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turn off parts of features in a neural network?



2019 Community Moderator Election Results
2019 Moderator Election Q&A - QuestionnaireDebugging Neural Network for (Natural Language) TaggingTroubleshooting Neural Network ImplementationNeural Network for Multiple Output RegressionConvnet training error does not decreaseScalar input to neural network whose existence is conditionalchoosing sample points when approximating a function with a neural networkNeural network only converges when data cloud is close to 0How much neural network theory required to design one?Voice recognition with fourier transformation with audio input in pythonApplying ML to estimate parameters of an existing physical model










2












$begingroup$


Suppose I have a neural network which accepts two sets of features as inputs and generates corresponding outputs, for instance, generate average final grade from: 1. working hours for N students in a class, 2. mid-term grade for N students in the same class. During training, the neural network is trained with many different classes. What I would like to during inference is to turn off one set of the features (i.e. feed only working hours as inputs) and predict outputs using the same neural network. Obviously, setting the mid-term grades to be all 0s would not be a good option. I wonder if anyone know what would be a good way to do this?



Thanks!









share











$endgroup$




bumped to the homepage by Community 1 hour ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.














  • $begingroup$
    Why not only train of the features available at test time?
    $endgroup$
    – kbrose
    May 25 '18 at 16:52















2












$begingroup$


Suppose I have a neural network which accepts two sets of features as inputs and generates corresponding outputs, for instance, generate average final grade from: 1. working hours for N students in a class, 2. mid-term grade for N students in the same class. During training, the neural network is trained with many different classes. What I would like to during inference is to turn off one set of the features (i.e. feed only working hours as inputs) and predict outputs using the same neural network. Obviously, setting the mid-term grades to be all 0s would not be a good option. I wonder if anyone know what would be a good way to do this?



Thanks!









share











$endgroup$




bumped to the homepage by Community 1 hour ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.














  • $begingroup$
    Why not only train of the features available at test time?
    $endgroup$
    – kbrose
    May 25 '18 at 16:52













2












2








2





$begingroup$


Suppose I have a neural network which accepts two sets of features as inputs and generates corresponding outputs, for instance, generate average final grade from: 1. working hours for N students in a class, 2. mid-term grade for N students in the same class. During training, the neural network is trained with many different classes. What I would like to during inference is to turn off one set of the features (i.e. feed only working hours as inputs) and predict outputs using the same neural network. Obviously, setting the mid-term grades to be all 0s would not be a good option. I wonder if anyone know what would be a good way to do this?



Thanks!









share











$endgroup$




Suppose I have a neural network which accepts two sets of features as inputs and generates corresponding outputs, for instance, generate average final grade from: 1. working hours for N students in a class, 2. mid-term grade for N students in the same class. During training, the neural network is trained with many different classes. What I would like to during inference is to turn off one set of the features (i.e. feed only working hours as inputs) and predict outputs using the same neural network. Obviously, setting the mid-term grades to be all 0s would not be a good option. I wonder if anyone know what would be a good way to do this?



Thanks!







neural-network deep-learning





share














share












share



share








edited Sep 23 '18 at 17:44









Brian Spiering

4,2981129




4,2981129










asked Mar 26 '18 at 14:48









username123username123

1113




1113





bumped to the homepage by Community 1 hour ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.







bumped to the homepage by Community 1 hour ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.













  • $begingroup$
    Why not only train of the features available at test time?
    $endgroup$
    – kbrose
    May 25 '18 at 16:52
















  • $begingroup$
    Why not only train of the features available at test time?
    $endgroup$
    – kbrose
    May 25 '18 at 16:52















$begingroup$
Why not only train of the features available at test time?
$endgroup$
– kbrose
May 25 '18 at 16:52




$begingroup$
Why not only train of the features available at test time?
$endgroup$
– kbrose
May 25 '18 at 16:52










1 Answer
1






active

oldest

votes


















0












$begingroup$

As far as I know, you cannot do that.

First :




Obviously setting the mid-term grades to be all 0s would be be a good
option




No, actually that is a really bad option, neural networks do not understand magical parameters, meaning, if you put a 0 there it will think that the mid-term grades is 0, therefore it will probably give you a very low final grade prediction for you.



My advise, if you are very interested in doing that, create boolean factors, training inputs:



  1. working_hours

  2. mid-term_grade

  3. mid-term_grade_present

mid-term_grade_present should be a 1 when you have the mid-term_grade training data, and 0 when you do not have it. BTW, yes, generate training data without the mid-term_grade.



