CODEWITHSUNDEEP
tensorflowmachine-learningneural-networkconv-neural-networktraining-data
fidz81
fidz81

Reputation: 3

Convolutional Neural Network Training

I have a question regarding convolutional neural network (CNN) training.

I have managed to train a network using tensorflow that takes an input image (1600 pixels) and output one of three classes that matches it.

Testing the network with variations of the trained classes is giving good results. However; when I give it a different -fourth- image (does not contain any of the trained 3 image), it always returns a random match to one of the classes.

My question is, how can I train a network to classify that the image does not belong to either of the three trained images? A similar example, if i trained a network against the mnist database and then a gave it the character "A" or "B". Is there a way to discriminate that the input does not belong to either of the classes?

Thank you

Upvotes: 0

Views: 281

Answers (4)

Mikhail Berlinkov
Mikhail Berlinkov

Reputation: 1624

Another solution would be to check what outcome your CNN usually gives when given something else. I believe the last layer must be softmax and your CNN should return probabilities of the three given classes. If none of these probabilities is close to 1 this might be a sign that this is something else assuming your CNN is well trained (it must be fined for overconfidence when predicting wrong labels).

Upvotes: 0

MSalters
MSalters

Reputation: 179779

You probably have three output nodes, and choose the maximum value (one-hot encoding). That's a bit unfortunate as it's a low number of outputs. Non-recognized inputs tend to cause pretty random outputs.

Now, with 3 outputs, roughly speaking you can get 7 outcomes. You might get a single high value (3 possibilities) but non-recognized input can also cause 2 high outputs (also 3 possibilities) or approximately equal output (also 3 possibilities). So there's a decent chance (~ 3/7) of random inputs producing a pattern on the output nodes which you'd only expect for a recognized input.

Now, if you had 15 classes and thus 15 output nodes, you'd be looking at roughly 32767 possible outcomes for unrecognized inputs, only 15 of which correspond to expected one-hot outcomes.

Underlying this is a lack of training data. If your training set has examples outside the 3 classes, you can just dump this in a 4th "other" category and train with that. This by itself isn't a reliable indication, as usually the theoretical "other" set is huge, but you now have 2 complementary ways of detecting other inputs: either by the "other" output node or by one of the 11 ambiguous outputs.

Upvotes: 0

Ioannis Nasios
Ioannis Nasios

Reputation: 8527

Your model will always make predictions like your labels, so for example if you train your model with MNIST data, when you will make predictions, prediction will always be 0-9 just like MNIST labels.

What you can do is train a different model first with 2 classes in which you will predict if an image belongs to data set A or B. E.x. for MNIST data you label all data as 1 and add data from other sources that are different (not 0-9) and label them as 0. Then train a model to find if image belongs to MNIST or not.

Upvotes: 1

Muhammad Hannan
Muhammad Hannan

Reputation: 2567

Convolutional Neural Network (CNN) predicts the result from the defined classes after training. CNN always return from one of the classes regardless of accuracy. I have faced similar problem, what you can do is to check for accuracy value. If the accuracy is below some threshold value then it's belong to none category. Hope this helps.

Upvotes: 0

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