For multiclass classification problem,
Is one hot encoding of target column necessary or we can use label encoded target column and just use the loss as "SparseCategoricalCrossEntropy"
The number of units in output layer is always equal to number of classes? Does it depends on type of encoding we are performing on target column ?
Unless you are dealing with RNNs ( sequential inputs ) , you can use SparseCategoricalCrossEntropy as loss (when the target aren't one hot encoded).
Related
Would this be a valid implementation of a cross entropy loss that takes the ordinal structure of the GT y into consideration? y_hat is the prediction from a neural network.
ce_loss = F.cross_entropy(y_hat, y, reduction="none")
distance_weight = torch.abs(y_hat.argmax(1) - y) + 1
ordinal_ce_loss = torch.mean(distance_weight * ce_loss)
I'll attempt to answer this question by first fully defining the task, since the question is a bit sparse on details.
I have a set of ordinal classes (e.g. first, second, third, fourth,
etc.) and I would like to predict the class of each data example from
among this set. I would like to define an entropy-based loss-function
for this problem. I would like this loss function to weight the loss
between a predicted class torch.argmax(y_hat) and the true class y
according to the ordinal distance between the two classes. Does the
given loss expression accomplish this?
Short answer: sure, it is "valid". You've roughly implemented L1-norm ordinal class weighting. I'd question whether this is truly the correct weighting strategy for this problem.
For instance, consider that for a true label n, the bin n response is weighted by 1, but the bin n+1 and n-1 responses are weighted by 2. This means that a lot more emphasis will be placed on NOT predicting false positives than on correctly predicting true positives, which may imbue your model with some strange bias.
It also means that examples on the edge will result in a larger total sum of weights, meaning that you'll be weighting examples where the true label is say "first" or "last" more highly than the intermediate classes. (Say you have 5 classes: 1,2,3,4,5. A true label of 1 will require distance_weight of [1,2,3,4,5], the sum of which is 15. A true label of 3 will require distance_weight of [3,2,1,2,3], the sum of which is 11.
In general, classification problems and entropy-based losses are underpinned by the assumption that no set of classes or categories is any more or less related than any other set of classes. In essence, the input data is embedded into an orthogonal feature space where each class represents one vector in the basis. This is quite plainly a bad assumption in your case, meaning that this embedding space is probably not particularly elegant: thus, you have to correct for it with sort of a hack-y weight fix. And in general, this assumption of class non-correlation is probably not true in a great many classification problems (consider e.g. the classic ImageNet classification problem, wherein the class pairs [bus,car], and [bus,zebra] are treated as equally dissimilar. But this is probably a digression into the inherent lack of usefulness of strict ontological structuring of information which is outside the scope of this answer...)
Long Answer: I'd highly suggest moving into a space where the ordinal value you care about is instead expressed in a continuous space. (In the first, second, third example, you might for instance output a continuous value over the range [1,max_place]. This allows you to benefit from loss functions that already capture well the notion that predictions closer in an ordered space are better than predictions farther away in an ordered space (e.g. MSE, Smooth-L1, etc.)
Let's consider one more time the case of the [first,second,third,etc.] ordinal class example, and say that we are trying to predict the places of a set of runners in a race. Consider two races, one in which the first place runner wins by 30% relative to the second place runner, and the second in which the first place runner wins by only 1%. This nuance is entirely discarded by the ordinal discrete classification. In essence, the selection of an ordinal set of classes truncates the amount of information conveyed in the prediction, which means not only that the final prediction is less useful, but also that the loss function encodes this strange truncation and binarization, which is then reflected (perhaps harmfully) in the learned model. This problem could likely be much more elegantly solved by regressing the finishing position, or perhaps instead by regressing the finishing time, of each athlete, and then performing the final ordinal classification into places OUTSIDE of the network training.
In conclusion, you might expect a well-trained ordinal classifier to produce essentially a normal distribution of responses across the class bins, with the distribution peak on the true value: a binned discretization of a space that almost certainly could, and likely should, be treated as a continuous space.
no
13
what's the meaning of 'parameterize' in deep learning? As shown in the photo, does it means the matrix 'A' can be changed by the optimization during training?
Yes, when something can be parameterized it means that gradients can be calculated.
