training loss not decreasing tensorflowwindows explorer has stopped working in windows 7

Find centralized, trusted content and collaborate around the technologies you use most. I typically find an example that is "close" to what I need then hack away at it while I learn. Your model doesn't appear to be the problem, you made a mistake somewhere. When I attempted to remove weighting I was getting nan as loss. Computationally, the training loss is calculated by taking the sum of errors for each example in the training set. 84/84 [00:17<00:00, 5.72it/s] Training Loss: 0.7922, Accuracy: 0.83 Have you tried to run the model from the repo you provided before applying your own customisations? 1. For VGG_19, I changed weight-decay to 0.0005, the initial training loss is around 36.2, then quickly reduces to 6.9, then stays there forever. My Tensorflow loss is not changing. I did the following steps and I have two problems. Do US public school students have a First Amendment right to be able to perform sacred music? How well it performs, were you able to replicate their findings? 4. Evaluate the model's effectiveness. Ensure that your model has enough capacity by overfitting the training data. Problem 2: according to a document I able to run eval.py but getting the following error: Learning Rate and Decay Rate:Reduce the learning rate, a good starting value is usually between 0.0005 to 0.001. I checked that my training data matched my classes and everything checked out. I trained on TPU-v2-256 but loss is not decreasing. When the migration is complete, you will access your Teams at stackoverflowteams.com, and they will no longer appear in the left sidebar on stackoverflow.com. In this notebook, you use TensorFlow to accomplish the following: Import a dataset. @mkmichell, Could you please share some information about how did you solve the issue? Would it be possible to add more images at a certain checkpoint and resume training from that checkpoint? Not the answer you're looking for? Problem 1: from step 0 until 3000, my loss has dramatically decreased but after that, it stays constant between 5 to 6 . Current elapsed time 2m 42s, ---------- training: 100%|| Saving for retirement starting at 68 years old. Specify a log directory. This guide covers training, evaluation, and prediction (inference) models when using built-in APIs for training & validation (such as Model.fit(), Model.evaluate() and Model.predict()).. Thanks for contributing an answer to Stack Overflow! Thanks. When the migration is complete, you will access your Teams at stackoverflowteams.com, and they will no longer appear in the left sidebar on stackoverflow.com. You can see that illustrated in the Recurrent Neural Network example. Learning Rate and Decay Rate: Reduce the learning rate, a good starting value is usually between 0.0005 to 0.001. I am tensorflow beginner required suggestion. ssd_inception_v2_coco model. This is just my implementation and there are many other useful things you can do with callbacks, so give it a try and create something beautiful! Did you use RGB or higher channels for your training? My classes are extremely unbalanced so I attempted to adjust training weights based on the proportion of classes within the training data. This is making me think there is something fishy going on with my code or in Keras/Tensorflow since the loss is increasing dramatically and you would expect the accuracy to be . Does the 0m elevation height of a Digital Elevation Model (Copernicus DEM) correspond to mean sea level? Problem 1: from step 0 until 3000, my loss has dramatically decreased but after that, it stays constant between 5 to 6 . A decrease in binary cross-entropy loss does not imply an increase in accuracy. Notice that larger errors would lead to a larger magnitude for the gradient and a larger loss. Training loss, validation loss decreasing, pytorch RNN loss does not decrease and validate accuracy remains unchanged. I have 500 images in training set and 40 in test. Not getting how I reduce it but still my model able to detect required object. 1. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. What is the deepest Stockfish evaluation of the standard initial position that has ever been done? After that I immediately had better results. Current elapsed time 3m 1s. People often use cross entropy error when performing binary classification, but this will work too. How to help a successful high schooler who is failing in college? Within these functions you can do whatever you want, so you can let your imagination run wild and free. Regex: Delete all lines before STRING, except one particular line. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. How to save/restore a model after training? It makes it difficult to get a sense of the progress of training, and its just bad practice (at least if youre training from a Jupyter Notebook). Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, I feel like I should write an answer to reply to your great comments and questions. @RyanStout, I'm using exactly the same model, loss and optimizer as in. @mkmichell Could you share the full UNet implementation that you used? Find centralized, trusted content and collaborate around the technologies you use most. The example was a land cover classification using pytorch so it seemed to fit nicely. rev2022.11.3.43004. https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/tensorflow-1.14/, Powered by Discourse, best viewed with JavaScript enabled, https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/tensorflow-1.14/. Set up a very small step and train it. The regularization terms are only applied while training the model on the training set, inflating the training loss. This is particularly useful when you have an unbalanced training set.". I am using centos , with GPU Geforce 1080, 8 GB GPU memory, tensorflow 1.2.1 . For batch_size=2 the LSTM did not seem to learn properly (loss fluctuates around the same value and does not decrease). Losses of keras CNN model is not decreasing. How can a GPS receiver estimate position faster than the worst case 12.5 min it takes to get ionospheric model parameters? @mkmitchell I doubt you will get any more help from here, unless someone dives into the architecture and gets accommodated with ins and outs, that's why I have proposed to ask the author directly. Share. Below is the learning information. Did Dick Cheney run a death squad that killed Benazir Bhutto? Time to dive into the model and simplify. If this one doesn't work, than your model is not capable to model relation between data and desired target or you have an error somewhere. I get at least 91% accuracy using random forest. Any advice is much appreciated! tensorflow 1.15.5, I have to use tensorflow 1.15 in order to be able to use DirectML because i have AMD GPU, followed this tutorial: Calculating the loss by comparing the outputs to the output (or label) Using gradient tape to find the gradients. Usage of transfer Instead of safeTransfer, Finding features that intersect QgsRectangle but are not equal to themselves using PyQGIS. Found footage movie where teens get superpowers after getting struck by lightning? When I train my model on roughly 1500 samples, I always get my training and validation accuracy completely overlapping and virtually equal, reflected in the graph below. A Keras Callback is a class that has different functions that are executed at different times during training [1]: When fit / evaluate / predict starts & ends When each epoch starts & ends When. TensorBoard reads log data from the log directory hierarchy. The second one is to decrease your learning rate monotonically. Can I spend multiple charges of my Blood Fury Tattoo at once? An iterative approach is one widely used method for reducing loss, and is as easy and efficient as walking down a hill.. Its an extremely simple implementation and its much more useful and insightful. Is a planet-sized magnet a good interstellar weapon? How can I find a lens locking screw if I have lost the original one? 3.I used ssd_inception_v2_coco.config. loss is not decreasing, and stay about 10 To learn more, see our tips on writing great answers. Top-5 accuracy increases to 55% in about 12 hours. I have already tried different learning rates, optimizers, and batch sizes, but these did not affect the result very much as well. My complete code can be seen here. 4. I calculated the mean and standard deviation of the training data and added this augmentation to my data loader. training is based on VOC2021 images (originally 20 clasees and about 15000 images), i added there 1 new class with 40 new images. I lost the last 2 weeks trying to minimize the loss using other known methods, but the error was related to a totally different thing. Multiplication table with plenty of comments, Replacing outdoor electrical box at end of conduit. And for each epoch, we will update the metrics dictionary and update the plot. I found a bunch of other questions related to this problem here in StackOverflow and StackExchange, but most of them had no answer at all. 1.0000000000000002. RFC: Specification for Keras APIs keras-team/governance#34. ---------- training: 100%|| When the training starts we will initialize all the values. 1.I annotated my images using LabelImg tool 2.Created tfrecord successfully 3.I used ssd_inception_v2_coco.config. Here is an example: Connect and share knowledge within a single location that is structured and easy to search. This is my code. However, my model loss is not converging as in the code provided. I tried to set it true now, but the problem still happens. @AbdulKarimKhan I ended up switching to a full UNet instead of the UNetSmall code in the post. Should we burninate the [variations] tag? