I was looking at the activation maps of vgg19 in pytorch. I found that all the values of the maps are positive even before I applied the ReLU. This seems very strange to me... If this would be correct (could be that I not used the register_forward_hook method correctly?) why would one then apply ReLu at all? This is my code to produce this: import torch import torchvision import torchvision.models as models import torchvision.transforms as transforms from torchsummary import summary …
So I recently started learning about CNNs, and one question struck out to menthe filters used in the second layer are a combination of the filters used in the first layer, right? Lets say I make use of 4 filters in my first layer, and my second layer, I decide to combine any two, to give one filter, does this mean that during training, all I need to learn are the low level features, and it'll be propagated to the …
I've been trying to recreate LeNet 1(LeNet 1 architecture is pictured in the top diagram) in python using NumPy. I am unsure of how the forward pass works when there is multiple Input feature maps in a convolutional layer. Referring to the bottom diagram of the expanded view of convolutional layer C3 are the outputs calculated as follows?: O1 = Cross_correlation(I1 , F1) O2 = Cross_correlation(I1 , F2) O3 = Cross_correlation(I1 , F3) O4 = Cross_correlation(I2 , F4) O5 = …
I am currently working on a problem where the dataset contains 200+ features (Let's call them the code features, e.g no.of.loops, memoryInst, loadInst, etc and Flags that are used to compile code that has such characteristics/code features) The flags are represented as strings: This is just dummy data. snippet FlagsUsed no.of.loops loadInst memoryInst 1 Mergesort " -a -b -c -d=10 -e -f =19 -c " 1 0 10 2 Bubblesort " -a -c -f=230 " 2 5 3 3 MatrixMulti …