如何训练带有负和正元素作为第一层输入的卷积神经网络？

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.

Answer 1

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.

Answer 2

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.

Answer 3

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.