I have been modified this source code for the SmallVggNet architecture presented in https://www.pyimagesearch.com/2018/04/16/keras-and-convolutional-neural-networks-cnns/
Then, I used the model with GradCam implementation for the Pokemon dataset to visualise the results. Hence, I have been changed some lines and successfully executing the script. The results obtained are presented in this repository:
'Charmander'
Gradient class activation maps are a visualization technique for deep learning networks.
See the paper: https://arxiv.org/pdf/1610.02391v1.pdf
The paper authors torch implementation: https://github.com/ramprs/grad-cam
This code assumes Tensorflow dimension ordering, and uses the VGG16 network in keras.applications by default (the network weights will be downloaded on first use).
Usage: python grad-cam.py <path_to_image>
Example image from the original implementation:
'boxer' (243 or 242 in keras)
'tiger cat' (283 or 282 in keras)
