I want to know how a dataset is fed to a neural network. Suppose I have a weather dataset
If the data of the dataset are following
outlook Temperature Humidity windy play
Sunny hot high false no
Sunny hot high True no
overcast hot high false yes
rainy mild high false yes
rainy cool normal false yes
I would like to know what values should replace the ? in the picture? I am confused between two inputs
1st input
2nd input
I think the input should be like the 1st input picture. For me 2nd input picture has no sense. However, somewhere on web, I read that the input array of each neuron should be row-wise input.
Any suggestion would be helpful.
CodePudding user response:
What "row wise" means is that your data
outlook Temperature Humidity windy play
Sunny hot high false no
Sunny hot high True no
overcast hot high false yes
rainy mild high false yes
rainy cool normal false yes
should be fed row by row
so, you would get on first row
outlook=Sunny, Temperature=hot, Humidity=high, windy=false, play=no
each of these key=value assignments is a value for one "neuron" (note, that in reality neurons are not really able to process a string, so you will need some form of encoding, e.g. one hot encoding, which will change the effective number of neurons)
and so on and so forth.
You don't really "feed dataset in", in practise we do often use batching, where many rows are processed in parallel, but this is just an implementational efficiency trick for heavily optimised vectorised computation, but for pure understanding you should just ignore its existence, as it is an independent thing.