How do I train the Convolutional Neural Network with negative and positive elements as the input of the first...












0















Just I am curious why I have to scale the testing set on the testing set, and not on the training set when I’m training a model on, for example, CNN?!
Or am I wrong? And I still have to scale it on the training set.
Also, can I train a dataset in the CNN that contents positive and negative elements as the first input of the network?
Any answers with reference will be really appreciated.






python tensorflow conv-neural-network







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  • you scale both train and test data

    – Mitch Wheat
    Nov 21 '18 at 3:06











  • I know that. My question is why should I scale the testing data on the testing data instead of the training data?

    – Protocol313
    Nov 21 '18 at 3:12


















0















Just I am curious why I have to scale the testing set on the testing set, and not on the training set when I’m training a model on, for example, CNN?!
Or am I wrong? And I still have to scale it on the training set.
Also, can I train a dataset in the CNN that contents positive and negative elements as the first input of the network?
Any answers with reference will be really appreciated.






python tensorflow conv-neural-network







share|improve this question

























  • you scale both train and test data

    – Mitch Wheat
    Nov 21 '18 at 3:06











  • I know that. My question is why should I scale the testing data on the testing data instead of the training data?

    – Protocol313
    Nov 21 '18 at 3:12
















0












0








0


2






Just I am curious why I have to scale the testing set on the testing set, and not on the training set when I’m training a model on, for example, CNN?!
Or am I wrong? And I still have to scale it on the training set.
Also, can I train a dataset in the CNN that contents positive and negative elements as the first input of the network?
Any answers with reference will be really appreciated.






python tensorflow conv-neural-network







share|improve this question
















Just I am curious why I have to scale the testing set on the testing set, and not on the training set when I’m training a model on, for example, CNN?!
Or am I wrong? And I still have to scale it on the training set.
Also, can I train a dataset in the CNN that contents positive and negative elements as the first input of the network?
Any answers with reference will be really appreciated.






python tensorflow conv-neural-network




python tensorflow conv-neural-network






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 21 '18 at 3:15







Protocol313

















asked Nov 21 '18 at 3:00









Protocol313Protocol313

44




44













  • you scale both train and test data

    – Mitch Wheat
    Nov 21 '18 at 3:06











  • I know that. My question is why should I scale the testing data on the testing data instead of the training data?

    – Protocol313
    Nov 21 '18 at 3:12





















  • you scale both train and test data

    – Mitch Wheat
    Nov 21 '18 at 3:06











  • I know that. My question is why should I scale the testing data on the testing data instead of the training data?

    – Protocol313
    Nov 21 '18 at 3:12



















you scale both train and test data

– Mitch Wheat
Nov 21 '18 at 3:06





you scale both train and test data

– Mitch Wheat
Nov 21 '18 at 3:06













I know that. My question is why should I scale the testing data on the testing data instead of the training data?

– Protocol313
Nov 21 '18 at 3:12







I know that. My question is why should I scale the testing data on the testing data instead of the training data?

– Protocol313
Nov 21 '18 at 3:12














3 Answers
3






active

oldest

votes


















0














Only because I cannot comment (reputation barrier), I am writing it here as an answer. Scaling data depends upon the requirement as well the feed/data you got. Test data gets scaled with Test data only, because Test data don't have the Target variable (one less feature in Test data). If we scale our Training data with new Test data, our model will not be able to correlate with any target variable and thus fail to learn. So the key difference is the existence of Target variable.






share|improve this answer
























  • Thanks for your answer. I knew the basic concepts of the answer, but I have not found any references yet. Do you have any reference for the answer? Please. Also, do you have an answer to the second one? Thanks

    – Protocol313
    Nov 21 '18 at 3:37













  • Few worthy references that I could find are Article1, Article2 and Article3 (kindly google for retraining ML models for more references). For second one, kindly detail your question with more info on what you mean by positive/negative elements (that too being passed in a CNN?)

    – Random Nerd
    Nov 21 '18 at 4:27



















0














We usually have 3 types of datasets for getting a model trained,




  1. Training Dataset

  2. Validation Dataset

  3. Test Dataset


Training Dataset



This should be an evenly distributed data set which covers all varieties of data. If your train with more epochs, the model will get used to the training dataset and will only give proper proper prediction on the training dataset and this is called Overfitting. Only way to keep a check on overfitting is by having other datasets which the model has never been trained on.