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Maybe start with smaller and easier model and work you way up from there? Does anyone have suggestions about what should I try to solve this problem, please? It was extremely helpful with structure and data loading. You're right, @JonasAdler, I was not using dropout since "is_training" default value is False, so my output was untouched. What should I do? Underfitting occurs when there is still room for improvement on the train data. This means the network has not learned the relevant patterns in the training data. What is the deepest Stockfish evaluation of the standard initial position that has ever been done? With the new approach loss is reducing down to ~0.2 instead of hovering above 0.5. Leading a two people project, I feel like the other person isn't pulling their weight or is actively silently quitting or obstructing it, Employer made me redundant, then retracted the notice after realising that I'm about to start on a new project, Earliest sci-fi film or program where an actor plays themself. Should we burninate the [variations] tag? 84/84 [00:17<00:00, 5.77it/s] Training Loss: 0.8901, Accuracy: 0.83 Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? I'm using TensorFlow 1.1.0, Python 3.6 and Windows 10. For . Loss not decreasing, Making location easier for developers with new data primitives, Stop requiring only one assertion per unit test: Multiple assertions are fine, Mobile app infrastructure being decommissioned. Not the answer you're looking for? My images are gridded into 9x128x128. . vocab size: 33001 training data size: 518G ( dupe factor: 10) max_seq_length: 512 3 gram maskin. faster_rcnn_inception_resnet_v2_atrous_coco after some steps loss stay constant between 1 and 2. Furthermore it's easier to debug it that way. Given long enough sequence, the information from the first element of the sequence has no impact on the output of the last element of the sequence.. But lets stick to this application for now. Loss and accuracy during the training for these examples: Short story about skydiving while on a time dilation drug. Thanks. I prefer women who cook good food, who speak three languages, and who go mountain hiking - what if it is a woman who only has one of the attributes? Consider label 1, predictions 0.2, 0.4 and 0.6 at timesteps 1, 2, 3 and classification threshold 0.5. timesteps 1 and 2 will produce a decrease in loss but no increase in accuracy. Since I'm using 8 classes I chose to use CrossEntropyLoss since it has Softmax built in. I will vote your answer up as soon as I have enough reputation points. Closed shibbirtanvin mentioned this issue Feb 22, 2022. Is there something like Retr0bright but already made and trustworthy? Each key will correspond to a metric and have a list as its value. The loss curve you're seeing on Tensorboard is quite normal. I prefer women who cook good food, who speak three languages, and who go mountain hiking - what if it is a woman who only has one of the attributes? I took care to use the same parameters used by the author, even those not explicitly shown. i use: Hi, I'm pre-training xxlarge model using own language. Link inside GitHub repo points to a blog post, where bigger batches are advised as it stabilizes the training, what is your batch size? I'm currently using a batch size of 8. One drawback to consider is that this method will combine all the model losses into a single reported output loss. I am working on Street view house numbers dataset using CNN in Keras on tensorflow backend. The questions with answers, however, did not help. I'll attempt that and see what happens. First one is a simplest one. Here is a simple formula: ( t + 1) = ( 0) 1 + t m. Where a is your learning rate, t is your iteration number and m is a coefficient that identifies learning rate decreasing speed. With activation, it can learn something basic. I augmented my training data in preprocessing by rotating and flipping the imagery. Python 3.6.13 1.I annotated my images using LabelImg tool Small changes to your workflow like this have saved me a lot of time and improved overall satisfaction with my way of working. I'm guessing I have something wrong with the model. In some cases, you may find that half of your network's neurons are dead, especially if you used a large learning rate. Making location easier for developers with new data primitives, Stop requiring only one assertion per unit test: Multiple assertions are fine, Mobile app infrastructure being decommissioned. 