Update README.md

Author: RohinSequeira

README.md

@@ -7,7 +7,7 @@ Let's look into some more advanced concepts.
 ### Grad-CAM: Gradient-weighted Class Activation Mapping
 Convolutional Neural Network (CNN)-based models can be made more transparent by visualizing the regions of input that are "important" for predictions from these models - or visual explanations. Gradient-weighted Class Activation Mapping (Grad-CAM), uses the class-specific gradient information flowing into the final convolutional layer of a CNN to produce a coarse localization map of the important regions in the image.
 
-![image](/Output%20Images%20and%20Logs/gradcam_flow.JPG)
+![image](/images_and_logs/gradcam_flow.JPG)
 
 Gradient-weighted Class Activation Mapping (GradCAM) uses the gradients of any target concept (say logits for 'dog' or even a caption), flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. We take the final convolutional feature map, and then we weigh every channel in that feature with the gradient of the class with respect to the channel. It tells us how intensely the input image activates different channels by how important each channel is with regard to the class. It does not require any re-training or change in the existing architecture.