MarabouNetworkONNX¶

Top contributors (to current version):

Kyle Julian

This file is part of the Marabou project. Copyright (c) 2017-2019 by the authors listed in the file AUTHORS in the top-level source directory) and their institutional affiliations. All rights reserved. See the file COPYING in the top-level source directory for licensing information.

MarabouNetworkONNX represents neural networks with piecewise linear constraints derived from the ONNX format

class maraboupy.MarabouNetworkONNX.MarabouNetworkONNX(filename, inputNames=None, outputName=None)[source]¶

Bases: maraboupy.MarabouNetwork.MarabouNetwork

Constructs a MarabouNetworkONNX object from an ONNX file

Parameters

filename (str) – Path to the ONNX file
inputNames – (list of str, optional): List of node names corresponding to inputs
outputName – (string, optional): Name of node corresponding to output

Returns

marabouNetworkONNX

addAbsConstraint(b, f)[source]¶

Function to add a new Abs constraint

Parameters

b (int) – Variable representing input of the Abs constraint
f (int) – Variable representing output of the Abs constraint

addDisjunctionConstraint(disjuncts)[source]¶

Function to add a new Disjunction constraint

Parameters: disjuncts (list of list of Equations) – Each inner list represents a disjunct

addEquality(vars, coeffs, scalar)[source]¶

Function to add equality constraint to network

\[\sum_i vars_i * coeffs_i = scalar\]

Parameters

vars (list of int) – Variable numbers
coeffs (list of float) – Coefficients
scalar (float) – Right hand side constant of equation

addEquation(x)[source]¶

Function to add new equation to the network

Parameters: x (Equation) – New equation to add

addInequality(vars, coeffs, scalar)[source]¶

Function to add inequality constraint to network

\[\sum_i vars_i * coeffs_i \le scalar\]

Parameters

vars (list of int) – Variable numbers
coeffs (list of float) – Coefficients
scalar (float) – Right hand side constant of inequality

addMaxConstraint(elements, v)[source]¶

Function to add a new Max constraint

Parameters

elements (set of int) – Variable representing input to max constraint
v (int) – Variable representing output of max constraint

addRelu(v1, v2)[source]¶

Function to add a new Relu constraint

Parameters

v1 (int) – Variable representing input of Relu
v2 (int) – Variable representing output of Relu

addSignConstraint(b, f)[source]¶

Function to add a new Sign constraint

Parameters

b (int) – Variable representing input of Sign
f (int) – Variable representing output of Sign

clear()[source]¶: Reset values to represent empty network

evaluate(inputValues, useMarabou=True, options=None, filename='evaluateWithMarabou.log')[source]¶

Function to evaluate network at a given point

Parameters

inputValues (list of np arrays) – Inputs to evaluate
useMarabou (bool) – Whether to use Marabou solver or TF/ONNX, defaults to True
options (Options) – Object for specifying Marabou options, defaults to None
filename (str) – Path to redirect output if using Marabou solver, defaults to “evaluateWithMarabou.log”

Returns

Values representing the output of the network or None if output cannot be computed

Return type

(np array)

evaluateWithMarabou(inputValues, filename='evaluateWithMarabou.log', options=None)[source]¶

Function to evaluate network at a given point using Marabou as solver

Parameters

inputValues (list of np arrays) – Inputs to evaluate
filename (str) – Path to redirect output if using Marabou solver, defaults to “evaluateWithMarabou.log”
options (Options) – Object for specifying Marabou options, defaults to None

Returns

Values representing the output of the network or None if system is UNSAT

Return type

(np array)

evaluateWithoutMarabou(inputValues)[source]¶

Try to evaluate the network with the given inputs using ONNX

Parameters: inputValues (list of numpy array) – Input values representing inputs to network
Returns: Output values of neural network
Return type: (numpy array)

findError(inputValues, options=None, filename='evaluateWithMarabou.log')[source]¶

Function to find error between Marabou solver and TF/Nnet at a given point

Parameters

inputValues (list of np arrays) – Input values to evaluate
options (Options) –
filename (str) – Path to redirect output if using Marabou solver, defaults to “evaluateWithMarabou.log”

Returns

Values representing the error in each output variable

Return type

(np array)

getMarabouQuery()[source]¶

Function to convert network into Marabou InputQuery

Returns: InputQuery

lowerBoundExists(x)[source]¶

Function to check whether lower bound for a variable is known

Parameters: x (int) – Variable to check

saveQuery(filename='')[source]¶

Serializes the inputQuery in the given filename

Parameters: filename – (string) file to write serialized inputQuery

setLowerBound(x, v)[source]¶

Function to set lower bound for variable

Parameters

x (int) – Variable number to set
v (float) – Value representing lower bound

setUpperBound(x, v)[source]¶

Function to set upper bound for variable

Parameters

x (int) – Variable number to set
v (float) – Value representing upper bound

solve(filename='', verbose=True, options=None)[source]¶

Function to solve query represented by this network

Parameters

filename (string) – Path for redirecting output
verbose (bool) – If true, print out solution after solve finishes
options (Options) – Object for specifying Marabou options, defaults to None

Returns

tuple containing:

vals (Dict[int, float]): Empty dictionary if UNSAT, otherwise a dictionary of SATisfying values for variables
stats (Statistics): A Statistics object to how Marabou performed

Return type

(tuple)

upperBoundExists(x)[source]¶

Function to check whether upper bound for a variable is known

Parameters: x (int) – Variable to check