资源优化排名网站,郑州集团网站建设,wordpress安装虚拟主机,frp做网站CEMExplainer: MNIST Example 这一部分屁话有点多#xff0c;导包没问题的话可以跳过加载MNIST数据集加载经过训练的MNIST模型加载经过训练的卷积自动编码器模型#xff08;可选#xff09;初始化CEM解释程序以解释模型预测解释输入实例获得相关否定#xff08;Pertinent N… CEMExplainer: MNIST Example 这一部分屁话有点多导包没问题的话可以跳过加载MNIST数据集加载经过训练的MNIST模型加载经过训练的卷积自动编码器模型可选初始化CEM解释程序以解释模型预测解释输入实例获得相关否定Pertinent Negative,PN解释获得相关的肯定Pertinent Positive,PP解释相关负PN和相关正PP解释图 CEMBexplainer:MNIST示例
本文了如何使用AIX360的CEMBexplainer来获得对比解释的示例即对MNIST数据训练的模型所做预测的相关否定PN和相关肯定PP。CEMBexplainer是对比解释方法的一种实现。此案例使用经过训练的模型这些模型可从aix360/models/CEM/文件夹访问。
官方代码在https://github.com/Trusted-AI/AIX360/blob/master/examples/contrastive/CEM-MNIST.ipynb
这一部分屁话有点多导包没问题的话可以跳过
pip install keraspip install --user tensorflowimport os
import sys
from keras.models import model_from_json
from PIL import Image
from matplotlib import pyplot as plt
import numpy as npfrom aix360.algorithms.contrastive import CEMExplainer, KerasClassifier
from aix360.datasets import MNISTDataset经典一步一bug眼睛一睁一闭休眠升天修仙。。。 TensorFlow 2.0中contrib被弃用尝试安装旧版tensorflow
conda install tensorflow1.14.0看到这我真的高兴坏了之前不小心把python版本装高了没办法就是这么倒霉推倒重来官网怎么喜欢用那么老的版本为什么我的眼里常含泪水因为对知识爱得深沉。。。
重新创建个虚拟环境 python3.6python3.6python3.6python3.6python3.6python3.6python3.6python3.6python3.6python3.6python3.6python3.6 https://blog.csdn.net/weixin_45735391/article/details/133197625 python3.6python3.6python3.6python3.6python3.6python3.6python3.6python3.6python3.6python3.6python3.6python3.6
清华源似乎没有这个古老的版本。。。 emmmm又是一个坑。。。
python3.7python3.7python3.7python3.7python3.7python3.7python3.7python3.7python3.7python3.7python3.7python3.7 https://blog.csdn.net/weixin_45735391/article/details/133197625 python3.7python3.7python3.7python3.7python3.7python3.7python3.7python3.7python3.7python3.7python3.7python3.7 此倒霉蛋已疯。。。
tensorflow装好了又多活了一天欧耶 可是 此人g了。。。那就pip吧。。。
pip install aix360看着它那么红就让它红这吧。。。
人生嘛惊喜不断不然多无聊哈哈哈。。。
pip install skimagepip install scikit-image还差亿点点。。。
conda install pytorch还差亿点点。。。
conda install requests不想看见这坨警告的话可以加上
import warnings
warnings.filterwarnings(ignore)好了导包这块终于结束了。 又多活了一天真不错今天是个好日子。。。
加载MNIST数据集
# load MNIST data and normalize it in the range [-0.5, 0.5]
data MNISTDataset()花的时间有亿点点久。。。真的等不下去了。。。脑子已经在修仙了。。。 看看源码写的啥
class MNISTDataset():def __init__(self, custom_preprocessingNone, dirpathNone): self._dirpath dirpathif not self._dirpath:self._dirpath os.path.join(os.path.dirname(os.path.abspath(__file__)),.., data,mnist_data)files [train-images-idx3-ubyte.gz,t10k-images-idx3-ubyte.gz,train-labels-idx1-ubyte.gz,t10k-labels-idx1-ubyte.gz]for name in files:if not os.path.exists(self._dirpath / name):print(retrieving file, name)urllib.request.urlretrieve(http://yann.lecun.com/exdb/mnist/ name, self._dirpath / name)print(retrieved)train_data extract_data(self._dirpath /train-images-idx3-ubyte.gz, 60000)train_labels extract_labels(self._dirpath /train-labels-idx1-ubyte.gz, 60000)self.test_data extract_data(self._dirpath /t10k-images-idx3-ubyte.gz, 10000)self.test_labels extract_labels(self._dirpath /t10k-labels-idx1-ubyte.gz, 10000)VALIDATION_SIZE 5000self.validation_data train_data[:VALIDATION_SIZE, :, :, :]self.validation_labels train_labels[:VALIDATION_SIZE]self.train_data train_data[VALIDATION_SIZE:, :, :, :]self.train_labels train_labels[VALIDATION_SIZE:]直接百度搜一下下载MNIST数据集找到别人分享的资源把这四个文件[train-images-idx3-ubyte.gz,t10k-images-idx3-ubyte.gz,train-labels-idx1-ubyte.gz, t10k-labels-idx1-ubyte.gz]下载下来。
然后代码改一下dirpath就是那四个文件的保存路径。
dirpath rC:\Users\gxx\Desktop\jupter\aix360\MNIST
data MNISTDataset(dirpathdirpath)# print the shape of train and test data
print(MNIST train data range :, (, np.min(data.train_data), ,, np.max(data.train_data), ))
print(MNIST test data range :, (, np.min(data.train_data), ,, np.max(data.train_data), ))
print(MNIST train data shape :, data.train_data.shape)
print(MNIST test data shape :, data.test_data.shape)
print(MNIST train labels shape:, data.test_labels.shape)
print(MNIST test labels shape :, data.test_labels.shape)输出结果
MNIST train data range : ( -0.5 , 0.5 )
MNIST test data range : ( -0.5 , 0.5 )
MNIST train data shape : (55000, 28, 28, 1)
MNIST test data shape : (10000, 28, 28, 1)
MNIST train labels shape: (10000, 10)
MNIST test labels shape : (10000, 10)加载经过训练的MNIST模型
此notebook使用经过训练的MNIST模型。此处提供了训练此模型的代码。请注意该模型输出logits并且不使用softmax函数。 把官网上的文件复制到本地改一下路径。
# path to mnist related models
# model_path ../../aix360/models/CEM
model_path rC:\Users\gxx\Desktop\jupter\aix360\CEMdef load_model(model_json_file, model_wt_file):# read model json filewith open(model_json_file, r) as f:model model_from_json(f.read())# read model weights filemodel.load_weights(model_wt_file)return model# load MNIST model using its json and wt files
mnist_model load_model(os.path.join(model_path, mnist.json), os.path.join(model_path, mnist))# print model summary
mnist_model.summary()不出意外bug又来了。。。 在安装 tensorflow 时默认安装 h5py 为3.7.0而报错是因为安装的 TF 不支持过高版本的 h5py。 卸 载 h5py 3.7.0版本安装 h5py 2.10.0 版本。
pip uninstall --user h5py
pip install --user h5py2.10.0结果输出
加载经过训练的卷积自动编码器模型可选
这个notebook使用了一个经过训练的卷积自动编码器模型。此处提供了训练此模型的代码。
# load the trained convolutional autoencoder model
ae_model load_model(os.path.join(model_path, mnist_AE_1_decoder.json), os.path.join(model_path, mnist_AE_1_decoder.h5))
# print model summary
ae_model.summary()初始化CEM解释程序以解释模型预测
# wrap mnist_model into a framework independent class structure
mymodel KerasClassifier(mnist_model)# initialize explainer object
explainer CEMExplainer(mymodel)解释输入实例
# choose an input image
image_id 340
input_image data.test_data[image_id]# rescale values from [-0.5, 0.5] to [0, 255] for plotting
plt.imshow((input_image[:,:,0] 0.5)*255, cmapgray)# check model prediction
print(Predicted class:, mymodel.predict_classes(np.expand_dims(input_image, axis0)))
print(Predicted logits:, mymodel.predict(np.expand_dims(input_image, axis0)))结果输出 观察结果
尽管上面的图像被模型分类为数字3但是由于它与数字5具有相似性所以它也可以被分类为数字5。我们现在使用AIX360的CEMBexplainer来计算相关的正面和负面解释这有助于我们理解为什么图像被模型分类为数字3而不是数字5。
