CODEWITHSUNDEEP
machine-learningneural-networknormalizationperceptron
PRCube
PRCube

Reputation: 586

Neural Networks normalizing output data

I have a training data for NN along with expected outputs. Each input is 10 dimensional vector and has 1 expected output.I have normalised the training data using Gaussian but I don't know how to normalise the outputs since it only has single dimension. Any ideas?

Example:

Raw Input Vector:-128.91, 71.076, -100.75,4.2475, -98.811, 77.219, 4.4096, -15.382, -6.1477, -361.18

Normalised Input Vector: -0.6049, 1.0412, -0.3731, 0.4912, -0.3571, 1.0918, 0.4925, 0.3296, 0.4056, -2.5168

The raw expected output for the above input is 1183.6 but I don't know how to normalise that. Should I normalise the expected output as part of the input vector?

Upvotes: 7

Views: 16927

Answers (2)

nochnoy.buran
nochnoy.buran

Reputation: 136

One important remark is that you normalized elements of a single input vector. Having one-dimensional output space, you could not normalize the output. The correct way is, indeed, to take a complete batch of training data, say N input (and output) vectors, and normalize each dimension (variable) individually (using N samples). Thus, for one-dimensional output, you will have N samples for normalization. In this way, the vector space of your input will not be distorted. The normalization of the output dimension is usually required when the scale-space of output variables significantly different. After training, you should use the same set normalization parameters (e.g., for zscore it is "mean" and "std") as you obtain from the training data. In this case, you will put new (unseen) data into the same scale space as you in training.

Upvotes: 0

rayryeng
rayryeng

Reputation: 104464

From the looks of your problem, you are trying to implement some sort of regression algorithm. For regression problems you don't normally normalize the outputs. For the training data you provide for a regression system, the expected output should be within the range you're expecting, or simply whatever data you have for the expected outputs.

Therefore, you can normalize the training inputs to allow the training to go faster, but you typically don't normalize the target outputs. When it comes to testing time or providing new inputs, make sure you normalize the data in the same way that you did during training. Specifically, use exactly the same parameters for normalization during training for any test inputs into the network.

Upvotes: 15

Related Questions