def forward_symbolic(self, var, inputs): x = inputs layer1_weights, layer2_weights, layer3_weights = var layer1 = [] for i in range(self.linear1.weight.size(0)): ex = 0 for j in range(self.linear1.weight.size(1)): ex += inputs[j] * layer1_weights[i][j] layer1.append(dreal.tanh(ex)) layer2 = [] for i in range(self.linear2.weight.size(0)): ex = 0 for j in range(self.linear2.weight.size(1)): ex += layer1[j] * layer2_weights[i][j] layer2.append(dreal.tanh(ex)) layer3 = [] for i in range(self.linear3.weight.size(0)): ex = 0 for j in range(self.linear3.weight.size(1)): ex += layer2[j] * layer3_weights[i][j] layer3.append(ex) softmax = [ dreal.exp(layer3[0]) / (dreal.exp(layer3[0]) + dreal.exp(layer3[1])), dreal.exp(layer3[1]) / (dreal.exp(layer3[0]) + dreal.exp(layer3[1])) ] return softmax
def hyper_tan_dr(x): y = x.copy() # original_shape = y.shape # y = y.reshape(max(y.shape[0], y.shape[1]), 1) for idx in range(len(y)): y[idx, 0] = dr.tanh(y[idx, 0]) return y # .reshape(original_shape)
def forward_symbolic(self, var, inputs): x = inputs layer1_weights, layer2_weights = var layer1 = [] for i in range(self.linear1.weight.size(0)): #in the case where wer have bias. Currently disabled #ex = self.linear1.bias.data[i].item() ex = 0 for j in range(self.linear1.weight.size(1)): ex += inputs[j] * layer1_weights[i][j] layer1.append(dreal.tanh(ex)) layer2 = [] for i in range(self.linear2.weight.size(0)): #ex = self.linear2.bias.data[i].item() ex = 0 for j in range(self.linear2.weight.size(1)): ex += layer1[j] * layer2_weights[i][j] layer2.append(ex) softmax = [ dreal.exp(layer2[0]) / (dreal.exp(layer2[0]) + dreal.exp(layer2[1])), dreal.exp(layer2[1]) / (dreal.exp(layer2[0]) + dreal.exp(layer2[1])) ] return softmax
def test_functions_with_expression(self): self.assertEqual(str(abs(e_x)), "abs(x)") self.assertEqual(str(exp(e_x)), "exp(x)") self.assertEqual(str(sqrt(e_x)), "sqrt(x)") self.assertEqual(str(pow(e_x, e_y)), "pow(x, y)") self.assertEqual(str(sin(e_x)), "sin(x)") self.assertEqual(str(cos(e_x)), "cos(x)") self.assertEqual(str(tan(e_x)), "tan(x)") self.assertEqual(str(asin(e_x)), "asin(x)") self.assertEqual(str(acos(e_x)), "acos(x)") self.assertEqual(str(atan(e_x)), "atan(x)") self.assertEqual(str(atan2(e_x, e_y)), "atan2(x, y)") self.assertEqual(str(sinh(e_x)), "sinh(x)") self.assertEqual(str(cosh(e_x)), "cosh(x)") self.assertEqual(str(tanh(e_x)), "tanh(x)") self.assertEqual(str(min(e_x, e_y)), "min(x, y)") self.assertEqual(str(max(e_x, e_y)), "max(x, y)") self.assertEqual(str(if_then_else(e_x > e_y, e_x, e_y)), "(if (x > y) then x else y)")
def test_functions_with_variable(self): self.assertEqual(str(abs(x)), "abs(x)") self.assertEqual(str(exp(x)), "exp(x)") self.assertEqual(str(sqrt(x)), "sqrt(x)") self.assertEqual(str(pow(x, y)), "pow(x, y)") self.assertEqual(str(sin(x)), "sin(x)") self.assertEqual(str(cos(x)), "cos(x)") self.assertEqual(str(tan(x)), "tan(x)") self.assertEqual(str(asin(x)), "asin(x)") self.assertEqual(str(acos(x)), "acos(x)") self.assertEqual(str(atan(x)), "atan(x)") self.assertEqual(str(atan2(x, y)), "atan2(x, y)") self.assertEqual(str(sinh(x)), "sinh(x)") self.assertEqual(str(cosh(x)), "cosh(x)") self.assertEqual(str(tanh(x)), "tanh(x)") self.assertEqual(str(min(x, y)), "min(x, y)") self.assertEqual(str(max(x, y)), "max(x, y)") self.assertEqual(str(if_then_else(x > y, x, y)), "(if (x > y) then x else y)")