获得相关否定Pertinent Negative,PN解释
arg_mode PN # Find pertinent negativearg_max_iter 1000 # Maximum number of iterations to search for the optimal PN for given parameter settings
arg_init_const 10.0 # Initial coefficient value for main loss term that encourages class change
arg_b 9 # No. of updates to the coefficient of the main loss termarg_kappa 0.9 # Minimum confidence gap between the PNs (changed) class probability and original class probability
arg_beta 1.0 # Controls sparsity of the solution (L1 loss)
arg_gamma 100 # Controls how much to adhere to a (optionally trained) autoencoder
arg_alpha 0.01 # Penalizes L2 norm of the solution
arg_threshold 0.05 # Automatically turn off features arg_threshold if arg_threshold 1
arg_offset 0.5 # the model assumes classifier trained on data normalized# in [-arg_offset, arg_offset] range, where arg_offset is 0 or 0.5(adv_pn, delta_pn, info_pn) explainer.explain_instance(np.expand_dims(input_image, axis0), arg_mode, ae_model, arg_kappa, arg_b, arg_max_iter, arg_init_const, arg_beta, arg_gamma, arg_alpha, arg_threshold, arg_offset)结果输出
WARNING:tensorflow:From C:\Users\gxx\anaconda3\envs\tf-py37\lib\site-packages\aix360\algorithms\contrastive\CEM_aen.py:60: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.WARNING:tensorflow:From C:\Users\gxx\anaconda3\envs\tf-py37\lib\site-packages\aix360\algorithms\contrastive\CEM_aen.py:151: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.WARNING:tensorflow:From C:\Users\gxx\anaconda3\envs\tf-py37\lib\site-packages\aix360\algorithms\contrastive\CEM_aen.py:213: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.WARNING:tensorflow:From C:\Users\gxx\anaconda3\envs\tf-py37\lib\site-packages\tensorflow\python\keras\optimizer_v2\learning_rate_schedule.py:409: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
WARNING:tensorflow:From C:\Users\gxx\anaconda3\envs\tf-py37\lib\site-packages\aix360\algorithms\contrastive\CEM_aen.py:216: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead.WARNING:tensorflow:From C:\Users\gxx\anaconda3\envs\tf-py37\lib\site-packages\tensorflow\python\ops\math_grad.py:1250: add_dispatch_support.locals.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From C:\Users\gxx\anaconda3\envs\tf-py37\lib\site-packages\aix360\algorithms\contrastive\CEM_aen.py:230: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.iter:0 const:[10.]
Loss_Overall:2737.2244, Loss_Attack:58.5389
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:2678.685546875
target_lab_score:19.3967, max_nontarget_lab_score:14.4428iter:500 const:[10.]
Loss_Overall:2737.2244, Loss_Attack:58.5389
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:2678.685546875
target_lab_score:19.3967, max_nontarget_lab_score:14.4428iter:0 const:[100.]
Loss_Overall:3152.3984, Loss_Attack:0.0000
Loss_L2Dist:12.6054, Loss_L1Dist:16.5280, AE_loss:3123.264892578125
target_lab_score:9.0004, max_nontarget_lab_score:29.0375iter:500 const:[100.]
