Neural networks have revolutionized the field of artificial intelligence, achieving state-of-the-art results in various domains like computer vision, natural language processing, and more. However, their complex nature often leaves us wondering: How do these models arrive at their decisions? Which parts of the input data influence the output the most?
The Need for Explainability
In many real-world applications, understanding the decision-making process of a neural network is crucial. For instance, in medical diagnosis, it’s essential to know why a model predicted a certain disease. In autonomous vehicles, it’s vital to understand how the model perceives its surroundings to make safe driving decisions.
Visualizing Neural Networks: A Deep Dive
To address this need for transparency, several techniques have emerged to visualize and interpret neural networks. Let’s explore two popular methods:
1. Saliency Maps
Saliency maps highlight the most important regions of an input image that contribute to a specific prediction. By visualizing these regions, we can gain insights into the model’s decision-making process.
Implementation with PyTorch:
import torch import torchvision.models as models import torchvision.transforms as transforms # Load a pre-trained model model = models.resnet18(pretrained=True).eval() # Load and preprocess an image img = Image.open('image.jpg') transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) img_tensor = transform(img).unsqueeze(0) # Get the model's output and gradients output = model(img_tensor) output.backward() # Extract the gradients and create a saliency map saliency_map = torch.abs(img_tensor.grad[0]).sum(dim=0)
2. Neural-Backed Decision Trees (NBDTs)
NBDTs provide a more interpretable representation of a neural network by breaking down its decision-making process into a series of simple rules. Each rule represents a decision node in a decision tree, making it easier to understand the model’s reasoning.
Implementation with PyTorch:
from nbdt.model import HardNBDT from torchvision import transforms # Load a pre-trained NBDT model model = HardNBDT(pretrained=True, dataset='CIFAR10', arch='wrn28_10_cifar10') # Load and preprocess an image # ... (same as above) # Get the model's output and decisions outputs, decisions = model.forward_with_decisions(img_tensor) # Print the prediction and decisions _, predicted = outputs.max(1) cls = DATASET_TO_CLASSES['CIFAR10'][predicted[0]] print('Prediction:', cls, '// Decisions:', ', '.join([ '{} ({:.2f}%)'.format(info['name'], info['prob'] * 100) for info in decisions[0] ][1:]))
Beyond Visualization: The Power of Explainable AI
By visualizing and interpreting neural networks, we can:
As AI continues to advance, explainable AI will play a crucial role in ensuring that these powerful models are used responsibly and ethically. By embracing these techniques, we can unlock the full potential of neural networks and build more transparent and trustworthy AI systems.
First, boot the system in the rescue kernel. I am assuming your boot partition is separated then the home partition, like in my case. Here are the list of commands I have used:
# mount /dev/md126 /mnt # mount /dev/md127 /mnt/boot # mount --bind /dev /mnt/dev # mount --bind /dev/pts /mnt/dev/pts # mount --bind /proc /mnt/proc # mount --bind /sys /mnt/sys # chroot /mnt # passwd
Now, make sure to reset the password properly. Once done. Umount all the partition and boot in regular mode:
# umount /mnt/boot # umount /mnt/sys # umount /mnt/proc # umount /mnt/dev/pts # umount /mnt/dev # umount /mnt # reboot
It should be it.
To mount qcow2, you need to mount this image as a ‘Network Block Device’.
First, enable NBD
modprobe nbd max_part=8
Now, connect the qcow2 as an NBD device
qemu-nbd --connect=/dev/nbd0 /vz/vmprivate/v1002/harddisk.hdd
Now to mount the partition, first detect the partition
fdisk /dev/nbd0 -l
Now, you may mount the partition
mount /dev/nbd0p1 /mnt
Once, all the jobs are done, you may unmount, disconnect, and remove the NBD kernel module
umount /mnt qemu-nbd --disconnect /dev/nbd0 rmmod nbd
Issue
KVM VM not starting with the following error when you try to start:
could not get access to acl tech driver 'ebiptables'
There is a nwfilter module for libvirt. If for some reason, it comes up with an issue, the above error would appear. To fix this, you need to update (If any update is available) / reinstall (If no update is available) the following module using Yum:
libvirt-daemon-config-nwfilter
The command would be like the following:
yum update libvirt-daemon-config-nwfilter
That shall fix the issue.