If you are working with non linear machine learning algorithms that should be enough, if you are using linear algorithms you should one hot encode the mid-term_grade_present and multiply it by the mid-term_grade, ending with something like this:



  1. working_hours

  2. mid-term_grade_present_1 * mid-term_grade

  3. mid-term_grade_present_0 * mid-term_grade




share









$endgroup$












  • $begingroup$
    Sorry that was a typo, it should be Obviously setting the mid-term grades to be all 0s would not be a good option.... I just updated it in the text. If I set mid-term_grade_present to be 0, what should I use as inputs, specifically what should be the data for mid-term_grade part? If it is the same with mid-term_grade_present=1 case, then this boolean flag basically has no effect, if it is something else, what should it be?
    $endgroup$
    – username123
    Mar 26 '18 at 15:23










  • $begingroup$
    if you have enough data you could try taking 20% of it and setting mid-term_grade_present=0
    $endgroup$
    – Kailegh
    Mar 26 '18 at 15:36










  • $begingroup$
    If I set mid-term_grade_present=0 I do not think mid_term_grade data would be ignored by the neural network since: 1. it is already included as part of inputs, 2. it is correlated with outputs.
    $endgroup$
    – username123
    Mar 26 '18 at 15:41










  • $begingroup$
    1.-it is a neural network, it can learn non-linear relationships, and learn features interactions, trust me, it will learn that when mid-term_grade_present=0 it cant rely on mid-term_grade 2.- it is not correlated, you randomly pick a 20% of the data and remove the mid-term_grade info, no correlation in there, give a try, but I sincerely think it should work
    $endgroup$
    – Kailegh
    Mar 26 '18 at 18:57










  • $begingroup$
    did this idea finally work for you? or do we have to come up with something else?
    $endgroup$
    – Kailegh
    Mar 27 '18 at 14:54


















1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









0












$begingroup$

As far as I know, you cannot do that.

First :




Obviously setting the mid-term grades to be all 0s would be be a good
option




No, actually that is a really bad option, neural networks do not understand magical parameters, meaning, if you put a 0 there it will think that the mid-term grades is 0, therefore it will probably give you a very low final grade prediction for you.



My advise, if you are very interested in doing that, create boolean factors, training inputs:



  1. working_hours

  2. mid-term_grade

  3. mid-term_grade_present

mid-term_grade_present should be a 1 when you have the mid-term_grade training data, and 0 when you do not have it. BTW, yes, generate training data without the mid-term_grade.



If you are working with non linear machine learning algorithms that should be enough, if you are using linear algorithms you should one hot encode the mid-term_grade_present and multiply it by the mid-term_grade, ending with something like this:



  1. working_hours

  2. mid-term_grade_present_1 * mid-term_grade

  3. mid-term_grade_present_0 * mid-term_grade




share









$endgroup$












  • $begingroup$
    Sorry that was a typo, it should be Obviously setting the mid-term grades to be all 0s would not be a good option.... I just updated it in the text. If I set mid-term_grade_present to be 0, what should I use as inputs, specifically what should be the data for mid-term_grade part? If it is the same with mid-term_grade_present=1 case, then this boolean flag basically has no effect, if it is something else, what should it be?
    $endgroup$
    – username123
    Mar 26 '18 at 15:23










  • $begingroup$
    if you have enough data you could try taking 20% of it and setting mid-term_grade_present=0
    $endgroup$
    – Kailegh
    Mar 26 '18 at 15:36










  • $begingroup$
    If I set mid-term_grade_present=0 I do not think mid_term_grade data would be ignored by the neural network since: 1. it is already included as part of inputs, 2. it is correlated with outputs.
    $endgroup$
    – username123
    Mar 26 '18 at 15:41










  • $begingroup$
    1.-it is a neural network, it can learn non-linear relationships, and learn features interactions, trust me, it will learn that when mid-term_grade_present=0 it cant rely on mid-term_grade 2.- it is not correlated, you randomly pick a 20% of the data and remove the mid-term_grade info, no correlation in there, give a try, but I sincerely think it should work
    $endgroup$
    – Kailegh
    Mar 26 '18 at 18:57










  • $begingroup$
    did this idea finally work for you? or do we have to come up with something else?
    $endgroup$
    – Kailegh
    Mar 27 '18 at 14:54















0












$begingroup$

As far as I know, you cannot do that.

First :




Obviously setting the mid-term grades to be all 0s would be be a good
option




No, actually that is a really bad option, neural networks do not understand magical parameters, meaning, if you put a 0 there it will think that the mid-term grades is 0, therefore it will probably give you a very low final grade prediction for you.



My advise, if you are very interested in doing that, create boolean factors, training inputs:



  1. working_hours

  2. mid-term_grade

  3. mid-term_grade_present

mid-term_grade_present should be a 1 when you have the mid-term_grade training data, and 0 when you do not have it. BTW, yes, generate training data without the mid-term_grade.



