I want only logic of how data goes to mongodb and get the return just tell me little hint how i can do that.
A little hint for color prediction game.
Related
I'm trying to determine, day or night, whether or not a GOES image pixel represents a clear view of the land, or if it's obscured by cloud. I believe the relevant channels are CMI_C13, CMI_C08, and CMI_C10, but I'm not sure what function to use to combine them. Does someone have a heuristic for this, or does it require something more complex (like a decision tree or neural network)?
(I'm using Google Earth Engine and the https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPC dataset.)
Even though I am quite familiar with the concepts of Machine Learning & Deep Learning, I never needed to create my own dataset before.
Now, for my thesis, I have to create my own dataset with images of an object that there are no datasets available on the internet(just assume that this is ground-truth).
I have limited computational power so I want to use YOLO, SSD or efficientdet.
Do I need to go over every single image I have in my dataset by my human eyes and create bounding box center coordinates and dimensions to log them with their labels?
Thanks
Yes, you will need to do that.
At the same time, though the task is niche, you could benefit from the concept of transfer learning. That is, you can use a pre-trained backbone in order to help your model to learn faster/achieve better results/need fewer annotations example, but you will still need to annotate the new dataset on your own.
You can use software such as LabelBox, as a starting point, it is very good since it allows you to output the format in Pascal(VOC) format, YOLO and COCO format, so it is a matter of choice/what is more suitable for you.
I'm a newbie in RL so please forgive me if I ask stupid question:)
I'm working a DQN project right now and it's very similar to the simplest snake game. The game is wrote in js and has a demo (in which snake moves randomly). But since I don't know how to write js， I can't pass the action value to the game during trainng process, what I'm doing now is generating random game image and training the dqn model instead.
What I want to ask is that: Is it possible to do in this way? Can the Q(s,r) still converge? If it's possible, is there anything I should pay attention to? and do I need the episilon parameter anymore?
Thank you very much:)
I'd definitely say no!
The problem is that the agent will only learn from random decisions and can never try if a learned action produces maybe even more reward. So everything he learns will be based on the starting frames.
Further, the agent will, in your case, never learn how to handle his size (if it grows like in snake) because he will never grow due to the bad random decisions.
Imagine a child that tries to ride a bike and you lift it off the bike as soon as it has ridden one meter. It will probably be able to ride one or even more meters straight but will never be able to do turns, etc.
I'm studying Image Recognition with Tensorflow. I already read about the topic How to retrain Inception's Layer for new categories on Tensorflow.org, which utilize the Inception v3 training model.
Now, I desire to creat my own CNN model in order to compare with Inception v3, but I don't know how can I begin with.
Anyone knows some guides step-by-step on this problem?
I'd appreciate any your suggestion
Thanks in advance
First baby steps
The gold standard for getting started in image recognition is processing MNIST images. Tensorflow has a great tutorial on how to get started and also how to move to convolutional networks.
From there it is a long hard road to compete with Inception without just copying someone else's graph. You'll probably want to get a feel for what the different layers of convolution do. I created a basic Tensorflow Tutorial which contains an example python file that demos different convolution graphs and their resulting accuracy.
Going deeper
After conquering MNIST you'll need a lot of images (you can get them from imageNet) and a lot of GPU (to run all your training) and a software setup so that you can not only run and test your model, but dozens (if not hundreds) of variations to explore your hyper parameters (like learning rate, convolution size, dropout, etc). Remember, it took a team of leading edge Machine Learning experts to create something like Inception, many many months (possibly years) of iteration to find the model they use today, and thousands of CPU/GPU hours.
If you are trying to understand what is going on and what makes a good graph, then trying to recreate Inception is a great idea. If you just want an excellent Image recognition model, then reuse an existing one.
If you are trying to have fun, just do it!
Cheers-
I want to learn to make infinite running games like temple run.... but at a very basic level. I am good at as3 and flash... and i have created all the graphics.... the whole scenario of the game is in first person view. And the graphics is all created in Flash.
Can anyone please suggest some tutorials or any techniques that i should use to make the objects look like they are coming from a far point and how i can write the logic of hitting them or escaping them.
Thanks for ur help... :)