When I tried to load my Roundcube today, found that it failed to load the inbox and instead had thrown the following error:
Server Error! (Ok)
Then, I tried searching the cpanel logs or the roundcube error log but found nothing. Then, I checked the Dovecot log located here:
/var/log/maillog
I found the following:
May 7 13:57:49 network2 dovecot: imap(shawon@mellowhost.com)<26343><cQG+qxX7MvdneMrv>: Error: Mailbox INBOX: mmap(size=351817308) failed with file /home/mellow/mail/mellowhost.com/shawon/dovecot.index.cache: Cannot allocate memory
This is happening because Dovecot caches the mail index in a file, once it tries to cache a lot of emails, it fails with a memory error. In those cases, you may remove the cache file and let Dovecot generate a new cache based on the latest mails. You may simply rm the file and see Roundcube is loading again:
rm -f /home/mellow/mail/mellowhost.com/shawon/dovecot.index.cache
To see/list the constraints, first, connect to the database using the following:
\c my_prod;
Here we are assuming the database name is my_prod. Please note, we are putting these commands in the psql client utility.
Now, use the following query to list all the constraints in the database:
select pgc.conname as constraint_name, ccu.table_schema as table_schema, ccu.table_name, ccu.column_name, contype, pg_get_constraintdef(pgc.oid) from pg_constraint pgc join pg_namespace nsp on nsp.oid = pgc.connamespace join pg_class cls on pgc.conrelid = cls.oid left join information_schema.constraint_column_usage ccu on pgc.conname = ccu.constraint_name and nsp.nspname = ccu.constraint_schema order by pgc.conname;
Good luck
There are 3 things you need:
First, switch to the user zimbra:
su - zimbra
Let’s except your files are located here:
Private Key: /tmp/private.key Certificate: /tmp/your.domain.com.crt Ca-Bundle: /tmp/your.domain.com.ca-bundle
Now, copy your private key file to the following location:
cp /tmp/private.key /opt/zimbra/ssl/zimbra/commercial/commercial.key
Now, first, verify 3 things to make sure, they are correct:
/opt/zimbra/bin/zmcertmgr verifycrt comm /opt/zimbra/ssl/zimbra/commercial/commercial.key /tmp/your.domain.com.crt /tmp/your.domain.com.ca-bundle
If it, says ok, now you may deploy the certificate like the following:
/opt/zimbra/bin/zmcertmgr deploycrt comm /tmp/your.domain.com.crt /tmp/your.domain.com.ca-bundle
Once done, now, exit from the user zimbra and restart zimbra:
exit service zimbra restart
Your SSL should work now.
Question: How to stop Postgresql when you have multiple versions of PGSQL Running on Ubuntu
You may run the following command to stop specific version of postgresql when using multiple versions of postgresql in a single system, under Ubuntu
systemctl stop postgresql[@version-main]
So, for example, if you have a system, with 3 postgresql server, 12, 14, 15, and would like to stop 14 and 15, then you can run the following:
systemctl stop postgresql@15-main systemctl stop postgresql@14-main
To disable them from booting:
systemctl disable postgresql@15-main systemctl disable postgresql@14-main
You need to first install NTFS-3G package to access NTFS on Debian. NTFS-3g depends on libntfs and fuse. Using the following shall install NTFS-3g on the system:
apt install ntfs-3g -y
Once done, now you can mount ntfs using the following command:
mount -t ntfs /dev/sdb2 /mnt
In this case, sdb2 is the ntfs partition, and we are mounting this to /mnt directory.
If you are trying to mount a Windows 10/11 partition, you might end up having a read only NTFS file system. The reason is Windows 10/11 partition doesn’t fully shutdown on shutdown command, instead it hibernates the system. To properly shutdown the system, remember to shutdown the system with ‘SHIFT’ + SHUTDOWN.
Error
When you try to install Imunify360, you get the following:
[root@stack10 ~]# bash i360deploy.sh IPL Checking for an update to i360deploy.sh Downloading i360deploy.sh.repo_version (please wait) i360deploy.sh is already the latest version (2.58) - continuing Detecting ostype... centos ipset: error while loading shared libraries: libipset.so.13: cannot open shared object file: No such file or directory [2022-12-21 04:44:14] Your OS virtualization technology kvm has limited support for ipset in containers. Please, contact Imunify360 Support Team.
The reason is, latest Imunify360 installer looking for the ipset library. To install that, use the following:
yum install ipset-libs -y
Once done, you should be able to install Imunify360 now.