This means that the (dE/dw) which means the derivative of Error with respect to weight can be calculated (i.e it must be differentiable) and subtracted from the model weights along with obviously a learning_rate and other params being included depending on the optimizer.
What the paper is saying is that if you make a binary matrix a weight and then find the gradient (dE/dw) of that weight with respect to a loss and then make an update on the binary matrix through backpropagation, there is not really an activation function (which by requirement must be differentiable) that can keep the values discrete (like 0 and 1) but rather you will end up with continous values (like these decimal values).
Therefore it is saying since the idea of having binary values be weights and for them to be back-propagated in a way where the weights + activation function also yields an updated weight matrix that is also binary is difficult, another solution like the Bernoulli Distribution is used instead to initialize parameters of a model.
Hope this helps,
I have dataset (sequence to sequence), each sample input is seq of charterers (combination from from 20 characters and max length 2166) and out is list of charterers (combination of three characters G,H,B). for example OIREDSSSRTTT ----> GGGHHHHBHBBB
I would like to do simple pytorch model that work in that type of dataset. Model that can predict sequence of classes. I would appreciate any suggestions or links for simple mode that do the same?
Thanks
If the output sequence always has the same length as the input sequence, you might want to use transformer encoder, because it basically transforms the inputs with attention to the context. Also you can try to use anything that is used to tagging: BiLSTM, BiGRU, etc.
If you want your model to be able to predict sequences of different length (not necessary the same as input length), look at some encoder-decoder models, such as vanilla transformer.
You can start with the sequence tagging model from PyTorch tutorial https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html .
As #Ilya Fedorov said, you can move to transformer models for potentially better performance.
I am currently looking into multi-labeling classification and I have some questions (and I couldn't find clear answers).
For the sake of clarity let's take an example : I want to classify images of vehicles (car, bus, truck, ...) and their make (Audi, Volkswagen, Ferrari, ...).
So I thought about training two independant CNN (one for the "type" classification and one fore the "make" classifiaction) but I thought it might be possible to train only one CNN on all the classes.
I read that people tend to use sigmoid function instead of softmax to do that. I understand that sigmoid does not sum up to 1 like softmax does but I dont understand in what doing that enables to do multi-labeling classification ?
My second question is : Is it possible to take into account that some classes are completly independant ?
Thridly, in term of performances (accuracy and time to give the classification for a new image), isn't training two independant better ?
Thank you for those who could give my some answers or some ideas :)
Softmax is a special output function; it forces the output vector to have a single large value. Now, training neural networks works by calculating an output vector, comparing that to a target vector, and back-propagating the error. There's no reason to restrict your target vector to a single large value, and for multi-labeling you'd use a 1.0 target for every label that applies. But in that case, using a softmax for the output layer will cause unintended differences between output and target, differences that are then back-propagated.
For the second part: you define the target vectors; you can encode any sort of dependency you like there.
Finally, no - a combined network performs better than the two halves would do independently. You'd only run two networks in parallel when there's a difference in network layout, e.g. a regular NN and CNN in parallel might be viable.
1 ad-jerry ad-bruckheimer ad-chase ad-premier ad-sept ad-th ad-clip ad-bruckheimer ad-chase page found
-1 ad-symptom ad-muscle ad-weakness ad-genetic ad-disease ad-symptom ad-include ad-search ad-learn page found
1 1:1 2:1 3:1 4:1 5:1 6:1 7:1 8:1 9:1
-1 8:1 9:1 429:1 430:1 431:1 432:1 433:1 434:1 435:1 436:1
I've text vector & its corresponding term vector, I want to learn a Decision Tree using ID3 algorithm in rapid miner, But I don't know how to process such data for ID3 Algorithm. I've tried to run ID3(Read CSV->ID3->Model) over term vector but I don't know whether It's working correct or not. Please help.
The ID3 Alogrithm does not accept numerical attributes. You will need to perform a preprocessing step or choose an alternative learning algorithm.
To convert numerical to nominal, you need to use one of the Discretize operators. These create bins into which the numerical values are placed. The attribute's name remains the same but its type changes to nominal. The value that an attribute has for a particular example is then dictated by the bin into which it was placed.