Is it a red flag that increasing the number of parameters makes the model less able to overfit small amounts of data? Unicorn Meta Zoo #1: Why another podcast? Announcing the arrival of Valued Associate #679: Cesar Manara 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsHow to set the number of neurons and layers in neural networksUnderstanding Tensorflow LSTM models?Neural net learning only one class?Implementing the Dependency Sensitive CNN (DSCNN ) in Kerasmultiple digit detectionHow to improve loss and avoid overfittingRegularization - Combine drop out with early stoppingNER at sentence level or document level?Using RNN (LSTM) for Gesture Recognition SystemComputer Vision: Handling dataset(3D data or scan) with different timestepsDoes it make sense to train a convolutional neural network on lo-res, use on hi-res pictures?
Reattaching fallen shelf to wall?
Air bladders in bat-like skin wings for better lift?
Why does Arg'[1. + I] return -0.5?
First instead of 1 when referencing
Unable to completely uninstall Zoom meeting app
Suing a Police Officer Instead of the Police Department
Could moose/elk survive in the Amazon forest?
Israeli soda type drink
Is Diceware more secure than a long passphrase?
Is accepting an invalid credit card number a security issue?
Why do games have consumables?
Is it possible to cast 2x Final Payment while sacrificing just one creature?
Was Dennis Ritchie being too modest in this quote about C and Pascal?
How to translate "red flag" into Spanish?
Long vowel quality before R
std::unique_ptr of base class holding reference of derived class does not show warning in gcc compiler while naked pointer shows it. Why?
Implementing 3DES algorithm in Java: is my code secure?
How to not starve gigantic beasts
Raising a bilingual kid. When should we introduce the majority language?
Older movie/show about humans on derelict alien warship which refuels by passing through a star
How much of a wave function must reside inside event horizon for it to be consumed by the black hole?
Retract an already submitted recommendation letter (written for an undergrad student)
Arriving in Atlanta after US Preclearance in Dublin. Will I go through TSA security in Atlanta to transfer to a connecting flight?
Did the Roman Empire have penal colonies?
Is it a red flag that increasing the number of parameters makes the model less able to overfit small amounts of data?
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsHow to set the number of neurons and layers in neural networksUnderstanding Tensorflow LSTM models?Neural net learning only one class?Implementing the Dependency Sensitive CNN (DSCNN ) in Kerasmultiple digit detectionHow to improve loss and avoid overfittingRegularization - Combine drop out with early stoppingNER at sentence level or document level?Using RNN (LSTM) for Gesture Recognition SystemComputer Vision: Handling dataset(3D data or scan) with different timestepsDoes it make sense to train a convolutional neural network on lo-res, use on hi-res pictures?
$begingroup$
I'm training a deep network (CNN-LSTM-CRF) for Named Entity Recognition. Is there a reason that increasing the number of parameters would make the network less able to overfit a small training set (~20 sentences), or does this indicate a serious bug in the code?
deep-learning nlp lstm cnn named-entity-recognition
asked Jul 9 '18 at 1:24
SolveItSolveIt
1162
$endgroup$
add a comment |
$begingroup$
I'm training a deep network (CNN-LSTM-CRF) for Named Entity Recognition. Is there a reason that increasing the number of parameters would make the network less able to overfit a small training set (~20 sentences), or does this indicate a serious bug in the code?
deep-learning nlp lstm cnn named-entity-recognition
asked Jul 9 '18 at 1:24
SolveItSolveIt
1162
$endgroup$
3
$begingroup$
No, it means you have to train it more.
$endgroup$
– Vaalizaadeh
Jul 9 '18 at 1:36
1
$begingroup$
"less able to overfit" -- explain how it is that you are measuring this, and we can help more.
$endgroup$
– Scott
Jul 9 '18 at 4:11
add a comment |
$begingroup$
I'm training a deep network (CNN-LSTM-CRF) for Named Entity Recognition. Is there a reason that increasing the number of parameters would make the network less able to overfit a small training set (~20 sentences), or does this indicate a serious bug in the code?
deep-learning nlp lstm cnn named-entity-recognition
asked Jul 9 '18 at 1:24
SolveItSolveIt
1162
$endgroup$
I'm training a deep network (CNN-LSTM-CRF) for Named Entity Recognition. Is there a reason that increasing the number of parameters would make the network less able to overfit a small training set (~20 sentences), or does this indicate a serious bug in the code?
deep-learning nlp lstm cnn named-entity-recognition
deep-learning nlp lstm cnn named-entity-recognition
asked Jul 9 '18 at 1:24
SolveItSolveIt
1162
asked Jul 9 '18 at 1:24
SolveItSolveIt
1162
asked Jul 9 '18 at 1:24
SolveItSolveIt
1162
asked Jul 9 '18 at 1:24
SolveItSolveIt
1162
asked Jul 9 '18 at 1:24
SolveItSolveIt
1162
1162
3
$begingroup$
No, it means you have to train it more.
$endgroup$
– Vaalizaadeh
Jul 9 '18 at 1:36
1
$begingroup$
"less able to overfit" -- explain how it is that you are measuring this, and we can help more.
