Right now I'm planning to make a DNN for classifying taste of crystals with their molecular structure which include information like no of atoms mass of each atoms atomic no of atoms. How should I make a data set for training testing and validation?
You can cluster all your features in one matrix X, in which each line would be one element of the data set you want to construct, and each column would be a different feature of this element.
You construct then a Y vector containing the different target classes, where the i-th element will be the target class of the i-th X element.
For the following I suppose you use python
Create train and test sets using sklearn wrapper train_test_split on X and Y just built, in which you can shuffle your X and Y (non-avoidable in this case) :
from sklearn.model_selection import train_test_split
BTW, I recommend you to rescale your data with sklearn with for example one of :
from sklearn.preprocessing import MinMaxScaler, StandardScaler
It allows better learning and so better generalization