Validation Dataset



This can be used fine tune model hyperparameters



Test Dataset



This is the dataset which the model has not been trained on has never been a part of deciding the hyperparameters and will give the reality of how the model is performing.






share|improve this answer































    0














    If scaling and normalization is used, the testing set should use the same parameters used during training.
    A good answer that links to that: https://datascience.stackexchange.com/questions/27615/should-we-apply-normalization-to-test-data-as-well



    Also, some models tend to require normalization and others do not.
    The Neural Network architectures are normally robust and might not need normalization.






    share|improve this answer























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      3 Answers
      3






      active

      oldest

      votes








      3 Answers
      3






      active

      oldest

      votes









      active

      oldest

      votes






      active

      oldest

      votes









      0














      Only because I cannot comment (reputation barrier), I am writing it here as an answer. Scaling data depends upon the requirement as well the feed/data you got. Test data gets scaled with Test data only, because Test data don't have the Target variable (one less feature in Test data). If we scale our Training data with new Test data, our model will not be able to correlate with any target variable and thus fail to learn. So the key difference is the existence of Target variable.






      share|improve this answer
























      • Thanks for your answer. I knew the basic concepts of the answer, but I have not found any references yet. Do you have any reference for the answer? Please. Also, do you have an answer to the second one? Thanks

        – Protocol313
        Nov 21 '18 at 3:37













      • Few worthy references that I could find are Article1, Article2 and Article3 (kindly google for retraining ML models for more references). For second one, kindly detail your question with more info on what you mean by positive/negative elements (that too being passed in a CNN?)

        – Random Nerd
        Nov 21 '18 at 4:27
















      0














      Only because I cannot comment (reputation barrier), I am writing it here as an answer. Scaling data depends upon the requirement as well the feed/data you got. Test data gets scaled with Test data only, because Test data don't have the Target variable (one less feature in Test data). If we scale our Training data with new Test data, our model will not be able to correlate with any target variable and thus fail to learn. So the key difference is the existence of Target variable.






      share|improve this answer
























      • Thanks for your answer. I knew the basic concepts of the answer, but I have not found any references yet. Do you have any reference for the answer? Please. Also, do you have an answer to the second one? Thanks

        – Protocol313
        Nov 21 '18 at 3:37













      • Few worthy references that I could find are Article1, Article2 and Article3 (kindly google for retraining ML models for more references). For second one, kindly detail your question with more info on what you mean by positive/negative elements (that too being passed in a CNN?)

        – Random Nerd
        Nov 21 '18 at 4:27














      0












      0








      0







      Only because I cannot comment (reputation barrier), I am writing it here as an answer. Scaling data depends upon the requirement as well the feed/data you got. Test data gets scaled with Test data only, because Test data don't have the Target variable (one less feature in Test data). If we scale our Training data with new Test data, our model will not be able to correlate with any target variable and thus fail to learn. So the key difference is the existence of Target variable.






      share|improve this answer













      Only because I cannot comment (reputation barrier), I am writing it here as an answer. Scaling data depends upon the requirement as well the feed/data you got. Test data gets scaled with Test data only, because Test data don't have the Target variable (one less feature in Test data). If we scale our Training data with new Test data, our model will not be able to correlate with any target variable and thus fail to learn. So the key difference is the existence of Target variable.







      share|improve this answer












      share|improve this answer



      share|improve this answer










      answered Nov 21 '18 at 3:23









      Random NerdRandom Nerd

      1314




      1314













      • Thanks for your answer. I knew the basic concepts of the answer, but I have not found any references yet. Do you have any reference for the answer? Please. Also, do you have an answer to the second one? Thanks

        – Protocol313
        Nov 21 '18 at 3:37













      • Few worthy references that I could find are Article1, Article2 and Article3 (kindly google for retraining ML models for more references). For second one, kindly detail your question with more info on what you mean by positive/negative elements (that too being passed in a CNN?)

        – Random Nerd
        Nov 21 '18 at 4:27



















      • Thanks for your answer. I knew the basic concepts of the answer, but I have not found any references yet. Do you have any reference for the answer? Please. Also, do you have an answer to the second one? Thanks

        – Protocol313
        Nov 21 '18 at 3:37













      • Few worthy references that I could find are Article1, Article2 and Article3 (kindly google for retraining ML models for more references). For second one, kindly detail your question with more info on what you mean by positive/negative elements (that too being passed in a CNN?)

        – Random Nerd
        Nov 21 '18 at 4:27

















      Thanks for your answer. I knew the basic concepts of the answer, but I have not found any references yet. Do you have any reference for the answer? Please. Also, do you have an answer to the second one? Thanks

      – Protocol313
      Nov 21 '18 at 3:37







      Thanks for your answer. I knew the basic concepts of the answer, but I have not found any references yet. Do you have any reference for the answer? Please. Also, do you have an answer to the second one? Thanks

      – Protocol313
      Nov 21 '18 at 3:37















      Few worthy references that I could find are Article1, Article2 and Article3 (kindly google for retraining ML models for more references). For second one, kindly detail your question with more info on what you mean by positive/negative elements (that too being passed in a CNN?)

