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NNFlow added batchwise prediction in validation step Feb 7, 2017
.gitignore inital commit Jan 18, 2017 added batchwise prediction in validation step Feb 7, 2017 Update Feb 1, 2017

NNFlow (Work In Progress)

A simple interface for Google's TensorFlow

Basic Workflow

Converting Root nTuples into numpy arrays

NNFlow provides a function, based on root-numpy, for converting Root nTuples into numpy arrays.


from NNFlow.preprocessing import conv_root_to_np

ntuples = ['path/to/ntuple_1', 'path/to/ntuple_2', ...]
save_to = 'path/to/storage/directory'
name = 'array.npy'
my_tree = 'tree'
my_branches = ['branch_1', 'branch_2']

conv_root_to_np(save_to, name, ntuples, treename=my_tree, branches=my_branches)

If there is only one TTree in the nTuple you do not have to provide the name of the tree which should be converted. If you want to convert all branches of a tree, do not provide a branchlist.

NOTE: The array the data is saved to is of type np.structure_array (

Preprocess numpy arrays

Once you've converted the nTuples to numpy arrays, you have do some other preprocessing steps, like getting some specific branches from the array if you've converted everything in the nTuple.


from NNFlow.preprocessing import GetVariables

sig_1 = 'path/to/signal_1.npy'
sig_2 = 'path/to/signal_2.npy'

bkg_1 = 'path/to/background_1.npy'
bkg_2 = 'path/to/background_2.npy'

variables = ['var_1', 'var_2', 'var_3', ...]
weights = ['weight_1', 'weight_2', 'weight_3', ...]
category = '63'

gv = GetVariables(variables, weights, category, 'my_variables')[sig_1, sig_2, ...], [bkg_1, bkg_2, ...])

This script gets the variables defined in the variables list from the structured array and saves them into a normal 2D np.ndarray. If a variable is vector like then you should check if the branch is included in the list in line 106 in file NNFlow/preprocessing (checking for vector like data is not automated yet).

The signal (ttH) and background (ttbarSL) are combined into one array. Labels for signal (1) and background (0) are added in the first column, the event weights are added in the last column. The data is split into a split into 3 datasets. 50% of the data is used for training, 10% for validation and 40% for testing. The event weights are normed to sum 1 respectively for signal and background in each dataset. The arrays will be saved in the directory 'my_variables/category' as train.npy, val.npy and test.npy.

Train a neural network

Here is a simple example script if you want to train a neural network. Please have a look at NNFlow/binary_mlp and NNFlow/data_frame files for more information about the options.

import numpy as np
from NNFlow.binary_mlp import BinaryMLP
from NNFlow.data_frame import Dataframe

# load numpy arrays
train = np.load('my_variables/category/train.npy')
val = np.load('my_variables/category/val.npy')
test = np.load('my_variables/category/test.npy')

# use DataFrame
train = DataFrame(train)
val = DataFrame(val)
test = DataFrame(test)

save_model_to = 'my_variables/category/models/2x100'
hidden_layers = [100, 100] 

# create neural net
mlp = BinaryMLP(train.nvariables, [100, 100], save_model_to)

# train 
mlp.train(train, val, epochs=250, lr=1e-3)

# classify new events
y_test = mlp.classify(test.x)

This script will train a neural network with two hidden layers with 100 neurons. The network is saved to the directory 'save_model_to'. Also some controll plots of the training process will be saved there. To reuse a model in a different script, you can use the same script without the training step.


  • root-numpy now supports python3 -> check if NNFlow supports Python3
  • get rid of scikit-learn dependency -> write own implementation for calculating ROC-Curves and ROC-AUC score


Tested with

  • tensorflow (0.12.0rc1)
  • numpy (1.11.2)
  • matplotlib (1.5.3)
  • scikit-learn (0.18.1)
  • root-numpy (4.5.2)
  • graphviz (0.5.1)