How to choose feedforward architecture for few number of features but very large instance? Assume I have 1 million of data instance and each instance contains 100 feature. For each instance, I also have a lable.
The size of X will  be 1,000,000 X 100 and the y will be 1,000,000 X 1. The value of y is binary.
Under this case, how to pick feedforward architecture?
Such as 100-50-1 or 5000-2500-50-1, any suggestion.
Thanks.
Is your set of possible labels discrete or continuous?
If it's discrete and you have L possible different labels, then you should have L units in the output layer (then do a softmax if you want probabilities).
If it's continuous, then you should have 1 output unit.
Lastly, the number of units in the input layer is equal to the flattened dimension of one instance of the input. So if your data is 100 dimensions, then you must have 100 units in the input layer.