What loss function to use when labels are probabilities? Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) Announcing the arrival of Valued Associate #679: Cesar Manara Unicorn Meta Zoo #1: Why another podcast?Why would neural networks be a particularly good framework for “embodied AI”?Understanding GAN Loss functionHelp with implementing Q-learning for a feedfoward network playing a video gameHow do I implement softmax forward propagation and backpropagation to replace sigmoid in a neural network?Gradient of hinge loss functionHow to understand marginal loglikelihood objective function as loss function (explanation of an article)?What is batch / batch size in neural networks?Comparing and studying Loss FunctionsLoss function spikesPredicting sine using LSTM: Small output range and delayed output?
Blender game recording at the wrong time
Can I throw a longsword at someone?
Strange behaviour of Check
Can I add database to AWS RDS MySQL without creating new instance?
How should I respond to a player wanting to catch a sword between their hands?
Single author papers against my advisor's will?
Stopping real property loss from eroding embankment
How to rotate it perfectly?
Active filter with series inductor and resistor - do these exist?
How do I keep my slimes from escaping their pens?
Jazz greats knew nothing of modes. Why are they used to improvise on standards?
Stars Make Stars
Direct Experience of Meditation
Is there a service that would inform me whenever a new direct route is scheduled from a given airport?
Need a suitable toxic chemical for a murder plot in my novel
What LEGO pieces have "real-world" functionality?
What do I do if technical issues prevent me from filing my return on time?
How do I automatically answer y in bash script?
Mortgage adviser recommends a longer term than necessary combined with overpayments
Biased dice probability question
I'm having difficulty getting my players to do stuff in a sandbox campaign
What can I do if my MacBook isn’t charging but already ran out?
Can smartphones with the same camera sensor have different image quality?
Unexpected result with right shift after bitwise negation
What loss function to use when labels are probabilities?
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
Announcing the arrival of Valued Associate #679: Cesar Manara
Unicorn Meta Zoo #1: Why another podcast?Why would neural networks be a particularly good framework for “embodied AI”?Understanding GAN Loss functionHelp with implementing Q-learning for a feedfoward network playing a video gameHow do I implement softmax forward propagation and backpropagation to replace sigmoid in a neural network?Gradient of hinge loss functionHow to understand marginal loglikelihood objective function as loss function (explanation of an article)?What is batch / batch size in neural networks?Comparing and studying Loss FunctionsLoss function spikesPredicting sine using LSTM: Small output range and delayed output?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
$begingroup$
What loss function is most appropriate when training a model with target values that are probabilities? For example, I have a 3-output model with x=[some features] and y=[0.2, 0.3, 0.5].
It seems like something like cross-entropy doesn't make sense here since it assumes that a single target is the correct label.
Would something like MSE (after applying softmax) make sense, or is there a better loss function?
neural-networks loss-functions probability-distribution
New contributor
$endgroup$
add a comment |
$begingroup$
What loss function is most appropriate when training a model with target values that are probabilities? For example, I have a 3-output model with x=[some features] and y=[0.2, 0.3, 0.5].
It seems like something like cross-entropy doesn't make sense here since it assumes that a single target is the correct label.
Would something like MSE (after applying softmax) make sense, or is there a better loss function?
neural-networks loss-functions probability-distribution
New contributor
$endgroup$
add a comment |
$begingroup$
What loss function is most appropriate when training a model with target values that are probabilities? For example, I have a 3-output model with x=[some features] and y=[0.2, 0.3, 0.5].
It seems like something like cross-entropy doesn't make sense here since it assumes that a single target is the correct label.
Would something like MSE (after applying softmax) make sense, or is there a better loss function?
neural-networks loss-functions probability-distribution
New contributor
$endgroup$
What loss function is most appropriate when training a model with target values that are probabilities? For example, I have a 3-output model with x=[some features] and y=[0.2, 0.3, 0.5].
It seems like something like cross-entropy doesn't make sense here since it assumes that a single target is the correct label.
Would something like MSE (after applying softmax) make sense, or is there a better loss function?
neural-networks loss-functions probability-distribution
neural-networks loss-functions probability-distribution
New contributor
New contributor
New contributor
asked 7 hours ago
Thomas JohnsonThomas Johnson
1133
1133
New contributor
New contributor
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Actually, the cross-entropy loss function would be appropriate here, since it measures the "distance" between a distribution $q$ and the "true" distribution $p$.