Loss_Overall:2977.4854, Loss_Attack:0.0000
Loss_L2Dist:7.0313, Loss_L1Dist:10.1030, AE_loss:2960.35107421875
target_lab_score:9.2486, max_nontarget_lab_score:28.5018iter:0 const:[55.]
Loss_Overall:2840.0422, Loss_Attack:0.0000
Loss_L2Dist:4.8674, Loss_L1Dist:7.2291, AE_loss:2827.94580078125
target_lab_score:9.7374, max_nontarget_lab_score:27.1471iter:500 const:[55.]
Loss_Overall:2670.4844, Loss_Attack:0.0000
Loss_L2Dist:0.8409, Loss_L1Dist:2.1313, AE_loss:2667.51220703125
target_lab_score:15.5937, max_nontarget_lab_score:19.4013iter:0 const:[32.5]
Loss_Overall:2644.0203, Loss_Attack:2.0429
Loss_L2Dist:0.5595, Loss_L1Dist:1.8527, AE_loss:2639.565185546875
target_lab_score:16.7141, max_nontarget_lab_score:17.5513iter:500 const:[32.5]
Loss_Overall:2868.9368, Loss_Attack:190.2513
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:2678.685546875
target_lab_score:19.3967, max_nontarget_lab_score:14.4428iter:0 const:[21.25]
Loss_Overall:2782.8979, Loss_Attack:117.1809
Loss_L2Dist:0.0176, Loss_L1Dist:0.2093, AE_loss:2665.490234375
target_lab_score:19.1928, max_nontarget_lab_score:14.5784iter:500 const:[21.25]
Loss_Overall:2803.0806, Loss_Attack:124.3951
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:2678.685546875
target_lab_score:19.3967, max_nontarget_lab_score:14.4428iter:0 const:[26.875]
Loss_Overall:2738.9089, Loss_Attack:91.5858
Loss_L2Dist:0.1530, Loss_L1Dist:0.9359, AE_loss:2646.234130859375
target_lab_score:18.1907, max_nontarget_lab_score:15.6829iter:500 const:[26.875]
Loss_Overall:2836.0088, Loss_Attack:157.3232
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:2678.685546875
target_lab_score:19.3967, max_nontarget_lab_score:14.4428iter:0 const:[24.0625]
Loss_Overall:2774.3594, Loss_Attack:117.5742
Loss_L2Dist:0.0524, Loss_L1Dist:0.4683, AE_loss:2656.2646484375
target_lab_score:18.8622, max_nontarget_lab_score:14.8760iter:500 const:[24.0625]
Loss_Overall:2819.5447, Loss_Attack:140.8591
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:2678.685546875
target_lab_score:19.3967, max_nontarget_lab_score:14.4428iter:0 const:[25.46875]
Loss_Overall:2754.6963, Loss_Attack:104.3005
Loss_L2Dist:0.0950, Loss_L1Dist:0.7232, AE_loss:2649.57763671875
target_lab_score:18.5058, max_nontarget_lab_score:15.3106iter:500 const:[25.46875]
Loss_Overall:2827.7766, Loss_Attack:149.0911
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:2678.685546875
target_lab_score:19.3967, max_nontarget_lab_score:14.4428iter:0 const:[24.765625]
Loss_Overall:2762.2129, Loss_Attack:109.3322
Loss_L2Dist:0.0725, Loss_L1Dist:0.6168, AE_loss:2652.191650390625
target_lab_score:18.6550, max_nontarget_lab_score:15.1403iter:500 const:[24.765625]
Loss_Overall:2823.6606, Loss_Attack:144.9751
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:2678.685546875
target_lab_score:19.3967, max_nontarget_lab_score:14.4428print(info_pn)结果输出
[INFO]kappa:0.9, Orig class:3, Perturbed class:5, Delta class: 1, Orig prob:[[-11.279339 0.73625 -9.008647 19.396711 -8.286123 14.442826 -1.3170443 -11.587322 -0.992185 1.0182207]], Perturbed prob:[[ -6.6607647 -1.9869652 -7.4231925 13.461045 -6.341817 13.8300295 1.2803447 -11.60892 0.31489015 1.1112802 ]], Delta prob:[[-0.11039171 1.0537697 -0.0954444 -0.2623107 -0.3357536 0.24241148 -0.0948096 -0.00691785 -0.31975082 -0.56200165]]获得相关的肯定Pertinent Positive,PP解释
arg_mode PP # Find pertinent positive
arg_beta 0.1 # Controls sparsity of the solution (L1 loss)
(adv_pp, delta_pp, info_pp) explainer.explain_instance(np.expand_dims(input_image, axis0), arg_mode, ae_model, arg_kappa, arg_b, arg_max_iter, arg_init_const, arg_beta, arg_gamma, arg_alpha, arg_threshold, arg_offset)结果输出 iter:0 const:[10.]
Loss_Overall:1186.7114, Loss_Attack:20.4772
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:1166.2342529296875
target_lab_score:-0.1036, max_nontarget_lab_score:1.0441iter:500 const:[10.]
Loss_Overall:1186.7114, Loss_Attack:20.4772
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:1166.2342529296875
target_lab_score:-0.1036, max_nontarget_lab_score:1.0441iter:0 const:[100.]
Loss_Overall:1374.8175, Loss_Attack:224.8764
Loss_L2Dist:0.0581, Loss_L1Dist:0.5667, AE_loss:1149.8262939453125
target_lab_score:-0.1908, max_nontarget_lab_score:1.1579iter:500 const:[100.]
Loss_Overall:1177.7847, Loss_Attack:0.0000
Loss_L2Dist:9.0615, Loss_L1Dist:26.9499, AE_loss:1166.0281982421875
target_lab_score:9.1723, max_nontarget_lab_score:5.3354iter:0 const:[55.]
Loss_Overall:1278.8588, Loss_Attack:112.6245
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:1166.2342529296875
target_lab_score:-0.1036, max_nontarget_lab_score:1.0441iter:500 const:[55.]
Loss_Overall:1278.8588, Loss_Attack:112.6245
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:1166.2342529296875
target_lab_score:-0.1036, max_nontarget_lab_score:1.0441iter:0 const:[77.5]
Loss_Overall:1324.9324, Loss_Attack:158.6981
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:1166.2342529296875
target_lab_score:-0.1036, max_nontarget_lab_score:1.0441iter:500 const:[77.5]
Loss_Overall:1324.9324, Loss_Attack:158.6981
Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:1166.2342529296875
target_lab_score:-0.1036, max_nontarget_lab_score:1.0441iter:0 const:[88.75]
Loss_Overall:1347.3350, Loss_Attack:190.4548
Loss_L2Dist:0.0195, Loss_L1Dist:0.2384, AE_loss:1156.8367919921875
target_lab_score:-0.1378, max_nontarget_lab_score:1.1082iter:500 const:[88.75]
Loss_Overall:1182.4167, Loss_Attack:0.0000
Loss_L2Dist:10.1261, Loss_L1Dist:29.5733, AE_loss:1169.3333740234375
target_lab_score:10.9503, max_nontarget_lab_score:8.5652iter:0 const:[83.125]
Loss_Overall:1336.9946, Loss_Attack:176.8078
Loss_L2Dist:0.0096, Loss_L1Dist:0.1385, AE_loss:1160.1634521484375
target_lab_score:-0.1352, max_nontarget_lab_score:1.0918iter:500 const:[83.125]
Loss_Overall:1177.7847, Loss_Attack:0.0000
Loss_L2Dist:9.0615, Loss_L1Dist:26.9499, AE_loss:1166.0281982421875
target_lab_score:9.1723, max_nontarget_lab_score:5.3355iter:0 const:[80.3125]
Loss_Overall:1330.7108, Loss_Attack:169.8772
Loss_L2Dist:0.0070, Loss_L1Dist:0.1182, AE_loss:1160.8148193359375