If you are working with non linear machine learning algorithms that should be enough, if you are using linear algorithms you should one hot encode the mid-term_grade_present and multiply it by the mid-term_grade, ending with something like this:



  1. working_hours

  2. mid-term_grade_present_1 * mid-term_grade

  3. mid-term_grade_present_0 * mid-term_grade




share









$endgroup$












  • $begingroup$
    Sorry that was a typo, it should be Obviously setting the mid-term grades to be all 0s would not be a good option.... I just updated it in the text. If I set mid-term_grade_present to be 0, what should I use as inputs, specifically what should be the data for mid-term_grade part? If it is the same with mid-term_grade_present=1 case, then this boolean flag basically has no effect, if it is something else, what should it be?
    $endgroup$
    – username123
    Mar 26 '18 at 15:23










  • $begingroup$
    if you have enough data you could try taking 20% of it and setting mid-term_grade_present=0
    $endgroup$
    – Kailegh
    Mar 26 '18 at 15:36










  • $begingroup$
    If I set mid-term_grade_present=0 I do not think mid_term_grade data would be ignored by the neural network since: 1. it is already included as part of inputs, 2. it is correlated with outputs.
    $endgroup$
    – username123
    Mar 26 '18 at 15:41










  • $begingroup$
    1.-it is a neural network, it can learn non-linear relationships, and learn features interactions, trust me, it will learn that when mid-term_grade_present=0 it cant rely on mid-term_grade 2.- it is not correlated, you randomly pick a 20% of the data and remove the mid-term_grade info, no correlation in there, give a try, but I sincerely think it should work
    $endgroup$
    – Kailegh
    Mar 26 '18 at 18:57










  • $begingroup$
    did this idea finally work for you? or do we have to come up with something else?
    $endgroup$
    – Kailegh
    Mar 27 '18 at 14:54













0












0








0





$begingroup$

As far as I know, you cannot do that.

First :




Obviously setting the mid-term grades to be all 0s would be be a good
option




No, actually that is a really bad option, neural networks do not understand magical parameters, meaning, if you put a 0 there it will think that the mid-term grades is 0, therefore it will probably give you a very low final grade prediction for you.



My advise, if you are very interested in doing that, create boolean factors, training inputs:



  1. working_hours

  2. mid-term_grade

  3. mid-term_grade_present

mid-term_grade_present should be a 1 when you have the mid-term_grade training data, and 0 when you do not have it. BTW, yes, generate training data without the mid-term_grade.



If you are working with non linear machine learning algorithms that should be enough, if you are using linear algorithms you should one hot encode the mid-term_grade_present and multiply it by the mid-term_grade, ending with something like this:



  1. working_hours

  2. mid-term_grade_present_1 * mid-term_grade

  3. mid-term_grade_present_0 * mid-term_grade




share









$endgroup$



As far as I know, you cannot do that.

First :




Obviously setting the mid-term grades to be all 0s would be be a good
option




No, actually that is a really bad option, neural networks do not understand magical parameters, meaning, if you put a 0 there it will think that the mid-term grades is 0, therefore it will probably give you a very low final grade prediction for you.



My advise, if you are very interested in doing that, create boolean factors, training inputs:



  1. working_hours

  2. mid-term_grade

  3. mid-term_grade_present

mid-term_grade_present should be a 1 when you have the mid-term_grade training data, and 0 when you do not have it. BTW, yes, generate training data without the mid-term_grade.



If you are working with non linear machine learning algorithms that should be enough, if you are using linear algorithms you should one hot encode the mid-term_grade_present and multiply it by the mid-term_grade, ending with something like this:



  1. working_hours

  2. mid-term_grade_present_1 * mid-term_grade

  3. mid-term_grade_present_0 * mid-term_grade





share











share


share










answered Mar 26 '18 at 15:10









KaileghKailegh

813




813











  • $begingroup$
    Sorry that was a typo, it should be Obviously setting the mid-term grades to be all 0s would not be a good option.... I just updated it in the text. If I set mid-term_grade_present to be 0, what should I use as inputs, specifically what should be the data for mid-term_grade part? If it is the same with mid-term_grade_present=1 case, then this boolean flag basically has no effect, if it is something else, what should it be?
    $endgroup$
    – username123
    Mar 26 '18 at 15:23










  • $begingroup$
    if you have enough data you could try taking 20% of it and setting mid-term_grade_present=0
    $endgroup$
    – Kailegh
    Mar 26 '18 at 15:36










  • $begingroup$
    If I set mid-term_grade_present=0 I do not think mid_term_grade data would be ignored by the neural network since: 1. it is already included as part of inputs, 2. it is correlated with outputs.
    $endgroup$
    – username123
    Mar 26 '18 at 15:41










