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carlini / nn_robust_attacks

Robust evasion attacks against neural network to find adversarial examples
BSD 2-Clause "Simplified" License
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Unsuccessful TensorSliceReader constructor #45

Open le000043 opened 3 years ago

le000043 commented 3 years ago 

Traceback (most recent call last):
  File "test_attack.py", line 69, in <module>
    data, model =  MNIST(), MNISTModel("models/mnist", sess)
  File "/Users/datle/Documents/Secure_machine_learning/SVM/Carlini/nn_robust_attacks/setup_mnist.py", line 89, in __init__
    model.load_weights(restore)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_v1.py", line 236, in load_weights
    return super(Model, self).load_weights(filepath, by_name, skip_mismatch)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 2199, in load_weights
    py_checkpoint_reader.NewCheckpointReader(filepath)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/training/py_checkpoint_reader.py", line 99, in NewCheckpointReader
    error_translator(e)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/training/py_checkpoint_reader.py", line 35, in error_translator
    raise errors_impl.NotFoundError(None, None, error_message)
tensorflow.python.framework.errors_impl.NotFoundError: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for models/mnist

I am using the latest python3, TensorFlow, and Keras. I changed tf.Session() --> tf.compat.v1.Session() at line 68, test_attack.py I changed tf.app.flags --> tf.compat.v1.flags at line 52,60, 65 and 67 in file setup_inception.py Please help !

carlini commented 3 years ago

The code is written for an old version of TF. If I remember right, keras recently changed the model storing setup so it has a folder instead of a single file. Downgrading your TF version is the easiest way to go for now.