Neural net input/output
You have to add the number of units for input and output you need for the problem. If the unknown function to approximate depends on n parameter, you will have n input units. The number of output units depends on the nature of the funcion. For real functions with n real parameters you will have one output unit.
Some problems, for example in forecasting of time series, you will have m output units for the m succesive values of the function. The encoding is important and depends on the choosen algorithm. For example, in backpropagation for feedforward nets, is better to transform, if possible, the greater number of features in discrete inputs, as for classification tasks.
Other aspect of the encoding is that you have to evaluate the number of input and hidden units in function of the amount of data. Too many units related to data may give poor approximation due the course ff dimensionality problem. In some cases, you may to aggregate some of the input data in some way to avoid that problem or use some reduction mechanism as PCA.
Can anyone explain to me how to do more complex data sets like team stats, weather, dice, complex number types 
i understand all the math and how everything works i just dont know how to input more complex data, and then how to read the data it spits out 
if someone could provide examples in python that would be a big help