All Questions
Tagged with ce or cross-entropy
37 questions
0
votes
1
answer
22
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Is there any scientific work that has measured the perplexity achievable using just word2vec?
I know word2vec is not enough to achieve high quality text prediction just by itself. But has any scientific work tried to do it anyway, just to know what the baseline is that you have to beat with ...
- 111
1
vote
0
answers
21
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What about the loss and custom metric with per-pair weights in multi-class classification?
Let's suppose that we have a multi-class classification problem with 5 classes: 0, 1, 2, 3, 4. The order is not random, they are neighbors. For example, imagine that a labelling is 1. If the ...
- 111
0
votes
0
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13
views
Incredibly High CrossEntropyLoss in Sequence-to-Sequence Generation
I'm trying to do SMILES chemical representation prediction from a large dataset (Around 5M Samples) to teach it do predict another downstream task. The model's part responsible for generating the data ...
- 101
0
votes
0
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80
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Reinforcement learning - calculating policy gradient using cross entropy loss
I am writing a program that uses reinforcement learning and the policy gradient method to play Pong. It basically extends Andrej Karpathy's version (https://gist.github.com/karpathy/...
- 1
2
votes
2
answers
162
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Why is the cross entropy formula different from the binary cross entropy?
why cross entropy formulation is different with binary cross entropy?
cross entorpy loss
$$
H_p(q) = -\sum_{q_i}^C [q_i \log(p_i)]
$$
binary cross entorpy
$$
-\sum [k_i \log(p_i)+(1-k_i) \log(1-p_i)]
...
1
vote
1
answer
102
views
What is the lowest possible loss for a language model?
Example: Suppose a character-level language model (three input letters to predict the next one), trained on a dataset which contains three instances of the sequence ...
- 179
1
vote
2
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198
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Backpropagation with CrossEntropy and Softmax, HOW?
Let Zs be the input of the output layer (for example, Z1 is the input of the first neuron in the output layer), Os be the output of the output layer (which are actually the results of applying the ...
- 11
0
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1
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456
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What is the correct loss function for binary classification: Cross entropy or Binary cross entropy?
Let's say I have a binary classification problem and I want to solve it by means of FC neural net. So which approach will be correct: 1) define the last layer of NN like this ...
- 23
0
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0
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455
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How to optimize ELBO(VAE's loss function)?
Suppose we've got the following formula:
$\log p(x;\theta)=\mathbb{E}_{q(z|x;\phi)}[\log p(x,z;\theta)-\log q(z|x;\phi)]+KL(q(z|x;\phi)||p(z|x;\theta))\\ \geq \mathbb{E}_{q(z|x;\phi)}[\log p(x,z;\...
- 11
1
vote
1
answer
78
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What loss function will be correlated with classification metrics?
Recently I developed a custom training algorithm for deep learning models, based on evolutionary algorithms. Details are not important, except that it also uses decreasing regular cross entropy loss ...
- 1,280
1
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1
answer
1k
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why cross entropy loss has to be multiplied by a batch size during an evaluation in transformer model?
I am trying to look through a code of the transformer model from Pytorch. However,
I do not understand why batch size needs to multiply with cross-entropy loss given that loss is calculated based on ...
- 11
1
vote
1
answer
196
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Trouble writing the backpropagation algorithm in python through crossentropy and softmax
so I am writing my own neural network library for a class project and I got everything working for a simple 2-class test using the distance (L2) cost function. I wanted to get a similar result using ...
- 121
3
votes
0
answers
3k
views
Focal Loss vs Weighted Cross Entropy Loss
Weighted Focal Loss is defined like so
$FL(p_t) = -\alpha_t log(p_t) (1-p_t)^\gamma $
Whereas weighted Cross Entropy Loss is defined like so
$CE(p_t) = -\alpha_t log(p_t)$
Some blog posts try to ...
- 789
-1
votes
1
answer
245
views
Why is the cross-entropy a cost function?
The question looks foolish, but I think cross-entropy is somewhat weird as a cost function.
As a cost function for linear regression, the mean square error $ \sum_{i=1}^{n} (y_i - (ax_i+b)) ^2$ seems ...
- 125
5
votes
3
answers
6k
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In logistic regression, why is the binary cross-entropy loss function convex?
I am studying logistic regression for binary classification.
The loss function used is cross-entropy. For a given input $x$, if our model outputs $\hat{y}$ instead of $y$, the loss is given by
$$\text{...
- 3,990
3
votes
0
answers
379
views
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).
...
- 143
0
votes
1
answer
245
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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 ...
- 26
1
vote
1
answer
733
views
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-...
- 115
1
vote
1
answer
774
views
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.
...
$$
E=\sum_{\mathbf{x} ...
- 635
3
votes
2
answers
332
views
Where is the mistake in my derivation of the GAN loss function?
I was pondering on the loss function of GAN, and the following thing turned out
\begin{aligned}
L(D, G)
& = \mathbb{E}_{x \sim p_{r}(x)} [\log D(x)] + \mathbb{E}_{x \sim p_g(x)} [\log(1 - D(x)] \...
- 324
2
votes
1
answer
2k
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How does the implementation of the VAE's objective function equate to ELBO?
For a lot of VAE implementations I've seen in code, it's not really obvious to me how it equates to ELBO.
$$L(X)=H(Q)-H(Q:P(X,Z))=\sum_ZQ(Z)logP(Z,X)-\sum_ZQ(Z)log(Q(Z))$$
The above is the definition ...
- 805
2
votes
1
answer
321
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Why are there two versions of softmax cross entropy? Which one to use in what situation?
I have seen 2 forms of softmax cross-entropy loss and are confused by the two. Which one is the right one?
For example in this Quora answer, there are 2 answers:
$L(\mathbf{w})=\frac{1}{N} \sum_{n=1}^...
- 123
5
votes
2
answers
2k
views
What is the advantage of using cross entropy loss & softmax?
I am trying to do the standard MNIST dataset image recognition test with a standard feed forward NN, but my network failed pretty badly. Now I have debugged it quite a lot and found & fixed some ...
- 445
8
votes
1
answer
1k
views
Which loss function should I use in REINFORCE, and what are the labels?
I understand that this is the update for the parameters of a policy in REINFORCE:
$$
\Delta \theta_{t}=\alpha \nabla_{\theta} \log \pi_{\theta}\left(a_{t} \mid s_{t}\right) v_{t},
$$
where $v_t$ is ...
- 590
2
votes
1
answer
2k
views
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:
...
- 121
2
votes
1
answer
2k
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
...
- 21
3
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0
answers
70
views
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 ...
- 1,419
2
votes
0
answers
222
views
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,...
- 43
1
vote
1
answer
75
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, ...
- 43
2
votes
1
answer
131
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 ...
- 53
1
vote
1
answer
154
views
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 ...
- 2,819
4
votes
1
answer
127
views
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 ...
- 141
4
votes
1
answer
620
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 ...
- 41
7
votes
1
answer
8k
views
How is division by zero avoided when implementing back-propagation for a neural network with sigmoid at the output neuron?
I am building a neural network for which I am using the sigmoid function as the activation function for the single output neuron at the end. Since the sigmoid function is known to take any number and ...
- 73
2
votes
2
answers
555
views
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 ...
- 59
21
votes
1
answer
5k
views
Why has the cross-entropy become the classification standard loss function and not Kullback-Leibler divergence?
The cross-entropy is identical to the KL divergence plus the entropy of the target distribution. The KL divergence equals zero when the two distributions are the same, which seems more intuitive to me ...
- 321
10
votes
2
answers
12k
views
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 ...
- 209