def test_functions_with_float(self): v_x = 1.0 v_y = 1.0 self.assertEqual(abs(v_x), math.fabs(v_x)) self.assertEqual(exp(v_x), math.exp(v_x)) self.assertEqual(sqrt(v_x), math.sqrt(v_x)) self.assertEqual(pow(v_x, v_y), v_x**v_y) self.assertEqual(sin(v_x), math.sin(v_x)) self.assertEqual(cos(v_x), math.cos(v_x)) self.assertEqual(tan(v_x), math.tan(v_x)) self.assertEqual(asin(v_x), math.asin(v_x)) self.assertEqual(acos(v_x), math.acos(v_x)) self.assertEqual(atan(v_x), math.atan(v_x)) self.assertEqual(atan2(v_x, v_y), math.atan2(v_x, v_y)) self.assertEqual(sinh(v_x), math.sinh(v_x)) self.assertEqual(cosh(v_x), math.cosh(v_x)) self.assertEqual(tanh(v_x), math.tanh(v_x)) self.assertEqual(min(v_x, v_y), min(v_x, v_y)) self.assertEqual(max(v_x, v_y), max(v_x, v_y)) self.assertEqual( if_then_else(Expression(v_x) > Expression(v_y), v_x, v_y), v_x if v_x > v_y else v_y)
w2 = model.layer2.weight.data.numpy() b1 = model.layer1.bias.data.numpy() b2 = model.layer2.bias.data.numpy() # Falsification if i % 10 == 0: f = [x2, -(system.p.g * dreal.sin(x1) - 2 * system.p.L0 * system.p.delta * x2 ** 3) / (system.length(vars_) + 2 * system.p.L0 * system.p.delta * x1 * x2)] # Candidate V z1 = np.dot(vars_, w1.T) + b1 a1 = [] for j in range(0, len(z1)): a1.append(dreal.tanh(z1[j])) z2 = np.dot(a1, w2.T)+b2 V_learn = dreal.tanh(z2.item(0)) print('===========Verifying==========') start_ = timeit.default_timer() result= CheckLyapunov(vars_, f, V_learn, ball_lb, ball_ub, config, epsilon) stop_ = timeit.default_timer() if result: print("Not a Lyapunov function. Found counterexample: ") print(result) x = LNF.AddCounterexamples(x, result, 10) else: valid = True print("Satisfy conditions!!")
b2 = model.layer2.bias.data.numpy() # Falsification if i % 10 == 0: f = [ x2, -(system.p.g * dreal.sin(x1) + 2 * system.p.L0 * system.p.delta * x2**3) / (system.length(vars_) + 2 * system.p.L0 * system.p.delta * x1 * x2) ] # Candidate V z1 = np.dot(vars_, w1.T) + b1 a1 = [] for j in range(0, len(z1)): a1.append(dreal.tanh(z1[j])) z2 = np.dot(a1, w2.T) + b2 V_learn = (z2.item(0))**2 print('===========Verifying==========') start_ = timeit.default_timer() result = CheckLyapunov(vars_, f, V_learn, ball_lb, ball_ub, config, epsilon) stop_ = timeit.default_timer() if result: print("Not a Lyapunov function. Found counterexample: ") print(result) x = LNF.AddCounterexamples(x, result, 10) else: valid = True
w5 = model.layer5.weight.data.numpy() b1 = model.layer1.bias.data.numpy() b2 = model.layer2.bias.data.numpy() b3 = model.layer3.bias.data.numpy() b4 = model.layer4.bias.data.numpy() b5 = model.layer5.bias.data.numpy() # Falsification if i % 10 == 0: f = [x2, -(system.p.g * dreal.sin(x1) + 2 * system.p.L0 * system.p.delta * x2 ** 3) / (system.length(vars_) + 2 * system.p.L0 * system.p.delta * x1 * x2)] z1 = np.dot(vars_, w1.T) + b1 a1 = [dreal.tanh(z) for z in z1] z2 = np.dot(a1, w2.T) + b2 a2 = [dreal.tanh(z) for z in z2] z3 = np.dot(a2, w3.T) + b3 a3 = [dreal.tanh(z) for z in z3] z4 = np.dot(a3, w4.T) + b4 a4 = [dreal.tanh(z) for z in z4] z5 = np.dot(a4, w5.T) + b5 V_learn = dreal.tanh(z5.item(0)) print('===========Verifying==========') start_ = timeit.default_timer() result = CheckLyapunov(vars_, f, V_learn, ball_lb, ball_ub, config, epsilon) stop_ = timeit.default_timer() if result:
b1 = model.layer1.bias.data.numpy() b2 = model.layer2.bias.data.numpy() b3 = model.layer3.bias.data.numpy() # Falsification if i % 10 == 0: f = [ x2, -(system.p.g * dreal.sin(x1) + 2 * system.p.L0 * system.p.delta * x2**3) / (system.length(vars_) + 2 * system.p.L0 * system.p.delta * x1 * x2) ] # Candidate V z1 = np.dot(vars_, w1.T) + b1 a1 = [dreal.tanh(z) for z in z1] z2 = np.dot(a1, w2.T) + b2 a2 = [dreal.tanh(z) for z in z2] z3 = np.dot(a2, w3.T) + b3 V_learn = z4.item(0)**2 print('===========Verifying==========') start_ = timeit.default_timer() result = CheckLyapunov(vars_, f, V_learn, ball_lb, ball_ub, config, epsilon) stop_ = timeit.default_timer() if result: print("Not a Lyapunov function. Found counterexample: ") print(result) x = LNF.AddCounterexamples(x, result, 10)
def _sympy_converter(var_map, exp, target, expand_pow=False): rv = None assert isinstance(exp, sp.Expr) and target is not None if isinstance(exp, sp.Symbol): rv = var_map.get(exp.name, None) elif isinstance(exp, sp.Number): try: rv = RealVal(exp) if isinstance(target, Z3Verifier) else sp.RealNumber(exp) except: # Z3 parser error rep = sp.Float(exp, len(str(exp))) rv = RealVal(rep) elif isinstance(exp, sp.Add): # Add(exp_0, ...) rv = _sympy_converter(var_map, exp.args[0], target, expand_pow=expand_pow) # eval this expression for e in exp.args[1:]: # add it to all other remaining expressions rv += _sympy_converter(var_map, e, target, expand_pow=expand_pow) elif isinstance(exp, sp.Mul): rv = _sympy_converter(var_map, exp.args[0], target, expand_pow=expand_pow) for e in exp.args[1:]: rv *= _sympy_converter(var_map, e, target, expand_pow=expand_pow) elif isinstance(exp, sp.Pow): x = _sympy_converter(var_map, exp.args[0], target, expand_pow=expand_pow) e = _sympy_converter(var_map, exp.args[1], target, expand_pow=expand_pow) if expand_pow: try: i = float(e.sexpr()) assert i.is_integer() i = int(i) - 1 rv = x for _ in range(i): rv *= x except: # fallback rv = _sympy_converter(var_map, exp, target, expand_pow=False) else: rv = x ** e elif isinstance(exp, sp.Max): x = _sympy_converter(var_map, exp.args[1], target, expand_pow=expand_pow) zero = exp.args[0] if target == Z3Verifier: rv = z3.If(x >= 0.0, x, 0.0) else: rv = dr.max(x, 0.0) elif isinstance(exp, sp.Heaviside): x = _sympy_converter(var_map, exp.args[0], target, expand_pow=False) if target == Z3Verifier: rv = z3.If(x > 0.0, 1.0, 0.0) else: rv = dr.if_then_else(x>0.0, 1.0, 0.0) elif isinstance(exp, sp.Function): # check various activation types ONLY FOR DREAL if isinstance(exp, sp.tanh): rv = dr.tanh(_sympy_converter(var_map, exp.args[0], target, expand_pow=expand_pow)) elif isinstance(exp, sp.sin): rv = dr.sin(_sympy_converter(var_map, exp.args[0], target, expand_pow=expand_pow)) elif isinstance(exp, sp.cos): rv = dr.cos(_sympy_converter(var_map, exp.args[0], target, expand_pow=expand_pow)) elif isinstance(exp, sp.exp): rv = dr.exp(_sympy_converter(var_map, exp.args[0], target, expand_pow=expand_pow)) else: ValueError('Term ' + str(exp) + ' not recognised') assert rv is not None return rv
b1 = model.layer1.bias.data.numpy() b2 = model.layer2.bias.data.numpy() b3 = model.layer3.bias.data.numpy() # Falsification if i % 10 == 0: f = [ x2, -(system.p.g * dreal.sin(x1) + 2 * system.p.L0 * system.p.delta * x2**3) / (system.length(vars_) + 2 * system.p.L0 * system.p.delta * x1 * x2) ] # Candidate V z1 = np.dot(vars_, w1.T) + b1 a1 = [dreal.tanh(z) for z in z1] z2 = np.dot(a1, w2.T) + b2 a2 = [dreal.tanh(z) for z in z2] z3 = np.dot(a2, w3.T) + b3 V_learn = dreal.tanh(z3.item(0)) print('===========Verifying==========') start_ = timeit.default_timer() result = CheckLyapunov(vars_, f, V_learn, ball_lb, ball_ub, config, epsilon) stop_ = timeit.default_timer() if result: print("Not a Lyapunov function. Found counterexample: ") print(result) x = LNF.AddCounterexamples(x, result, 10)
b1 = model.layer1.bias.data.numpy() b2 = model.layer2.bias.data.numpy() b3 = model.layer3.bias.data.numpy() b4 = model.layer4.bias.data.numpy() # Falsification if i % 10 == 0: f = [ x2, -(system.p.g * dreal.sin(x1) + 2 * system.p.L0 * system.p.delta * x2**3) / (system.length(vars_) + 2 * system.p.L0 * system.p.delta * x1 * x2) ] z1 = np.dot(vars_, w1.T) + b1 a1 = [dreal.tanh(z) for z in z1] z2 = np.dot(a1, w2.T) + b2 a2 = [dreal.tanh(z) for z in z2] z3 = np.dot(a2, w3.T) + b3 a3 = [dreal.tanh(z) for z in z3] z4 = np.dot(a3, w4.T) + b4 V_learn = dreal.tanh(z4.item(0)) print('===========Verifying==========') start_ = timeit.default_timer() result = CheckLyapunov(vars_, f, V_learn, ball_lb, ball_ub, config, epsilon) stop_ = timeit.default_timer() if result: print("Not a Lyapunov function. Found counterexample: ")