You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
in jupyter notebook Examples, when applying integrated_gradient, "mask = inter_grad.get_mask(x[0])" might need to be changed to "mask = inter_grad.GetMask(x[0])" cuz from the current implementation, the class method get_mask is GetMask considering it needs to call the super class's method get_mask. So if call integraded_gradient's get mask method as get_mask just like what has been done in the Examples, it will return the exact same images as vanilla's get_mask.
visual_propagation.py, line 15, "outs = [layer.output for layer in model.layers]" might need to be changed to "outs = [layer.output for layer in model.layers][1:]" to ensure the input layer is different from output layer.
The text was updated successfully, but these errors were encountered:
in jupyter notebook Examples, when applying integrated_gradient, "mask = inter_grad.get_mask(x[0])" might need to be changed to "mask = inter_grad.GetMask(x[0])" cuz from the current implementation, the class method get_mask is GetMask considering it needs to call the super class's method get_mask. So if call integraded_gradient's get mask method as get_mask just like what has been done in the Examples, it will return the exact same images as vanilla's get_mask.
visual_propagation.py, line 15, "outs = [layer.output for layer in model.layers]" might need to be changed to "outs = [layer.output for layer in model.layers][1:]" to ensure the input layer is different from output layer.
The text was updated successfully, but these errors were encountered: