【发布时间】：2020-08-07 23:37:31
【问题描述】：
我按照blog(grad-CAM - vanilla,guided,rectified)了解卷积神经网络的注意力机制。该博客使用预训练的 ResNet 进行示例演示。
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还是不同。其中，上述博文中模型只有一个输入（用于分类）。
我在传递多个输入时遇到问题。
我的模型架构如下：
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]
==================================================================================================
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，然后网络的其余部分决定图像是否相同。
我已经尝试了以下代码：
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>.
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。或者，如果可能的话，甚至修改网络架构以一张一张地处理图像。我只想可视化网络正在聚焦的给定图像。
【问题讨论】：
标签： keras conv-neural-network visualization feature-detection