Error when using custom convolutional layer about tf-keras-vis HOT 4 CLOSED

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.