64)        256         fc3[0][0]                        
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 1, 64)        0           batch_normalization_5[0][0]      
__________________________________________________________________________________________________
dropout_3 (Dropout)             (None, 1, 64)        0           activation_5[0][0]               
__________________________________________________________________________________________________
fc4 (Dense)                     (None, 1, 1)         65          dropout_3[0][0]                  
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 1, 1)         4           fc4[0][0]                        
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 1, 1)         0           batch_normalization_6[0][0]      
__________________________________________________________________________________________________
dropout_4 (Dropout)             (None, 1, 1)         0           activation_6[0][0]               
__________________________________________________________________________________________________
reshape_2 (Reshape)             (None, 1)            0           dropout_4[0][0]                  
==================================================================================================

该模型采用两个输入，并使用编码器网络提取特征。两个特征合并后，网络的其余部分决定图像是否相同

我尝试了以下代码：

import numpy as np
from keras.models import load_model
import keras.backend as K
import matplotlib.cm as cm
from vis.utils import utils
from vis.visualization import visualize_cam

model = load_model('model.h5', compile=False)

img1 = utils.load_img('/path/image1.jpg', target_size=(256, 256))
img2 = utils.load_img('/path/images2.jpg', target_size=(256, 256))

penultimate_layer = utils.find_layer_idx(model, 'mnet_conv2')

layer_idx = utils.find_layer_idx(model, 'fc4')

for i, img in enumerate([img1, img2]):
  grads = visualize_cam(model, 
                        layer_idx, 
                        filter_indices=1, 
                        seed_input=img, 
                        penultimate_layer_idx=penultimate_layer)

我收到以下错误：

ValueError: slice index 1 of dimension 2 out of bounds. for 'strided_slice' (op: 'StridedSlice') with input shapes: [?,1,1], [3], [3], [3] and with computed input tensors: input[1] = <0 0 1>, input[2] = <0 0 2>, input[3] = <1 1 1>.

ValueError:维度2的切片索引1超出范围。对于输入形状为[？，1,1]，[3]，[3]，[3]，[3]且具有计算输入张量的“步态切面”（op:“步态切面”）：输入[1]=，输入[2]=，输入[3]=。

---

我正在寻找一种方法来传递两个图像，并在两个图像上绘制热图。甚至，如果可能的话，修改网络架构以逐个处理图像。我只想想象一幅给定的图像，网络聚焦的地方

对于梯度凸轮，必须将激活函数转换为线性

layer_idx = utils.find_layer_idx(model,'fc4')
model.layers[layer_idx].activation = keras.activations.linear
model = utils.apply_modifications(model)
penultimate_layer = utils.find_layer(model, 'mnet_conv2')