All Questions
Tagged with neural-networks multi-class
15 questions with no upvoted or accepted answers
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Categorical cross-entropy vs Binary cross-entropy for multi-class classification with mixup
I understand that for multi-class classification the correct loss to use is categorical cross-entropy. However, when performing mixup as a regularisation technique two samples $(X_1, y_1)$ and $(X_2, ...
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3
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545
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What is a good loss function in multiclass multilabel classification where only one of the possible labels is observed?
I am training an ANN in multiclass multilabel scenario, where only one of the possible labels is observed at a time, let me illustrate on an example:
I have a state X and the ground truth label Y for ...
- 53
2
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538
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Similarity between Train and Test data sets
I have multiclass classification dataset and I am using Deep nets for the classification task. To explain the problem, let's assume that I have 5 classes to classify. No matter what I try, be it ...
- 199
1
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1
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Reason for softmax approximation in Ian Goodfellow's deep learning book
In section 6.2.2.2 (equation 6.31) they state:
Overall, unregularized maximum likelihood will drive the model to learn parameters that drive the softmax to predict the fraction of counts of each ...
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1
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1
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Should I train my classifier with examples that are outside my classes of interest? And should I create an "others" class to handle them?
This is a 2 part question regarding a multi-class classifier based on a neural network that is expected to predict whether the input image has a cat or a dog. If shown something different (like a man),...
1
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0
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184
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Multi-class multi-label with partial mutual exclusivity
Given an input, I want to predict 0/1 for each of N output classes. The output can be 1 for multiple classes. So I'm training with individual binary cross-entropies for each of the output classes. ...
- 11
1
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Which loss function is the best for muticlass ordinal model?
I have a deep-learning model, where you can predict 5 different class (0-4). I want a model that punishes predictions that are more wrong. So if the model predicts 3 and it is a 4, it's not as bad as ...
1
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184
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How to maximize subset accuracy for multilabel multiclass image classification
I working on multi-label multi-class image classification. I am using TensorFlow.
Currently I am using sigmoid on output layer with binary_crossentrpy. Model is ...
- 111
1
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0
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499
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Multi-class image segmentation with conflicting labels
I'm trying to perform multi-class semantic segmentation on a corpus made up of several sub-corpora. The difficult part is that across sub-corpora labels are not consistent. That is to say that similar ...
- 121
1
vote
1
answer
91
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Neural Networks: How to do class prediction from murky labels
I'm conducting an experiment with the MNIST digit data - handwritten digits 0-9, each example composed of 28x28 bitmap of pixels.
Imagine a collection of examples is drawn at random without class ...
- 595
1
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Learning separate models vs single model
I have seen in several texts that: "We learn separate models for each class/category". What does this mean and how is this different from learning a single model to classify all the classes?
- 153
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How do I know which class has been difficult to learn for my multi-class model?
For a multi-class model, there are always chances that the model is learning one class's features more than the other. But how do I find which class has been weakly learned? Please help.
- 11
0
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522
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Increasing precision for one label in multiclass classification
I am doing multiclass classification for 3 labell with neural net. The model works fine but when I check precision/recall per label in validation set I can see that precision is a little bit too low ...
- 31
0
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27
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Neural network performs well on test but classifies completely the opposite when used to classify on training data
I have a neural network that is being used to classify three classes -1, 0, 1. It going to be applied to time series data to determine a class at each time point to ...
- 101
0
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Why not approach the Multi-Class Classification problem using Neural Networks as a model the same way it's approached with a Logistic Regression model
I can't seem to understand why when approaching a K-class classification problem using a Neural Network we take a different approach than any other classification model.
I understand that in the ...