Questions tagged [loss-function]
A function used to quantify the difference between observed data and predicted values according to a model. Minimization of loss functions is a way to estimate the parameters of the model.
528 questions
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Custom loss function not behaving as expected in PyTorch but does in TensorFlow
I tried modifying the reconstruction loss such that values that are pushed out of bounds do not contribute to the loss and it works as expected in tensorflow after training an autoencoder. However, ...
1vote
0answers
29views
Using a differentiable Self-Organizing Map loss in a CNN
I've been trying to aggregate a normal CNN loss with a loss that quantifies how well we can cluster the second-to-last layer embeddings (i.e. feed the embeddings to a 2D Self Organizing Map (SOM) and ...
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5votes
2answers
47views
Does it make sense to mix the labels in each batch?
For a binary classification model, When training a deep model, at each training step, the model receives a batch (i.e batch of size 32 samples). Let's assume that in each training batch there are ...
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Logistic Regression Loss can be zero? Question from a Test
I have a question from a test, I managed to solve it, but something feels weird... Prove it is false: If all the samples for Logistic Regression are categorized false, so the training loss is 0. What ...
3votes
1answer
88views
How to incorporate weights (probability measurements) of data into a mean squared error loss function
I am training a CNN to regress on 4 targets related to a given image. Within the image is a point of interest whose position can be defined by phi, and theta (corresponding to x and y of a normal ...
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10views
Numerical precision in Flux.jl
I am trying to study ANN training in terms of dynamical systems framework, by treating the model as the system, and the training as the time evolution dynamics. As an extension, I tried to make the ...
5votes
2answers
644views
Is there any advantage of a lower value of a loss function?
I have two loss functions $\mathcal{L}_1$ and $\mathcal{L}_2$ to train my model. The model is predominantly a classification model. Both $\mathcal{L}_1$ and $\mathcal{L}_2$ takes are two variants of ...
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2votes
1answer
69views
Taking into account instance cost in learning?
I am generally trying to take into account costs in learning. The set-up is as follows: a statistical learning problem with usuall X and y, where y is imbalanced (roughly 1% of ones). Scikit learn ...
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Per Channel loss or Per Sample Loss
I am currently tackling a semantic segmentation problem where, for each sample, my goal is to segment two masks corresponding to two objects. Notably, object two is typically located inside object one,...
3votes
1answer
42views
Why softmax training is more stable
I'm wondering about which activation function will be easier to train with (get better accuracy / smallest loss) - with SoftMax or sigmoid (for multiclass classification problem) According to: https://...
- 689
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A loss and metrics problem while training a cnn model
i'm having a problem with my custom loss and metrics . My purpose is to train a cnn model on images and with the tangente of the angle orientation of the object in my image and i have a column that ...
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Loss function for multi label classification with multiple digits in mnist
As the title suggests, i'm looking for a loss function to apply to a modified mnist dataset which has multiple digits. I need to predict all the digits in the image. Each image has 1-3 digits and each ...
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1answer
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What exactly is a true distribution in ML problems?
I define a classification problem as a problem of calculating a function $h$ that approximates a function $f$ that classifies data. The approximation is calculated by taking a set of training samples ...
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Which type of GAN will be appropriate for regression?
I designed a cGAN for a regression problem and prediction of some values like the mass. I've tried different activation functions and optimizers as well as loss functions. but my model is not stable ...
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Ranking discriminator and loss
A ranking discriminator D(z1, z2, z3 ) ∈ [0, 1] that should be high if _z1 ∼ p1 , z2 ∼ p2 , z3 ∼ p3 _ and low otherwise. For finding the loss and optimal discriminator D(z1 , z2 , z3 ) should I modify ...