$endgroup$
– Scott
Jul 9 '18 at 4:11
add a comment |
3
$begingroup$
No, it means you have to train it more.
$endgroup$
– Vaalizaadeh
Jul 9 '18 at 1:36
1
$begingroup$
"less able to overfit" -- explain how it is that you are measuring this, and we can help more.
$endgroup$
– Scott
Jul 9 '18 at 4:11
3
3
$begingroup$
No, it means you have to train it more.
$endgroup$
– Vaalizaadeh
Jul 9 '18 at 1:36
$begingroup$
No, it means you have to train it more.
$endgroup$
– Vaalizaadeh
Jul 9 '18 at 1:36
1
1
$begingroup$
"less able to overfit" -- explain how it is that you are measuring this, and we can help more.
$endgroup$
– Scott
Jul 9 '18 at 4:11
$begingroup$
"less able to overfit" -- explain how it is that you are measuring this, and we can help more.
$endgroup$
– Scott
Jul 9 '18 at 4:11
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
It is not necessarily a red flag. Of course, without seeing the code, it is impossible to say that for sure.
What is likely happening here is that adding parameters to your model, it makes it harder for it to converge to some minimum. More parameters roughly mean that your model is able to explain "more complex stuff". And since you have a small amount of data, the explanation should remain rather simple. Therefore, your model is trying to explain something simple in a complicated way, and it might not be easy to do so.
Also, are you using dropout or regularization? If yes, this might also be an issue as these are explicitly use to avoid overfitting.
answered Jul 10 '18 at 12:52
Valentin CalommeValentin Calomme
1,365523
$endgroup$
add a comment |
Your Answer
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "557"
;
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
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
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%2fdatascience.stackexchange.com%2fquestions%2f34174%2fis-it-a-red-flag-that-increasing-the-number-of-parameters-makes-the-model-less-a%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
$begingroup$
It is not necessarily a red flag. Of course, without seeing the code, it is impossible to say that for sure.
What is likely happening here is that adding parameters to your model, it makes it harder for it to converge to some minimum. More parameters roughly mean that your model is able to explain "more complex stuff". And since you have a small amount of data, the explanation should remain rather simple. Therefore, your model is trying to explain something simple in a complicated way, and it might not be easy to do so.
Also, are you using dropout or regularization? If yes, this might also be an issue as these are explicitly use to avoid overfitting.
answered Jul 10 '18 at 12:52
Valentin CalommeValentin Calomme
1,365523
$endgroup$
add a comment |
$begingroup$
It is not necessarily a red flag. Of course, without seeing the code, it is impossible to say that for sure.
What is likely happening here is that adding parameters to your model, it makes it harder for it to converge to some minimum. More parameters roughly mean that your model is able to explain "more complex stuff". And since you have a small amount of data, the explanation should remain rather simple. Therefore, your model is trying to explain something simple in a complicated way, and it might not be easy to do so.
Also, are you using dropout or regularization? If yes, this might also be an issue as these are explicitly use to avoid overfitting.
answered Jul 10 '18 at 12:52
Valentin CalommeValentin Calomme
1,365523
$endgroup$
add a comment |
$begingroup$
It is not necessarily a red flag. Of course, without seeing the code, it is impossible to say that for sure.
What is likely happening here is that adding parameters to your model, it makes it harder for it to converge to some minimum. More parameters roughly mean that your model is able to explain "more complex stuff". And since you have a small amount of data, the explanation should remain rather simple. Therefore, your model is trying to explain something simple in a complicated way, and it might not be easy to do so.
Also, are you using dropout or regularization? If yes, this might also be an issue as these are explicitly use to avoid overfitting.
answered Jul 10 '18 at 12:52
Valentin CalommeValentin Calomme
1,365523
$endgroup$
It is not necessarily a red flag. Of course, without seeing the code, it is impossible to say that for sure.
What is likely happening here is that adding parameters to your model, it makes it harder for it to converge to some minimum. More parameters roughly mean that your model is able to explain "more complex stuff". And since you have a small amount of data, the explanation should remain rather simple. Therefore, your model is trying to explain something simple in a complicated way, and it might not be easy to do so.
Also, are you using dropout or regularization? If yes, this might also be an issue as these are explicitly use to avoid overfitting.
answered Jul 10 '18 at 12:52
Valentin CalommeValentin Calomme
1,365523
answered Jul 10 '18 at 12:52
Valentin CalommeValentin Calomme
1,365523
answered Jul 10 '18 at 12:52
Valentin CalommeValentin Calomme
1,365523
answered Jul 10 '18 at 12:52
Valentin CalommeValentin Calomme
1,365523
1,365523
add a comment |
add a comment |
Thanks for contributing an answer to Data Science 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%2fdatascience.stackexchange.com%2fquestions%2f34174%2fis-it-a-red-flag-that-increasing-the-number-of-parameters-makes-the-model-less-a%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
3
$begingroup$
No, it means you have to train it more.
$endgroup$
– Vaalizaadeh
Jul 9 '18 at 1:36
1
$begingroup$
"less able to overfit" -- explain how it is that you are measuring this, and we can help more.
$endgroup$
– Scott
Jul 9 '18 at 4:11