target_lab_score:-0.1306, max_nontarget_lab_score:1.0846iter:500 const:[80.3125]
Loss_Overall:1187.8037, Loss_Attack:0.0000
Loss_L2Dist:9.0935, Loss_L1Dist:26.5365, AE_loss:1176.0565185546875
target_lab_score:10.0619, max_nontarget_lab_score:2.9340iter:0 const:[78.90625]
Loss_Overall:1327.5865, Loss_Attack:166.4040
Loss_L2Dist:0.0058, Loss_L1Dist:0.1080, AE_loss:1161.1658935546875
target_lab_score:-0.1282, max_nontarget_lab_score:1.0807iter:500 const:[78.90625]
Loss_Overall:1176.6401, Loss_Attack:0.0000
Loss_L2Dist:8.3147, Loss_L1Dist:24.4263, AE_loss:1165.8828125
target_lab_score:8.1241, max_nontarget_lab_score:4.7113iter:0 const:[78.203125]
Loss_Overall:1326.0416, Loss_Attack:164.6752
Loss_L2Dist:0.0053, Loss_L1Dist:0.1030, AE_loss:1161.350830078125
target_lab_score:-0.1270, max_nontarget_lab_score:1.0788iter:500 const:[78.203125]
Loss_Overall:1180.0135, Loss_Attack:0.0000
Loss_L2Dist:9.0324, Loss_L1Dist:26.5381, AE_loss:1168.327392578125
target_lab_score:9.0967, max_nontarget_lab_score:5.0136print(info_pp)结果输出
[INFO]kappa:0.9, Orig class:3, Perturbed class:3, Delta class: 3, Orig prob:[[-11.279339 0.73625 -9.008647 19.396711 -8.286123 14.442826 -1.3170443 -11.587322 -0.992185 1.0182207]], Perturbed prob:[[ -6.0453925 -0.16173983 -6.025815 11.575153 -3.0273986 11.318211 4.259432 -11.328725 -1.0278873 -2.3766122 ]], Delta prob:[[-2.3122752 0.60199463 -0.6148693 4.709517 -2.2623286 1.0073487 -2.2190797 -0.83646446 -1.5357832 0.9802128 ]]相关负PN和相关正PP解释图
# rescale values from [-0.5, 0.5] to [0, 255] for plotting
fig0 (input_image[:,:,0] 0.5)*255fig1 (adv_pn[0,:,:,0] 0.5) * 255
fig2 (fig1 - fig0) #rescaled delta_pn
fig3 (adv_pp[0,:,:,0] 0.5) * 255
fig4 (delta_pp[0,:,:,0] 0.5) * 255 #rescaled delta_ppf, axarr plt.subplots(1, 5, figsize(10,10))
axarr[0].set_title(Original ( str(mymodel.predict_classes(np.expand_dims(input_image, axis0))[0]) ))
axarr[1].set_title(Original PN ( str(mymodel.predict_classes(adv_pn)[0]) ))
axarr[2].set_title(PN)
axarr[3].set_title(Original PP)
axarr[4].set_title(PP ( str(mymodel.predict_classes(delta_pp)[0]) ))axarr[0].imshow(fig0, cmapgray)
axarr[1].imshow(fig1, cmapgray)
axarr[2].imshow(fig2, cmapgray)
axarr[3].imshow(fig3, cmapgray)
axarr[4].imshow(fig4, cmapgray)
plt.show()结果输出 说明
PP突出显示图像中存在的最小像素集以便将其分类为数字3。注意原始图像和PP都被分类器分类为数字3。PN在顶部突出显示一条小水平线该水平线的存在会将原始图像的分类改变为数字5因此应该不存在以便分类保持为数字3。