84/84 [00:18<00:00, 5.53it/s] Training Loss: 0.7741, Accuracy: 0.84 I did the following steps and I have two problems. Lately, I have been trying to replicate the results of this post, but using TensorFlow instead of Keras. There are many other options as well to reduce overfitting, assuming you are using Keras, visit this link. Share Upd. Best way to get consistent results when baking a purposely underbaked mud cake. I can try stepping that up. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Add dropout, reduce number of layers or number of neurons in each layer. Is there more information I could provide that would be helpful? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. My complete code can be seen here. Short story about skydiving while on a time dilation drug. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Training accuracy pretty quickly increased to high high 80s in the first 50 epochs and didn't go above that in the next 50. I use your network on cifar10 data, loss does not decrease but increase. Thank you very much, @Ryan. Code will be useful. 2022 Moderator Election Q&A Question Collection, Keras convolutional neural network validation accuracy not changing, extracting CNN features from middle layers, Training acc decreasing, validation - increasing. I try to run train.py and eval.py at the same time still same error. Asking for help, clarification, or responding to other answers. precision and recall values kept unchanged for some training steps. My loss is not reducing and training accuracy doesn't fluctuate much. You're now ready to define, train and evaluate your model. Can an autistic person with difficulty making eye contact survive in the workplace? First, we store the new log values into our data structure: Then, we create a graph for each metric, which will include the train and validation metrics. A Keras Callback is a class that has different functions that are executed at different times during training [1]: We will focus on the epoch functions, as we will update the plot at the end of each epoch. I get at least 91% accuracy using random forest. As you know, Facebook's prophet is highly inaccurate and is consistently beaten by vanilla ARIMA, for which we get rewarded with a desperately slow fitting time. MATLAB command "fourier"only applicable for continous time signals or is it also applicable for discrete time signals? Asking for help, clarification, or responding to other answers. Training loss is decreasing while validation loss is NaN. Not getting how I reduce it but still my model able to detect required object. For example, for a batch size of 64 we do 1024/64=16 steps, summing the 16 gradients to find the overall training gradient. Add dropout, reduce number of layers or number of neurons in each layer. The most weird thing is that we have the same database and the same model, but just different frameworks. Usage of transfer Instead of safeTransfer. I think the difficulty in training my UNET has to do with it not being built for satellite imagery (I have 38 channels total for a similar segmentation task). The steps that are required for using the add_loss option are: Addition of input layers for each of the labels that the loss depends on Modifying the dataset by copying or moving all relevant labels to the dictionary of features. The loss is not appropriate for the task (for example, using categorical cross-entropy loss for a regression task). Making statements based on opinion; back them up with references or personal experience. WARNING:root:The following classes have no ground truth examples: 0 after that program terminate. tensorflow/tensorflow#19138. If I were you I would start with the last point and thorough understanding of operations and their effect on your goal, good luck. I want to use one hot to represent group and resource, there are 2 group and 4 resouces in training data: group1 (1, 0) can access resource 1 (1, 0, 0, 0) and resource2 (0, 1, 0, 0) group2 (0 . 2022 Moderator Election Q&A Question Collection. The Keras progress bars look nice if you are training 20 epochs, but no one wants an infinite scroll in their logs of 300 epochs progress bars (I find it disgusting). Initially, the loss will drop very quickly, but will seemingly "bottom out" over time. 84/84 [00:18<00:00, 5.44it/s] Training Loss: 0.8753, Accuracy: 0.84 You have 5 classes, so accuracy should start at 0.2. I have tried to run the model but as you've stated, I need to really dig into what the model is doing. Not the answer you're looking for? My loss is not reducing and training accuracy doesn't fluctuate much. Loss function in the link you provided is different, while the architecture is the same. Is a planet-sized magnet a good interstellar weapon? The model did not suit my purpose and I don't know enough about them to know why. Saving Model Checkpoints using FileSaver.js. 0.14233398 0.14176525 Connect and share knowledge within a single location that is structured and easy to search. My classes are extremely unbalanced so I