  • $begingroup$
    1.-it is a neural network, it can learn non-linear relationships, and learn features interactions, trust me, it will learn that when mid-term_grade_present=0 it cant rely on mid-term_grade 2.- it is not correlated, you randomly pick a 20% of the data and remove the mid-term_grade info, no correlation in there, give a try, but I sincerely think it should work
    $endgroup$
    – Kailegh
    Mar 26 '18 at 18:57










  • $begingroup$
    did this idea finally work for you? or do we have to come up with something else?
    $endgroup$
    – Kailegh
    Mar 27 '18 at 14:54
















  • $begingroup$
    Sorry that was a typo, it should be Obviously setting the mid-term grades to be all 0s would not be a good option.... I just updated it in the text. If I set mid-term_grade_present to be 0, what should I use as inputs, specifically what should be the data for mid-term_grade part? If it is the same with mid-term_grade_present=1 case, then this boolean flag basically has no effect, if it is something else, what should it be?
    $endgroup$
    – username123
    Mar 26 '18 at 15:23










  • $begingroup$
    if you have enough data you could try taking 20% of it and setting mid-term_grade_present=0
    $endgroup$
    – Kailegh
    Mar 26 '18 at 15:36










  • $begingroup$
    If I set mid-term_grade_present=0 I do not think mid_term_grade data would be ignored by the neural network since: 1. it is already included as part of inputs, 2. it is correlated with outputs.
    $endgroup$
    – username123
    Mar 26 '18 at 15:41










  • $begingroup$
    1.-it is a neural network, it can learn non-linear relationships, and learn features interactions, trust me, it will learn that when mid-term_grade_present=0 it cant rely on mid-term_grade 2.- it is not correlated, you randomly pick a 20% of the data and remove the mid-term_grade info, no correlation in there, give a try, but I sincerely think it should work
    $endgroup$
    – Kailegh
    Mar 26 '18 at 18:57










  • $begingroup$
    did this idea finally work for you? or do we have to come up with something else?
    $endgroup$
    – Kailegh
    Mar 27 '18 at 14:54















$begingroup$
Sorry that was a typo, it should be Obviously setting the mid-term grades to be all 0s would not be a good option.... I just updated it in the text. If I set mid-term_grade_present to be 0, what should I use as inputs, specifically what should be the data for mid-term_grade part? If it is the same with mid-term_grade_present=1 case, then this boolean flag basically has no effect, if it is something else, what should it be?
$endgroup$
– username123
Mar 26 '18 at 15:23




$begingroup$
Sorry that was a typo, it should be Obviously setting the mid-term grades to be all 0s would not be a good option.... I just updated it in the text. If I set mid-term_grade_present to be 0, what should I use as inputs, specifically what should be the data for mid-term_grade part? If it is the same with mid-term_grade_present=1 case, then this boolean flag basically has no effect, if it is something else, what should it be?
$endgroup$
– username123
Mar 26 '18 at 15:23












$begingroup$
if you have enough data you could try taking 20% of it and setting mid-term_grade_present=0
$endgroup$
– Kailegh
Mar 26 '18 at 15:36




$begingroup$
if you have enough data you could try taking 20% of it and setting mid-term_grade_present=0
$endgroup$
– Kailegh
Mar 26 '18 at 15:36












$begingroup$
If I set mid-term_grade_present=0 I do not think mid_term_grade data would be ignored by the neural network since: 1. it is already included as part of inputs, 2. it is correlated with outputs.
$endgroup$
– username123
Mar 26 '18 at 15:41




$begingroup$
If I set mid-term_grade_present=0 I do not think mid_term_grade data would be ignored by the neural network since: 1. it is already included as part of inputs, 2. it is correlated with outputs.
$endgroup$
– username123
Mar 26 '18 at 15:41












$begingroup$
1.-it is a neural network, it can learn non-linear relationships, and learn features interactions, trust me, it will learn that when mid-term_grade_present=0 it cant rely on mid-term_grade 2.- it is not correlated, you randomly pick a 20% of the data and remove the mid-term_grade info, no correlation in there, give a try, but I sincerely think it should work
$endgroup$
– Kailegh
Mar 26 '18 at 18:57




$begingroup$
1.-it is a neural network, it can learn non-linear relationships, and learn features interactions, trust me, it will learn that when mid-term_grade_present=0 it cant rely on mid-term_grade 2.- it is not correlated, you randomly pick a 20% of the data and remove the mid-term_grade info, no correlation in there, give a try, but I sincerely think it should work
$endgroup$
– Kailegh
Mar 26 '18 at 18:57












$begingroup$
did this idea finally work for you? or do we have to come up with something else?
$endgroup$
– Kailegh
Mar 27 '18 at 14:54




$begingroup$
did this idea finally work for you? or do we have to come up with something else?
$endgroup$
– Kailegh
Mar 27 '18 at 14:54



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ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result