      – Random Nerd
      Nov 21 '18 at 4:27





      Few worthy references that I could find are Article1, Article2 and Article3 (kindly google for retraining ML models for more references). For second one, kindly detail your question with more info on what you mean by positive/negative elements (that too being passed in a CNN?)

      – Random Nerd
      Nov 21 '18 at 4:27













      0














      We usually have 3 types of datasets for getting a model trained,




      1. Training Dataset

      2. Validation Dataset

      3. Test Dataset


      Training Dataset



      This should be an evenly distributed data set which covers all varieties of data. If your train with more epochs, the model will get used to the training dataset and will only give proper proper prediction on the training dataset and this is called Overfitting. Only way to keep a check on overfitting is by having other datasets which the model has never been trained on.



      Validation Dataset



      This can be used fine tune model hyperparameters



      Test Dataset



      This is the dataset which the model has not been trained on has never been a part of deciding the hyperparameters and will give the reality of how the model is performing.






      share|improve this answer




























        0














        We usually have 3 types of datasets for getting a model trained,




        1. Training Dataset

        2. Validation Dataset

        3. Test Dataset


        Training Dataset



        This should be an evenly distributed data set which covers all varieties of data. If your train with more epochs, the model will get used to the training dataset and will only give proper proper prediction on the training dataset and this is called Overfitting. Only way to keep a check on overfitting is by having other datasets which the model has never been trained on.



        Validation Dataset



        This can be used fine tune model hyperparameters



        Test Dataset



        This is the dataset which the model has not been trained on has never been a part of deciding the hyperparameters and will give the reality of how the model is performing.






        share|improve this answer


























          0












          0








          0







          We usually have 3 types of datasets for getting a model trained,




          1. Training Dataset

          2. Validation Dataset

          3. Test Dataset


          Training Dataset



          This should be an evenly distributed data set which covers all varieties of data. If your train with more epochs, the model will get used to the training dataset and will only give proper proper prediction on the training dataset and this is called Overfitting. Only way to keep a check on overfitting is by having other datasets which the model has never been trained on.



          Validation Dataset



          This can be used fine tune model hyperparameters



          Test Dataset



          This is the dataset which the model has not been trained on has never been a part of deciding the hyperparameters and will give the reality of how the model is performing.






          share|improve this answer













          We usually have 3 types of datasets for getting a model trained,




          1. Training Dataset

          2. Validation Dataset

          3. Test Dataset


          Training Dataset



          This should be an evenly distributed data set which covers all varieties of data. If your train with more epochs, the model will get used to the training dataset and will only give proper proper prediction on the training dataset and this is called Overfitting. Only way to keep a check on overfitting is by having other datasets which the model has never been trained on.



          Validation Dataset



          This can be used fine tune model hyperparameters



          Test Dataset



          This is the dataset which the model has not been trained on has never been a part of deciding the hyperparameters and will give the reality of how the model is performing.







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 21 '18 at 3:46









          JeevanJeevan

          1326




          1326























              0














              If scaling and normalization is used, the testing set should use the same parameters used during training.
              A good answer that links to that: https://datascience.stackexchange.com/questions/27615/should-we-apply-normalization-to-test-data-as-well



              Also, some models tend to require normalization and others do not.
              The Neural Network architectures are normally robust and might not need normalization.






              share|improve this answer




























                0














                If scaling and normalization is used, the testing set should use the same parameters used during training.
                A good answer that links to that: https://datascience.stackexchange.com/questions/27615/should-we-apply-normalization-to-test-data-as-well



                Also, some models tend to require normalization and others do not.
                The Neural Network architectures are normally robust and might not need normalization.






                share|improve this answer


























                  0












                  0








                  0







                  If scaling and normalization is used, the testing set should use the same parameters used during training.
                  A good answer that links to that: https://datascience.stackexchange.com/questions/27615/should-we-apply-normalization-to-test-data-as-well



                  Also, some models tend to require normalization and others do not.
                  The Neural Network architectures are normally robust and might not need normalization.






                  share|improve this answer













                  If scaling and normalization is used, the testing set should use the same parameters used during training.
                  A good answer that links to that: https://datascience.stackexchange.com/questions/27615/should-we-apply-normalization-to-test-data-as-well



                  Also, some models tend to require normalization and others do not.
                  The Neural Network architectures are normally robust and might not need normalization.







                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Nov 21 '18 at 12:38









                  Pedro TorresPedro Torres

                  703413




                  703413






























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