I am training a cnn model with five classes using keras library. Using model.predict function i get prediction percentage of the classes. My problem is for a image which doesn't belong to these classes and completely irrelevant, the predict class still predicts the percentages according to the classes.
How do I prevent it? How do I identify it as irrelevant?
I assume you are using a softmax activation on your last layer to generate the probabilities for each class. By definition, the sum of the outputs from the softmax activation must add up to 1. Therefore, it is impossible for the neural net to say that the image does not belong to any of your classes, with your current setup.
There are two potential ways you could address this:
Add another class that represents "other" or "unknown" objects (so you have 6 classes).
Add another output to your neural net (or train a completely independent neural net) that does binary classification on whether or not the image is in one of the 5 classes. That way, if your secondary output says that the image is not in the 5 classes, you can ignore the softmax output.
In both cases, you will need to augment your dataset with images that do not fall in your 5 classes.
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Im working on a binary classificaton project, and im using VAE (variational autoencoder) to handle the imbalance between the 2 classes by generating new samples for the minority class.
the first class (majority class) contains 20000 samples, and the second one (minority class) contains 500 samples.
After training VAE model on the minority class, i generated new samples for this class and add them to the training set, then i trained two classification models, a model on trained on the imbalanced data (only training set) and the second one trained with training set + data generated by VAE). The problem is the first model is giving results better than the second(f1-score, Roc auc...), and i thought that maybe the problem was because of the limited amount of data that the VAE was trained on.
Any help please.
Though 500 training Images are not good enough to generate diversified images from a VAE, you can still try producing some. It's better to take mean of latents of 10 different images (or even more) and pass it through the decoder ( if you're already doing this, ignore it. If you're doing some other method, try this).
If it's still not working, then, I suggest you to build a Conditional VAE on your entire dataset. In conditional VAE, you train VAE using the labels so that your models learns not only reconstruction but also what class of image it is reconstructing. This helps you to generate an Image of any particular class.
I have five classes and I want to compare four of them against one and the same class. This isn't a One vs Rest classifier, as for each output I want to score them against one base class.
The four outputs should be: base class vs classA, base class vs classB, etc.
I could do this by having multiple binary classification tasks, but that's wasting computation time if the first layers are BERT preprocessing + pretrained BERT layers, and the only differences between the four classifiers are the last few layers of BERT (finetuned ones) and the Dense layer.
So why not merge the graphs for more performance?
My inputs are four different datasets, each annotated with true/false for each class.
As I understand it, I can re-use most of the pipeline (BERT preprocessing and the first layers of BERT), as those have shared weights. I should then be able to train the last few layers of BERT and the Dense layer on top differently depending on the branch of the classifier (maybe using something like keras.switch?).
I have tried many alternative options including multi-class and multi-label classifiers, with actual and generated (eg, machine-annotated) labels in the case of multiple input labels, different activation and loss functions, but none of the results were acceptable to me (none were as good as the four separate models).
Is there a solution for merging the four different models for more performance, or am I stuck with using 4x binary classifiers?
When you train DNN for specific task it will be (in vast majority of cases) be better than the more general model that can handle several task simultaneously. Saying that, based on my experience the properly trained general model produces very similar results to the original binary ones. Anyways, here couple of suggestions for training strategies (assuming your training datasets for each task are completely different):
Weak supervision approach
Train your binary classifiers, and label your datasets using them (i.e. label with binary classifier trained on dataset 2 datasets [1,3,4]). Then train your joint model as multilabel task using all the newly labeled datasets (don't forget to randomize samples before feeding them to trainer ;) ). Here you will need to experiment if you will use threshold and set a label to 0/1 or use the scores of the binary classifiers.
Create custom loss function that will not penalize if no information provided for certain class. So when your will introduce sample from (say) dataset 2, your loss will be calculated only for the 2nd class.
Of course you can apply both simultaneously. For example, if you know that binary classifier produces scores that are polarized (most results are near 0 or 1), you can use weak labels, and automatically label your data with scores. Now during the second stage penalize loss such that for score x' = 4(x-0.5)^2 (note that you get logits from the model, so you will need to apply sigmoid function). This way you will increase contribution of the samples binary classifier is confident about, and reduce that of less certain ones.
As for releasing last layers of BERT, usually unfreezing upper 3-6 layers is enough. Releasing more layers improves results very little and increases time and memory requirements.
Corresponding Tensorflow documentation I trained 3 objects and get result (It can recognize these objects). When I show other objects (not the 3 ones) it doesn't work correctly.
I want to train only one object (example: a cup) and recognize only this object. Is it possible to do via Tensorflow ?
Your question doesn't provide enough details, but as I can guess your trained the network with softmax activation and Categorical or SparseCategorical cross entropy loss. If my guess is right, such network always generates prediction to one of three classess, regardless to actual data, i.e. there is no option of "no-one".
In order to train network to recognize only one class of objects, make the only one output with only one channel and sigmoid activation. Use BinaryCrossEntropy loss to train your model for the specific object. Provide dataset that includes examples with this object and without it.
The goal is classifying dog and non-dog. The train dataset only contains dog images. Neural network will be trained using this train dataset only and then tested using test dataset that contains dog and non-dog images.
I followed the encoder Datacamp tutorial and in my case, the autoencoder classified all test images as dog which is wrong.
Building CNN for one class classification is not possible too. Any idea how to do this?
The correct term for what you're asking about is binary classification.
And you can safely use a CNN for binary classification. The question is just how you model the output layer. You can either use a softmax layer with two output units (argmax(output) is class 1: dog, class 2: not dog) or use the more traditional method of using a single sigmoid unit as the output (output>0.5: dog, <0.5: no dog). As the softmax normalization is a multiclass extension of sigmoid activation, a sigmoid layer makes more sense on a theoretical basis but if you have trouble implementing this, a softmax should work just as well.
Of course, you'll have to adapt your data labels to fit either approach.
Edit: You need both images of dogs and those that include no dogs. The network needs to know the distributions for both. Just think about your use case and what kind of images you expect that are not dogs. Then collect images that are not dogs and label them accordingly.
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
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.
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.
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.
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).