Comments (4)
Thank you for your error reporting!
As you said, the problem should be fixed.
I believe that Gradcam#__call__
should be improved to have seek_penultimate_layer
argument that is a flag.
When seek_penultimate_layer
is False, _find_penultimate_output
would return the output that is indicated by penultimate_layer
argument.
That is, you won't need to comment out above codes.
class Gradcam(ModelVisualization):
def __call__(self,
loss,
seed_input,
penultimate_layer=-1,
seek_penultimate_layer=True,
activation_modifier=lambda cam: K.relu(cam),
normalize_gradient=True):
What do you think about this plan?
from tf-keras-vis.
would also be more than great to have an option to return
the non-interpolated grad-CAM
as for custom convolutional layers the interpolation may be special.
I couldn't understand the non-interpolated grad-CAM
.
Could you be more specific?
Thanks!
from tf-keras-vis.
Hi Keisen,
I just meant that it could be useful in some very specific cases to return the grad-CAM vectors before they undergo the interpolation step (Line 68 of gradcam.py).
Thanks for the fix, and thanks so much for sharing this code.
from tf-keras-vis.
I got it. I'll solve it that was opened issues/#8.
from tf-keras-vis.
Related Issues (20)
- 'NoneType' object has no attribute 'ndim' HOT 1
- AttributeError: 'NoneType' object has no attribute 'ndim' HOT 1
- 'Tensor' object has no attribute 'ndim' HOT 1
- scorecam seems to be weighting all feature maps equally
- How to output the visualization intermediate layer
- TypeError: '<=' not supported between instances of 'int' and 'str'
- Add a Citation widget to the repo HOT 1
- Feature request: Support Python 3.10 HOT 4
- if vision transformers are supported ?
- cannot use BinaryScore HOT 3
- How to use the package with one dimensional data? HOT 1
- Problems with TF 2.9 HOT 2
- Incorrect implementation of LayerCam HOT 1
- It does not work with models with custom layers. HOT 3
- Reconstruction Error Score? HOT 2
- 'Tensor' object has no attribute 'ndim' HOT 5
- Tutorials on using 3D data
- GradCAM does not detect correct convolutional layer in multi-input case HOT 1
- Is it possible to compute the full inference at the same time as the activations? HOT 2
- value_range param is ignored
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