'Tensor' object has no attribute 'ndim'

[YIsonP]

First, great Thanks to this masterpiece! It really saved my life when I found this toolkit. But I got some errors while I was trying to visualize my model via saliency map.
I think the problem is from the format of seed_input, I followed exactly the same way as the tutorial did, using one image, using img_to_array to process it, and passing the array to the seed_input, but some how seed_inputs were transformed into a tensor such that it has no attribute 'ndim', I tried to transform X into ndarray by K.eval(X), but so far it didn't work well. Many thanks for all the help!!

---> 94 seed_inputs = list(seed_inputs)
     95 total = (np.zeros_like(X[0]) for X in seed_inputs)
     96 for i in range(smooth_samples):

File ~\Anaconda3\lib\site-packages\tf_keras_vis\saliency.py:93, in <genexpr>(.0)
     89 seed_inputs = ((X, smooth_noise * (tf.math.reduce_max(X, axis=axis, keepdims=True) -
     90                                    tf.math.reduce_min(X, axis=axis, keepdims=True)))
     91                for X, axis in seed_inputs)
     92 print(seed_inputs)
---> 93 seed_inputs = (X + np.random.normal(0., sigma,X.shape) for X, sigma in seed_inputs)
     94 seed_inputs = list(seed_inputs)
     95 total = (np.zeros_like(X[0]) for X in seed_inputs)

File ~\Anaconda3\lib\site-packages\tf_keras_vis\saliency.py:89, in <genexpr>(.0)
     86 seed_inputs = (tf.reshape(X, (smooth_samples, -1) + tuple(X.shape[1:]))
     87                for X in seed_inputs)
     88 seed_inputs = ((X, tuple(range(K.eval(X).ndim)[2:])) for X in seed_inputs)
---> 89 seed_inputs = ((X, smooth_noise * (tf.math.reduce_max(X, axis=axis, keepdims=True) -
     90                                    tf.math.reduce_min(X, axis=axis, keepdims=True)))
     91                for X, axis in seed_inputs)
     92 print(seed_inputs)
     93 seed_inputs = (X + np.random.normal(0., sigma, X.shape) for X, sigma in seed_inputs)

File ~\Anaconda3\lib\site-packages\tf_keras_vis\saliency.py:88, in <genexpr>(.0)
     84 seed_inputs = (tf.tile(X, (smooth_samples, ) + tuple(np.ones(X.ndim - 1, np.int)))
     85                for X in seed_inputs)
     86 seed_inputs = (tf.reshape(X, (smooth_samples, -1) + tuple(X.shape[1:]))
     87                for X in seed_inputs)
---> 88 seed_inputs = ((X, tuple(range(X.ndim)[2:])) for X in seed_inputs)
     89 seed_inputs = ((X, smooth_noise * (tf.math.reduce_max(X, axis=axis, keepdims=True) -
     90                                    tf.math.reduce_min(X, axis=axis, keepdims=True)))
     91                for X, axis in seed_inputs)
     92 print(seed_inputs)

File ~\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py:446, in Tensor.__getattr__(self, name)
    437 if name in {"T", "astype", "ravel", "transpose", "reshape", "clip", "size",
    438             "tolist", "data"}:
    439   # TODO(wangpeng): Export the enable_numpy_behavior knob
    440   raise AttributeError(
    441       f"{type(self).__name__} object has no attribute '{name}'. " + """
    442     If you are looking for numpy-related methods, please run the following:
    443     from tensorflow.python.ops.numpy_ops import np_config
    444     np_config.enable_numpy_behavior()
    445   """)
--> 446 self.__getattribute__(name)

AttributeError: 'Tensor' object has no attribute 'ndim'

[keisen]

Hi, @YIsonP . Thank you for your reporting.

I tried to reproduce your problem, I didn't get the error.
Could you please the code snippet to reproduce it?
And please let me know the versions of tf-keras-vis, Tensorflow and Python.

Thanks!

[YIsonP]

Hi @keisen. Thanks for reply. here is my env:
tf-keras-vis:0.8.2
tensorflow:2.10.0
python: 3.9.12
and this is the link for the snippet :https://github.com/YIsonP/saliency-map
a simple bg: I use pre-trained model in Keras and try to visualize its decision by saliency map.

[keisen]

Hi, @YIsonP , Thank you for sharing the notebook.

The cause of the error is tf.compat.v1.disable_eager_execution() at 2nd cell in your notebook.
tf-keras-vis only support TF2.0+, but not TF1.x.
So if you want to visualize with the model built on TF1.x, I recommend you to use keras-vis.
If not, please use tf-keras-vis without tf.compat.v1.disable_eager_execution() .

Thanks!

[YIsonP]

Hi, @YIsonP , Thank you for sharing the notebook.

The cause of the error is tf.compat.v1.disable_eager_execution() at 2nd cell in your notebook. tf-keras-vis only support TF2.0+, but not TF1.x. So if you want to visualize with the model built on TF1.x, I recommend you to use keras-vis. If not, please use tf-keras-vis without tf.compat.v1.disable_eager_execution() .

Thanks!

Hi @keisen
It's been super helpful. Thank you so much for the help!!

[keisen]

Anytime!