How do I train the Convolutional Neural Network with negative and positive elements as the input of the first...
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
asked Nov 21 at 3:00
Protocol313
44
add a comment |
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
asked Nov 21 at 3:00
Protocol313
44
you scale both train and test data
– Mitch Wheat
Nov 21 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 at 3:12
add a comment |
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
asked Nov 21 at 3:00
Protocol313
44
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
asked Nov 21 at 3:00
Protocol313
44
asked Nov 21 at 3:00
Protocol313
44
edited Nov 21 at 3:15
asked Nov 21 at 3:00
Protocol313
44
asked Nov 21 at 3:00
Protocol313
44
asked Nov 21 at 3:00
Protocol313
44
44
you scale both train and test data
– Mitch Wheat
Nov 21 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 at 3:12
add a comment |
you scale both train and test data
– Mitch Wheat
Nov 21 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 at 3:12
you scale both train and test data
– Mitch Wheat
Nov 21 at 3:06
you scale both train and test data
– Mitch Wheat
Nov 21 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 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 at 3:12
add a comment |
3 Answers
3
active
oldest
votes
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.
answered Nov 21 at 3:23
Random Nerd
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 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 at 4:27
add a comment |
We usually have 3 types of datasets for getting a model trained,
- Training Dataset
- Validation Dataset
- 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.
answered Nov 21 at 3:46
Jeevan
1326
add a comment |
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.
answered Nov 21 at 12:38
Pedro Torres
683413
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
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.
answered Nov 21 at 3:23
Random Nerd
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 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 at 4:27
add a comment |
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.
answered Nov 21 at 3:23
Random Nerd
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 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 at 4:27
add a comment |
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.
answered Nov 21 at 3:23
Random Nerd
1314
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.
answered Nov 21 at 3:23
Random Nerd
1314
answered Nov 21 at 3:23
Random Nerd
1314
answered Nov 21 at 3:23
Random Nerd
1314
answered Nov 21 at 3:23
Random 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 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 at 4:27
add a comment |
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 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 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 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 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 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 at 4:27
add a comment |
We usually have 3 types of datasets for getting a model trained,
- Training Dataset
- Validation Dataset
- 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.
answered Nov 21 at 3:46
Jeevan
1326
add a comment |
We usually have 3 types of datasets for getting a model trained,
- Training Dataset
- Validation Dataset
- 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.
answered Nov 21 at 3:46
Jeevan
1326
add a comment |
We usually have 3 types of datasets for getting a model trained,
- Training Dataset
- Validation Dataset
- 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.
answered Nov 21 at 3:46
Jeevan
1326
We usually have 3 types of datasets for getting a model trained,
- Training Dataset
- Validation Dataset
- 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.
answered Nov 21 at 3:46
Jeevan
1326
answered Nov 21 at 3:46
Jeevan
1326
answered Nov 21 at 3:46
Jeevan
1326
answered Nov 21 at 3:46
Jeevan
1326
1326
add a comment |
add a comment |
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.
answered Nov 21 at 12:38
Pedro Torres
683413
add a comment |
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.
answered Nov 21 at 12:38
Pedro Torres
683413
add a comment |
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.
answered Nov 21 at 12:38
Pedro Torres
683413
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.
answered Nov 21 at 12:38
Pedro Torres
683413
answered Nov 21 at 12:38
Pedro Torres
683413
answered Nov 21 at 12:38
Pedro Torres
683413
answered Nov 21 at 12:38
Pedro Torres
683413
683413
add a comment |
add a comment |
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you scale both train and test data
– Mitch Wheat
Nov 21 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 at 3:12