You are right, though, that using a loss function called "cross_entropy" in many APIs would be a mistake. This is because these functions, as you said, assume a one-hot label. You would need to use the general cross-entropy function,
$$H(p,q)=-sum_xin X p(x) log q(x).$$
$ $
Note that one-hot labels would mean that
$$
p(x) =
begincases
1 & textif x text is the true label\
0 & textotherwise
endcases$$
which causes the cross-entropy $H(p,q)$ to reduce to the form you're familiar with:
$$H(p,q) = -log q(x_label)$$
$endgroup$
add a comment |
Your Answer
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "658"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
noCode: true, onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Thomas Johnson is a new contributor. Be nice, and check out our Code of Conduct.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fai.stackexchange.com%2fquestions%2f11816%2fwhat-loss-function-to-use-when-labels-are-probabilities%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Actually, the cross-entropy loss function would be appropriate here, since it measures the "distance" between a distribution $q$ and the "true" distribution $p$.
You are right, though, that using a loss function called "cross_entropy" in many APIs would be a mistake. This is because these functions, as you said, assume a one-hot label. You would need to use the general cross-entropy function,
$$H(p,q)=-sum_xin X p(x) log q(x).$$
$ $
Note that one-hot labels would mean that
$$
p(x) =
begincases
1 & textif x text is the true label\
0 & textotherwise
endcases$$
which causes the cross-entropy $H(p,q)$ to reduce to the form you're familiar with:
$$H(p,q) = -log q(x_label)$$
$endgroup$
add a comment |
$begingroup$
Actually, the cross-entropy loss function would be appropriate here, since it measures the "distance" between a distribution $q$ and the "true" distribution $p$.
You are right, though, that using a loss function called "cross_entropy" in many APIs would be a mistake. This is because these functions, as you said, assume a one-hot label. You would need to use the general cross-entropy function,
$$H(p,q)=-sum_xin X p(x) log q(x).$$
$ $
Note that one-hot labels would mean that
$$
p(x) =
begincases
1 & textif x text is the true label\
0 & textotherwise
endcases$$
which causes the cross-entropy $H(p,q)$ to reduce to the form you're familiar with:
$$H(p,q) = -log q(x_label)$$
$endgroup$
add a comment |
$begingroup$
Actually, the cross-entropy loss function would be appropriate here, since it measures the "distance" between a distribution $q$ and the "true" distribution $p$.
You are right, though, that using a loss function called "cross_entropy" in many APIs would be a mistake. This is because these functions, as you said, assume a one-hot label. You would need to use the general cross-entropy function,
$$H(p,q)=-sum_xin X p(x) log q(x).$$
$ $
Note that one-hot labels would mean that
$$
p(x) =
begincases
1 & textif x text is the true label\
0 & textotherwise
endcases$$
which causes the cross-entropy $H(p,q)$ to reduce to the form you're familiar with:
$$H(p,q) = -log q(x_label)$$
$endgroup$
Actually, the cross-entropy loss function would be appropriate here, since it measures the "distance" between a distribution $q$ and the "true" distribution $p$.
You are right, though, that using a loss function called "cross_entropy" in many APIs would be a mistake. This is because these functions, as you said, assume a one-hot label. You would need to use the general cross-entropy function,
$$H(p,q)=-sum_xin X p(x) log q(x).$$
$ $
Note that one-hot labels would mean that
$$
p(x) =
begincases
1 & textif x text is the true label\
0 & textotherwise
endcases$$
which causes the cross-entropy $H(p,q)$ to reduce to the form you're familiar with:
$$H(p,q) = -log q(x_label)$$
answered 6 hours ago
Philip RaeisghasemPhilip Raeisghasem
988119
988119
add a comment |
add a comment |
Thomas Johnson is a new contributor. Be nice, and check out our Code of Conduct.
Thomas Johnson is a new contributor. Be nice, and check out our Code of Conduct.
Thomas Johnson is a new contributor. Be nice, and check out our Code of Conduct.
Thomas Johnson is a new contributor. Be nice, and check out our Code of Conduct.
Thanks for contributing an answer to Artificial Intelligence Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fai.stackexchange.com%2fquestions%2f11816%2fwhat-loss-function-to-use-when-labels-are-probabilities%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown