Can't exploit gradients of ResNet-20 about ml_privacy_meter HOT 4 CLOSED

Hi @chris-prenode I will try the experiment with your ResNet model and get back to you.

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I forgot important information: My pre-trained ResNet model was created by TensorFlow version 2.4!

This could lead to the error.
I used the following code: https://github.com/sayakpaul/Sharpness-Aware-Minimization-TensorFlow/blob/main/resnet_cifar10.py
To create my ResNet20 model.

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I have the same problem. But I was trying to load pytorch tried model which had been converted to keras model.

File "/home/yang/gan/118/ml_privacy_meter/attackdc.py", line 1, in
import numpy as np
File "/home/yang/gan/118/ml_privacy_meter/ml_privacy_meter/attack/meminf.py", line 168, in init
self.create_attack_components(layers)
File "/home/yang/gan/118/ml_privacy_meter/ml_privacy_meter/attack/meminf.py", line 278, in create_attack_components
self.create_gradient_components(model, layers)
File "/home/yang/gan/118/ml_privacy_meter/ml_privacy_meter/attack/meminf.py", line 254, in create_gradient_components
module = cnn_for_fcn_gradients(shape)
File "/home/yang/gan/118/ml_privacy_meter/ml_privacy_meter/attack/meminf_modules/create_cnn.py", line 12, in cnn_for_fcn_gradients
dim2 = int(input_shape[1])
File "/home/yang/tools/anaconda3/envs/datacond/lib/python3.7/site-packages/tensorflow/python/framework/tensor_shape.py", line 887, in getitem
return self._dims[key].value
IndexError: list index out of range

I tried to reduce the value of 'gradients_to_exploit' to 1 and it worked.
I just notice that in 'cnn_for_fcn_gradients' it treat the variables in model as 2 or 3 dimentions. Actually, in my model, it just only 1 dimention. Only the first layer has 4 dimentions so I change 'gradients_to_exploit' to 1.

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Hi all, we have released a new version of our tool which works with the newer versions of TensorFlow and PyTorch. Hopefully this should resolve the gradient issue reported here.

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