Suppose I have a lot of photos of 2 eye cats and other creatures, but I will receive a 3 eye cats photos, what algorithm should I use to find this 3 eye cat in the test data set?
In general Convolutional Neural Network (CNN) might fit your problem well.
But, again there are a lot of points that you need to consider: How many samples have you got? Is there a class imbalance i.e how many cats Vs other animals (since you said particularly about cats) etc.
More description about your problem will aid you to get a more apt response.
Related
The question is simple, however I cannot find solution:
How to recognise cats and dogs and filter out everything other?
Another words: I have a big database with images of cats and dogs and all other photos mixed together, is there a way to say in output: cat -or- dog -or- something else?
Either CNNs are not constructed to do such things efficiently?
I'm using python / Keras / Tensorflow; solution with finding cats / dogs when I provide images of cats and dogs only - works fine.
Labelling next class as 'other' or something like that is the simplest way to do that.
What I found, that because of many different types of images in 'other' class, training sample should be much bigger than just when differing between cats and dogs.
what approach should i take when I want my CNN multi-class network to output something like [0.1, 0,1] when image doesn't belong
to any class. Using softmax and categorical_crossentropy for multi-class would give me output that sums up to 1 so still not what I want.
I'm new to neural networks so sorry for silly question and thanks in advance for any help.
I think you are gonna think about Bayesian Learning. First, talking about uncertainty.
For example, given several pictures of dog breeds as training data—when a user uploads a photo of his dog—the hypothetical website should return a prediction with rather high confidence. But what should happen if a user uploads a photo of a cat and asks the website to decide on a dog breed?
The above is an example of out of distribution test data. The model has been trained on photos of dogs of different breeds, and has (hopefully) learnt to distinguish between them well. But the model has never seen a cat before, and a photo of a cat would lie outside of the data distribution the model was trained on. This illustrative example can be extended to more serious settings, such as MRI scans with structures a diagnostics system has never observed before, or scenes an autonomous car steering system has never been trained on.
A possible desired behaviour of a model in such cases would be to return a prediction (attempting to extrapolate far away from our observed data), but return an answer with the added information that the point lies outside of the data distribution. We want our model to possess some quantity conveying a high level of uncertainty with such inputs (alternatively, conveying low confidence).
Then, I think you could read briefly this paper when they also apply to classification task and generate uncertainty for classes (dog, cat...). From this paper, you can extend your finding to application using this paper, and I think you will find what you want.
I am building a classifier that predicts the damage of a vehicle(like high, low, medium, good). I referred to this GitHub repository
https://github.com/raviranjan0309/Car-Damage-Detector
There is a retrained_label.txt file in models/tf_files which consist of four classes
not,
car,
high,
low
I do not want these four classes and I want my tf to predict one of the following
Good,
High Damage,
Low Damage,
Medium Damage
Is this possible ?
Should I need to retrain the tf for these classes ?
If so how ?
Thanks
The file you mentioned only has 4 words in it and to be honest it is difficult to understand why they are in that file.
Normally, for any tensorflow related analysis, you have to retrain the algorithm to be able to predict based on new labels.
If you are new to ML/DL and Tensorflow, I would suggest looking into excellent tutorials on Titanic predictors where you can use a simple database to predict either one of two outcomes: survive or die
You can then use a similar code and just use a different dataset (in this case I guess a car dataset) to have it predict one of four possible outcomes for damage. The only problem is getting that dataset of course
(many examples, but here's one: https://towardsdatascience.com/predicting-the-survival-of-titanic-passengers-30870ccc7e8)
Without having at least a 1000 or so data point with car information where that damage is already listed, it would be quite challenging.
So just to summarize:
1) yes you have to retrain and probably need a different dataset too
2) you may be able to create a dataset with damage info based on what you already have
3) once training/testing sets are ready, you can then retrain using simple ML techniques
I am planning on making an AI song composer that would take in a bunch of songs of one instrument, extract musical notes (like ABCDEFG) and certain features from the sound wave, preform machine learning (most likely through recurrent neural networks), and output a sequence of ABCDEFG notes (aka generate its own songs / music).
I think that this would be an unsupervised learning problem, but I am not really sure.
I figured that I would use recurrent neural networks, but I have a few questions on how to approach this:
- What features from the sound wave I should extract so that the output music is melodious?
Also, I have a few other questions as well
- Is it possible, with recurrent neural networks, to output a vector of sequenced musical notes (ABCDEF)?
- Any smart way I can feed in the features of the soundwaves as well as sequence of musical notes?
Well i did something similar once(making a shazam like app on matlab) , i think you can use FFT(Fast Fourier Transform ) to break it down into the constituent frequencies and their corresponding amplitudes .Then you can use the frequency range of different instruments to select them out of the whole bunch and classify .
I already tried something similar with an RNN (Recurrent Neural Network). Try using an LSTM network (Long Short Term Memory), they are a WAY better than RNNs for this type of data processing from what I read afterward, because they do not suffer from the "vanishing gradient problem".
What Chris Thaliyath said is a good hint on how to train the feature detector.
Can anybody please explain multitask learning in simple and intuitive way? May be some real
world problem would be useful.Mostly, these days i am seeing many people are using it for natural language processing tasks.
Let's say you've built a sentiment classifier for a few different domains. Say, movies, music DVDs, and electronics. These are easy to build high quality classifiers for, because there is tons of training data that you've scraped from Amazon. Along with each classifier, you also build a similarity detector that will tell you for a given piece of text, how similar it was to the dataset each of the classifiers was trained on.
Now you want to find the sentiment of some text from an unknown domain or one in which there isn't such a great dataset to train on. Well, how about we take a similarity weighted combination of the classifications from the three high quality classifiers we already have. If we are trying to classify a dish washer review (there is no giant corpus of dish washer reviews, unfortunately), it's probably most similar to electronics, and so the electronics classifier will be given the most weight. On the other hand, if we are trying to classify a review of a TV show, probably the movies classifier will do the best job.