Questions tagged [cross-entropy]

For questions related to the concept of cross-entropy in the context of artificial intelligence. For example, when the cross-entropy is used as a loss function to train a neural network.

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How do I implement the cross-entropy-method for a RL environment with a continuous action space?

I found many tutorials and posts on how to solve RL environments with discrete action spaces using the cross entropy method (e.g., in this blog post for the OpenAI Gym frozen lake environment). ...
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Is it possible to get this loss with spikes, when training an LSTM with the cross-entropy on a multi-class classification problem of a time series?

The main question here will be "should I look for a bug?" My setup is a time series multiclass classification task, labeled per frame. I am using an LSTM, feeding inputs and using ...
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What error should I use for RNN?

I'm relatively new to machine learning, and I don't know what error I should use for an RNN. I want to use a simple Elman RNN to predict the cases of Covid-19 there will be in a hospital for the next ...
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How to compute the gradient of the cross-entropy loss function with respect to the parameters with softmax activation function?

I've seen plenty of examples of people doing Sigmoid + MSE backpropagation implementations, yet I do not seem to understand how to implement backpropagation as stated in the title in the case of multi-...
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Have I understood the loss function from the original U-Net paper correctly?

In the original U-Net paper, it is written The energy function is computed by a pixel-wise soft-max over the final feature map combined with the cross entropy loss function. ... $$ E=\sum_{\mathbf{x} ...
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Mathematical Analysis of the Loss function of GAN

I was pondering on loss function of GAN and following thing turned out \begin{aligned} L(D, G) & = \mathbb{E}_{x \sim p_{r}(x)} [\log D(x)] + \mathbb{E}_{x \sim p_g(x)} [\log(1 - D(x)] \\ & =...
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How does implementation of VAE's objective function equate to ELBO?

For a lot of VAE implementations I've seen in code, it's not really obvious to me how it equates to ELBO. $$L(X)=H(Q)-H(Q:P(X,Z))=\sum_ZQ(Z)logP(Z,X)-\sum_ZQ(Z)log(Q(Z))$$ The above is the definition ...
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Understanding the MuZero loss function for a two-player game

This question is connected to a question that I asked some time ago. This is how I understood the training procedure takes place (please correct any conceptual mistakes here): Many complete games are ...
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174 views

Why are there two versions of softmax cross entropy? Which one to use in what situation?

I have seen 2 forms of softmax cross-entropy loss and are confused by the two. Which one is the right one? For example in this Quora answer, there are 2 answers: $L(\mathbf{w})=\frac{1}{N} \sum_{n=1}^...
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What is the advantage of using cross entropy loss & softmax?

I am trying to do the standard MNIST dataset image recognition test with a standard feed forward NN, but my network failed pretty badly. Now I have debugged it quite a lot and found & fixed some ...
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Loss function for better class separability in multi class classification

So I am trying to enforce better separability in my deep learning model and was wondering what I can use besides cross entropy loss to do that? Could maybe using logarithm with different basis in ...
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Model Based rl and cross entropy method with nonlinear function approximators

Pseudo code for Cross entropy method according to youtube lecture 32:55 Initialize $\mu \in R^{d}, \sigma \in R^{d}$ iteration 1,2,... Collect n samples of $\theta_{i} \sim N(\mu,diag(\sigma))$ ...
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How do you manage negative rewards in policy gradients?

This old question has no definitive answer yet, that's why I am asking it here again. I also asked this same question here. If I'm doing policy gradient in Keras, using a loss of the form: ...
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1answer
37 views

How are weights for weighted x-entropy loss on imbalanced data calculated?

I am trying to build a classifier which should be trained with the cross entropy loss. The training data is highly class-imbalanced. To tackle this, I've gone through the advice of the tensorflow docs ...
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Is maximum likelihood estimation meaningless for a dataset of only outliers?

From my understanding, maximum likelihood estimation chooses the set of parameters for the estimator that maximizes likelihood with the ground truth distribution. I always interpreted it as the ...
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Can the cross-entropy loss be used for a NLP task with LSTM?

I am trying to build an LSTM model to generate Shakspeare-like poems. I have training set $\{s_1,s_2, \dots,s_m\}$, which are sentences of Shakespeare poems, and each sentence contains words $\{w_1,...
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1answer
58 views

Are sentences from the same document independent and identically distributed?

I am trying to build an LSTM model to generate Shakspeare-like poems. I have data set $\{s_1, s_2, \dots, s_m \}$, which are sentences of Shakespeare poems, and each sentence contains words $\{w_1, ...
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1answer
49 views

How should I penalize the model proportionally to the error?

I am making an MNIST classifier. I am using categorical cross-entropy as my loss function. I want to make it so that if the correct label is 3, then it will penalize the model less heavily if it ...
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Why does PyTorch use a different formula for the cross-entropy?

In my understanding, the formula to calculate the cross-entropy is $$ H(p,q) = - \sum p_i \log(q_i) $$ But in PyTorch nn.CrossEntropyLoss is calculated using this ...
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How to formalize learning in terms of information theory?

Consider the following game on a MNIST dataset: There are 60000 images. You can pick any 1000 images and train your Neural Network without access to the rest of images. Your final result is ...
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283 views

Why does the binary cross-entropy work better than categorical cross-entropy in a multi-class single label problem?

I was just doing a simple NN example with the fashion MNIST dataset, where I was getting 97% accuracy, when I noticed that I was using Binary cross-entropy instead of categorical cross-entropy by ...
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Why do non-linear activation functions that produce values larger than 1 or smaller than 0 work?

Why do non-linear activation functions that produce values larger than 1 or smaller than 0 work? My understanding is that neurons can only produce values between 0 and 1, and that this assumption can ...
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How do I handle negative rewards in policy gradients with the cross-entropy loss function?

I am using policy gradients in my reinforcement learning algorithm, and occasionally my environment provides a severe penalty (i.e. negative reward) when a wrong move is made. I'm using a neural ...