Identifying importance of each feature in deep model I have a deep model and I want to figure out which feature has the maximum influence on predicted result. For this I train the model with all the features I think are important, during prediction I set all features =0 one by one keeping the rest unchanged so I could figure out which is the least important of all. On predicting the results of these tampered test set on the trained model I get the same(changes at 5th-6th decimal place) F1 score, Recall and Precision.

Can some one explain me where I am wrong?
This could mean that your features are heavily correlated. As a result, when you set one variable to 0, there is always another one that carries the same information. 
Try to set several features to 0 to see if you have the same issue. If yes, then it might be something else (maybe your code).

You might also be interested in this paper, you'll find several methods to assess feature importance.