Questions tagged [normalisation]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
0
votes
1answer
24 views

Normalization of possibly not fully representative data

I am trying to train a classification RNN model on a sequence of table medical data, but I stuck with the normalization problem. I realized that I cannot simply use MinMaxScaler, because of 3 problems:...
1
vote
1answer
43 views

How to scale all positive continuous reward?

My RL project has all positive continuous rewards for every step and the goal is to have the maximum cumulative reward (episodic reward). The problem is that the rewards are too close and all between ...
1
vote
0answers
26 views

Why do you calculate the mean and standard deviation over the complete dataset before training rather than for every batch?

In most implementations of neural networks the features are scaled to make the optimization of the loss function as stable as possible. Mostly a min-max scaler is used. Alternatively, there is also a ...
1
vote
0answers
27 views

For image preprocessing, is it better to use normalization or standartization?

For a neural network model that classifies images, is it better to use normalization (dividing by 255.0) or using standardization (subtract mean and divide by STD)? When I started learning ...
1
vote
1answer
31 views

How to define a “don't care” class in time series classification in Pytorch?

This is a theoretical question. Setup I have a time series classification task in which I should output a classification of 3 classes for every time stamp t. All ...
1
vote
1answer
47 views

How to normalize images before training?

I have seen people normalize images by just dividing 255. But why? Why not use mean normalization or Z-score Normalization? I also came across this StackOverflow topic while searching but the answers ...
2
votes
1answer
40 views

Is data leakage relevant when scaling across samples?

I have a question about data leakage when pre-processing data for a neural network and whether data leakage actually applies in my instance. I have variance stabilising transformed genomic data. ...
2
votes
0answers
26 views

Why does batch norm standardize with sample mean/variance, when it also learns parameters to scale the mean/variance?

Batch norm is a normalizing layer that is shown to help deep networks learn faster and with higher generalization accuracy. It normalizes the activations of the previous layer to a mean $\beta$ and ...
0
votes
0answers
28 views

What's the best way to normalize 2D input data?

Currently I'm working on a project that the input to the network is SWT of a signal which is 2D array. Currently I'm using batch normalization to normalize inputs(Stationary Wavelet Transform of ...
0
votes
1answer
34 views

Flatten image using Neural network and matrix transpose

I have read a lecture note of Prof. Andrew Ng. There was something about data normalization like how can we flatten an image of (64x64x3) into a (64x64x3)*x1 vector. After that there is pictorial ...
0
votes
1answer
41 views

What would be a typical pre-processing and data normalization pipeline for time series data (for non-linear models such as neural networks)?

I've started to work on time series. I was wondering what would be the best data normalizing and pre-processing technique for non-linear models, specifically, neural networks. One I can think of is ...
0
votes
1answer
63 views

Is it necessary to standardise the expected output

Normalisation transform data into a range: $$X_i = \dfrac{X_i - Min}{Max-Min}$$ Practically, I found out that the model doesn't generalise well when using normalisation of input data, instead of ...
1
vote
1answer
381 views

Could the normalisation of the inputs make the neural network insensitive to changes in the inputs?

When using neural networks (NNs), we often normalized the inputs. I think this is done to equally capture the changes in any input feature, that is, if any feature takes huge values and other features ...