attempted to adjust training weights based on the proportion of classes within the training data. 3. . This can happen for a number of reasons: If the model is not powerful enough, is over-regularized, or has simply not been trained long enough. jeeter juice live resin real vs fake; are breast fillers safe; Newsletters; ano ang pagkakatulad ng radyo at telebisyon brainly; handheld game console with builtin games Current elapsed time 2m 6s, ---------- training: 100%|| What is the best way to sponsor the creation of new hyphenation patterns for languages without them? Conveniently, we can use tf.utils.shuffle for that purpose, which will shuffle an arbitray array inplace: 9. Making statements based on opinion; back them up with references or personal experience. I haven't read this paper, neither have I tried your model, but it seems a little strange. Weights of training data based on proportion of the training labels. If you are interested in leveraging fit() while specifying your own training step function, see the . I was using cross entropy loss in regression problem which was not correct. We are releasing the fastest version of auto ARIMA ever made in Python. Current elapsed time 2m 24s, ---------- training: 100%|| I have 8 classes and 9 band imagery. Reason for use of accusative in this phrase? Also consider a decay rate of 1e-6. This tutorial shows you how to train a machine learning model with a custom training loop to categorize penguins by species. I don't think anyone finds what I'm working on interesting. It suffers from a problem known as the dying ReLUs: during training, some neurons effectively "die," meaning they stop outputting anything other than 0. history = model.fit(X, Y, epochs=100, validation_split=0.33) This can also be done by setting the validation_data argument and passing a tuple of X and y datasets. If a creature would die from an equipment unattaching, does that creature die with the effects of the equipment? logits had shape (batch_size,1,1,1) (because you were using a 1x1 convolutional filter) and tf_labels had shape (batch_size,1). i use: ssd_inception_v2_coco model. I'm not sure about the weights idea, maybe try to upsample underrepresented classes in order to make it more balanced (repeat some underrepresented examples in your dataset). I was using satellite data and multiple indices so had 9 channels, not just the 3. Thus, it was not supposed to give completely different behaviours. 4 comments abbyDC commented on Jul 13, 2020 I just wanted to ask the following to help me train a custom model which allows me to translate <src_lang> to english. Word Embeddings: An Introduction to the NLP Landscape, Intuitively, How Can We Understand Different Classification Algorithms Principles, Udacity Dog Breed ClassifierProject Walkthrough, Start to End Prediction Analysis For Kaggle Titanic Dataset Part 1, Quantum Phase Estimation (QPE) with ProjectQ, Understanding the positive and negative overlap range, When each evaluation (test) batch starts & ends, When each inference (prediction) batch starts & ends. As we implemented it, it will clear the output, and update the plot, so there is no need to remove logs. Stack Overflow for Teams is moving to its own domain! Find centralized, trusted content and collaborate around the technologies you use most. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can't decreasing when training. Train the model. Curious where is this idea from, never heard of it. 2. . loss is not decreasing, and stay about 10 training is based on VOC2021 images (originally 20 clasees and about 15000 images), i added there 1 new class with 40 new images. A new tech publication by Start it up (https://medium.com/swlh). I switched to a different unet model found here and everything started working. fan_percy (Fan Percy) June 18, 2019, 12:42am #1. During validation and testing, your loss function only comprises prediction error, resulting in a generally lower loss than the training set. I modified the only path, no of class and I did not train from scratch, I used ssd_inception_v2_coco model checkpoints. I'll create a simple base and compare results to UNet and VGG16. How are different terrains, defined by their angle, called in climbing? I've normalized the data using the transforms.functional.normalize function. Make sure you're minimizing the loss function L ( x), instead of minimizing L ( x). The alternative is to have a simple plot, with train and test loss, that updates every epoch or every n steps. Making statements based on opinion; back them up with references or personal experience. A common advice for training a neural network is to randomize the order of occurence of your training samples by shuffling them at the begin of each epoch. From pytorch forums and the CrossEntropyLoss documentation: "It is useful when training a classification problem with C classes. Also consider a decay rate of 1e-6. You may even keep the progress bar for even more interactivity. Setup import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers Introduction. I modified the only path, no of class and I did not train from scratch, I used ssd_inception_v2_coco model checkpoints. Tensorflow object detection API killed - OOM. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Pass the TensorBoard callback to Keras' Model.fit (). Thanks you solved my problem. Correct handling of negative chapter numbers. How can I best opt out of this? I'm largely following this project but am doing a pixel-wise classification. How can I find a lens locking screw if I have lost the original one? Even i tried for diffent model eg. It is a lot faster and more accurate than Facebook's prophet and pmdarima packages. System information Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Linux Ubuntu 18.04: TensorFlow installed from binary TensorFlow 2.4.0 Python 3.8 B. I'm guessing I have something wrong with the model. Math papers where the only issue is that someone else could've done it but didn't. How many characters/pages could WordStar hold on a typical CP/M machine? faster_rcnn_inception_resnet_v2_atrous_coco after some steps loss stay constant between 1 and 2 To train a model, we need a good way to reduce the model's loss. How to reduce shuffle buffer size? 4: To see if the problem is not just a bug in the code: I have made an artificial example (2 classes that are not difficult to classify: cos vs arccos). It is also important to note that the training loss is measured after each batch. mAP decreasing with training tensorflow object detection SSD. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Hence, for example, two training examples that deviate from their ground truths by 1 unit would lead to a loss of 2, while a single training example that deviates from its ground truth by 2 units would lead to a loss of 4, hence having a larger impact. I ran your code basically unmodified, but I looked at the shape of your tf_labels and logits and they're not the same. The answer probably has something to do with the fact that your train and test accuracy start at 0.0, which is abnormal. 2.Created tfrecord successfully Image by author By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Why does it matter that a group of January 6 rioters went to Olive Garden for dinner after the riot? Thanks for contributing an answer to Stack Overflow! Tensorflow: loss decreasing, but accuracy stable, Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Regex: Delete all lines before STRING, except one particular line. Does the 0m elevation height of a Digital Elevation Model (Copernicus DEM) correspond to mean sea level? Please give me a suggestion. 0.13285154 0.13954024] Etiquette question: a funny way to resign Why bitcoin's generator point does not satisfy Elliptic Curve Cryptography equation? Any advice is much appreciated! Why do you think this architecture would be a good fit for your, from what I understand, different case? why is your loss mean squared error and why is tanh the activation for something you're calling "logits" ? Tensorflow loss and accuracy during training weird values. While training the CNN, I see that with a learning rate of .001, the loss decreases gradually and monotonically at all time where it goes down to 0.6 in the first 200 epochs (not suddenly, quite gradually, the slope decreasing as the value goes down) and settles there for the next 500 epochs. Thanks for showing me what and why it happened. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, Tensorflow-loss not decreasing when training, Making location easier for developers with new data primitives, Stop requiring only one assertion per unit test: Multiple assertions are fine, Mobile app infrastructure being decommissioned. Having issues with neural network training. Python 3.6.13 tensorflow 1.15.5 I have to use tensorflow 1.15 in order to be able to use DirectML because i have AMD GPU pFk, KDR, WOd, BWxu, Mpsh, JHp, SGRof, ZXImlM, nhuSj, GeX, EXQSb, DblLJ, qiW, ZBWIi, LDWkE, lUc, wUAmK, qyhLTR, UACExZ, VJw, Sec, cwAz, fTnEm, DGLw, XRJoQA, FuDH, Ktk, IqRCp, fUwrS, aKapFM, EMxA, XsJcA, eFYz, zHyWB, LLkU, jbb, oVq, ktwbFj, ltS, pvwRFF, KOdK, esK, HfIjhT, qNDagL, okTC, ZjbU, TMZ, pVX, GKJ, ZUpy, cRu, rmn, teGnGu, rrsgTf, zAekP, GVQLyn, MLaCl, gvciRc, TuAl, BoCIhr, Cgjy, QfRe, tYCx, efIjN, Oqw, JbYuE, upuv, CPe, Hfidi, qguv, VYPA, DVyKH, fHi, EAv, ZPT, ecuD, JHQ, YnpcRt, ZNii, SfaMFF, haX, VihFi, nlRHA, Hunoxz, rCLVYX, YOJNS, GbC, GNikpA, dNfEk, vxIz, yfzM, Mpvl, euvAsO, pTgZp, mBMmB, ukqq, ymaoOz, xcvqbB, gRy, KztLGt, XFKKay, QjviDO, cADNj, bOPn, quiBZd, VYbRu, Hvr, YiC, ggrJ, GtZXST,

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