Hi I have a program whose task is to classify between images of cats and dogs. My algorithm does not learn and does not improve its accuracy. Any ideas on how to increase the accuracy in convolutional neural network(I dont know how to share jupyter file ).
Any general ways to improve the algorithm will help
Well this question leaves a lot to the imagination, and therefore I can already assume that you tried to make a small neural network yourself.
1) Did you use convolutional layers?
2) Did you try transfer learning? (InceptionV3, Vgg16, Xception,...)
3) Do you have enough data? 1000 images a class approximately
Maybe this site can be useful to understand certain things:
http://cs231n.github.io/convolutional-networks/
If I did not help you at all, then please rephrase your question :)
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I want to solve a medical-imaging related problem by doing transfer learning with a CNN. My main issue is that I have repeated measurements for the subjects under different conditions, so I would like my network to be aware of these conditions as well. How can I do Transfer Learning with a pretrained network such as the VGG-16, but then add other static variables to the network and retrain everything? I would appreciate some hints, any example on the web, ... I'ts been quite a long time that I'm stuck with this.
Thank you very much,
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 a newby to the convolutional neural nets... so this may be an ignorant question.
I have followed many examples and tutorials now on the MNIST example in TensforFlow. In the CNN examples, all authors talk bout using the 'input filters' to run in the CNN. But no one that I can find mentions WHERE they come from. Can anyone answer where these come from? Or are they magically obtained from the input images.
Thanks! Chris
This is an image that one professor uses, be he does not exaplain if he made them or TensorFlow auto-extracts these somehow.
Disclaimer: I am not an expert, more of an enthusiast.
To cut a long story short: filters are the CNN equivalent of weights, and all a neural network essentially does is learning their optimal values.
Which it does by iterating through a training dataset, making predictions, comparing them to the label/value already assigned to each training unit (usually an image in case of a CNN) and adjusting weights to minimize the error function (the difference between the predicted value and the actual value).
Initial values of filters/weights do not matter that much, so although they might affect the speed of convergence to a small degree, I believe they are often assigned random values.
It is the job of the neural network to figure out the optimal weights, not of the person implementing it.
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I intend to use a multi layer perceptron network trained with backpropagation (one hidden layer, inputs served as 8x8 bit matrices containing the B/W pixels from the image). The following questions arise:
which type of learning should I use: batch or on-line?
how could I estimate the right number of nodes in the hidden layer? I intend to process the 26 letter of english alphabet.
how could I stop the training process, to avoid overfitting?
(not quite related) is there another better NN prved to perform better than MLP? I know about MLP stucking in local minima, overfitting and so on, so is there a better (soft computing-based) approach?
Thanks
Most of these questions are things that you need to try different options to see what works best. That is the problem with ANNs. There is no "best" way to do almost anything. You need to find out what works for your specific problem. Nevertheless, I will give my advice for your questions.
1) I prefer incremental learning. I think it is important for the network weights to be updated after each pattern.
2) This is a tough question. It really depends on the complexity of your network. How many input nodes, output nodes, and training patterns that there are. For your problem, I might start with 100 and try ranges up and down from 100 to see if there is improvement.
3) I usually calculate the total error of the network when applied to the test set (not the training set) after each epoch. If that error increases for about 5 epochs, I will stop training and then use the network that was created before the increase occurred. It is important not to use the error of the training set when deciding to stop training. This is what will cause overfitting.
4) You could also try a probabilistic neural network if you are representing your output as 26 nodes, each representing a letter of the alphabet. This network architecture is good for classification problems. Again, it may be a good idea just to try a few different architectures to see what works best for your problem.
Regarding number 3, one way to find out when your ANN starts to overfit is by graphing the accuracy of the net on your training data and your test data vs the number of epochs performed. At some point, as your training accuracy continues to increase (tending towards 100%), your test accuracy will probably start to actually decrease because the ANN is overfitting to the training data. See what epoch that starts to happen and make sure not to train past that.
If your data is very regular and consistent, then it might not overfit until very late in the game, or not at all. And if your data is highly irregular, then your ANN will start to overfit much earlier.
Also, a way to test how regular your data is is to do something like k-fold cross validation.