My input data is a set of numbers and an output number is predicted for each input number. input numbers are independent of the other and the output number is predicted based on how the input number digits are positioned.
This is a one-by-one mapping.
How do I do this?
Do I need neural networks or is there a simpler way?
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I'm working on a neural network with Keras using TensorFlow as the backend right now, and my model takes 5 inputs, all normalized to 0 to 1. The inputs' units vary from m/s to meters to m/s/s. So, for example, one input could vary from 0 m/s to 30 m/s, while another input could vary from 5 m to 200 m in the training dataset.
Is it better to individually and independently normalize all inputs so that I have different scales for each unit/input? Or would normalizing all inputs to one scale (mapping 0-200 to 0-1 for the example above) be better for accuracy?
Normalize individualy each input. Because if you normalize everything by dividing 200 some inputs will affect your network less than others. If one input vary between 0-30, after dividing by 200 you get 0-0.15 scale and scale for input which vary 0-200 will be 0-1 after division. So 0-30 input will have less numbers and you tell your network that input is not so relevant as one whith 0-200.
I’m trying to use a Neural Network for purposes of binary classification. It consist of three layers. The first layer has three input neurons, the hidden layer has two neurons, and the output layer has three neurons that output a binary value of 1 or 0. Actually the output is usually a floating point number, but it typically rounds up to a whole number.
If the network only outputs vectors of 3, then shouldn't my input vectors be the same size? Otherwise, for classification, how else do you map the output to the input?
I wrote the neural network in Excel using VBA based on the following article: https://www.analyticsvidhya.com/blog/2017/05/neural-network-from-scratch-in-python-and-r/
So far it works exactly as described in the article. I don’t have access to a machine learning library at the moment so I’ve chosen to give this a try.
For example:
If the output of the network is [n, n ,n], does that mean that my input data has to be [n, n, n] also?
From what I read in here: Neural net input/output
It seems that's the way it should be. I'm not entirely sure though.
To speak simple,
for regression task, your output usually has the dimension [1] (if you predict single value).
For the classification task, your output should have the same number of dimensions equal to the number of classes you have (outputs are probabilities, the sum of them = 1).
So, there is no need to have equal dimensions of input and output. NN is just a projection of one dimension to another.
For example,
regression, we predict house prices: input is [1, 10] (to features of the property), the output is [1] - price
classification, we predict class (will be sold or not): input is [1, 11] (same features + listed price), output is [1, 2] (probability of class 0 (will be not sold) and 1 (will be sold); for example, [1; 0], [0; 1] or [0.5; 0.5] and so on; it is binary classification)
Additionally, equality of input-output dimensions exists in more specific tasks, for example, autoencoder models (when you need to present your data in other dimension and then represent it back, to the original dimension).
Again, the output dimension is the size of outputs for 1 batch. Only one, not of the whole dataset.
I have several thousands samples with equal number of features (5000, they are time dependent) and I would like to predict of vectors with variable length.
I'm beginner in RNN, and I'd like to know if there are the approaches except zero padding, when we have vector with variable length.
I want to train the GMM with the help of MFCC.
I have 588 audio files (wav, if it's important). After extracting the features, I get a set of 588 two-dimensional arrays (13x?). Each file has a different number of columns.
And how to submit MFCCs to the input algorithm?
You can compute the length of columns. Make the longest one as reference.
Then traverse the mfccs.
If the number of columns is smaller the the reference,Pad the mfccs with zeros.
After doing that, the mfccs are in same shape. Then the mfccs can be fed to the model.
I calculate the fft for a given soundfile and get an array of the shape e.g. (100,257) with 100 rows and 257 frequency bins. I want to use this as an input vector for a neural network but before I want to normalize with librosa lib
https://librosa.github.io/librosa/generated/librosa.util.normalize.html#librosa.util.normalize
so should I normalize over axis=0 or axis=1? axis=0 normalizes the columns aggregated over the rows and axis=1 normalizes every row or should I normalize over every value independent of rows and columns?
The way how you normalize the fft depends on your application and the final performance. There isn't a general normalization scheme.
In one of my application, I didn't normalize and input the raw fft to the neural network. One common way to normalize is taking the logarithm. This operation can reduce the dynamic range.