def test():
"""Gaussian/Neural numeric-analytic consistency."""
images = generate_data()
regressor = Regressor(optimizer='BFGS')
_G = make_symmetry_functions(type='G2',
etas=[0.05, 5.],
elements=['Cu', 'Pt'])
_G += make_symmetry_functions(type='G4',
etas=[0.005],
zetas=[1., 4.],
gammas=[1.],
elements=['Cu', 'Pt'])
Gs = {'Cu': _G, 'Pt': _G}
calc = Amp(descriptor=Gaussian(Gs=Gs),
model=NeuralNetwork(
hiddenlayers=(2, 1),
regressor=regressor,
randomseed=42,
),
cores=1)
step = 0
for d in [None, 0.00001]:
for fortran in [True, False]:
for cores in [1, 2]:
step += 1
label = \
'numeric_analytic_test/analytic-%s-%i' % (fortran, cores) \
if d is None \
else 'numeric_analytic_test/numeric-%s-%i' \
% (fortran, cores)
print(label)
loss = LossFunction(convergence={
'energy_rmse': 10**10,
'force_rmse': 10**10
},
d=d)
calc.set_label(label)
calc.dblabel = 'numeric_analytic_test/analytic-True-1'
calc.model.lossfunction = loss
calc.descriptor.fortran = fortran
calc.model.fortran = fortran
calc.cores = cores
calc.train(images=images, )
if step == 1:
ref_energies = []
ref_forces = []
for image in images:
ref_energies += [calc.get_potential_energy(image)]
ref_forces += [calc.get_forces(image)]
ref_dloss_dparameters = \
calc.model.lossfunction.dloss_dparameters
else:
energies = []
forces = []
for image in images:
energies += [calc.get_potential_energy(image)]
forces += [calc.get_forces(image)]
dloss_dparameters = \
calc.model.lossfunction.dloss_dparameters
for image_no in range(2):
diff = abs(energies[image_no] - ref_energies[image_no])
assert (diff < 10.**(-13.)), \
'The calculated value of energy of image %i is ' \
'wrong!' % (image_no + 1)
for atom_no in range(len(images[0])):
for i in range(3):
diff = abs(forces[image_no][atom_no][i] -
ref_forces[image_no][atom_no][i])
assert (diff < 10.**(-10.)), \
'The calculated %i force of atom %i of ' \
'image %i is wrong!' \
% (i, atom_no, image_no + 1)
# Checks analytical and numerical dloss_dparameters
for _ in range(len(ref_dloss_dparameters)):
diff = abs(dloss_dparameters[_] -
ref_dloss_dparameters[_])
assert(diff < 10 ** (-10.)), \
'The calculated value of loss function ' \
'derivative is wrong!'
# Checks analytical and numerical forces
forces = []
for image in images:
image.set_calculator(calc)
forces += [calc.calculate_numerical_forces(image, d=d)]
for atom_no in range(len(images[0])):
for i in range(3):
diff = abs(forces[image_no][atom_no][i] -
ref_forces[image_no][atom_no][i])
print('{:3d} {:1d} {:7.1e}'.format(atom_no, i, diff))
assert (diff < 10.**(-6.)), \
'The calculated %i force of atom %i of ' \
'image %i is wrong! (Diff = %f)' \
% (i, atom_no, image_no + 1, diff)
def test():
images = generate_data(2)
regressor = Regressor(optimizer='BFGS')
calc = Amp(descriptor=Gaussian(),
model=NeuralNetwork(hiddenlayers=(3, 3),
regressor=regressor,),
cores=1)
step = 0
for d in [None, 0.00001]:
for fortran in [True, False]:
for cores in [1, 2]:
step += 1
label = \
'numeric_analytic_test/analytic-%s-%i' % (fortran, cores) \
if d is None \
else 'numeric_analytic_test/numeric-%s-%i' \
% (fortran, cores)
print(label)
loss = LossFunction(convergence={'energy_rmse': 10 ** 10,
'force_rmse': 10 ** 10},
d=d)
calc.set_label(label)
calc.dblabel = 'numeric_analytic_test/analytic-True-1'
calc.model.lossfunction = loss
calc.descriptor.fortran = fortran
calc.model.fortran = fortran
calc.cores = cores
calc.train(images=images,)
if step == 1:
ref_energies = []
ref_forces = []
for image in images:
ref_energies += [calc.get_potential_energy(image)]
ref_forces += [calc.get_forces(image)]
ref_dloss_dparameters = \
calc.model.lossfunction.dloss_dparameters
else:
energies = []
forces = []
for image in images:
energies += [calc.get_potential_energy(image)]
forces += [calc.get_forces(image)]
dloss_dparameters = \
calc.model.lossfunction.dloss_dparameters
for image_no in range(2):
diff = abs(energies[image_no] - ref_energies[image_no])
assert (diff < 10.**(-13.)), \
'The calculated value of energy of image %i is ' \
'wrong!' % (image_no + 1)
for atom_no in range(6):
for i in range(3):
diff = abs(forces[image_no][atom_no][i] -
ref_forces[image_no][atom_no][i])
assert (diff < 10.**(-10.)), \
'The calculated %i force of atom %i of ' \
'image %i is wrong!' \
% (i, atom_no, image_no + 1)
# Checks analytical and numerical dloss_dparameters
for _ in range(len(ref_dloss_dparameters)):
diff = abs(dloss_dparameters[_] -
ref_dloss_dparameters[_])
assert(diff < 10 ** (-10.)), \
'The calculated value of loss function ' \
'derivative is wrong!'
# Checks analytical and numerical forces
forces = []
for image in images:
image.set_calculator(calc)
forces += [calc.calculate_numerical_forces(image, d=d)]
for atom_no in range(6):
for i in range(3):
diff = abs(forces[image_no][atom_no][i] -
ref_forces[image_no][atom_no][i])
assert (diff < 10.**(-9.)), \
'The calculated %i force of atom %i of ' \
'image %i is wrong!' % (i, atom_no, image_no + 1)
def test():
images = make_training_images()
for descriptor in [None, Gaussian()]:
for global_search in [
None, SimulatedAnnealing(temperature=10, steps=5)
]:
for data_format in ['json', 'db']:
for save_memory in [
False,
]:
for fortran in [False, True]:
for cores in range(1, 4):
print(descriptor, global_search, data_format,
save_memory, fortran, cores)
pwd = os.getcwd()
testdir = 'read_write_test'
os.mkdir(testdir)
os.chdir(testdir)
regression = NeuralNetwork(hiddenlayers=(
5,
5,
))
calc = Amp(
label='calc',
descriptor=descriptor,
regression=regression,
fortran=fortran,
)
# Should start with new variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
# Test that we cannot overwrite. (Strange code
# here because we *want* it to raise an
# exception...)
try:
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
except IOError:
pass
else:
raise RuntimeError(
'Code allowed to overwrite!')
# Test that we can manually overwrite.
# Should start with existing variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
overwrite=True,
cores=cores,
)
label = 'testdir'
if not os.path.exists(label):
os.mkdir(label)
# New directory calculator.
calc = Amp(
label='testdir/calc',
descriptor=descriptor,
regression=regression,
fortran=fortran,
)
# Should start with new variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
# Open existing, save under new name.
calc = Amp(
load='calc',
label='calc2',
descriptor=descriptor,
regression=regression,
fortran=fortran,
)
# Should start with existing variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
label = 'calc_new'
if not os.path.exists(label):
os.mkdir(label)
# Change label and re-train
calc.set_label('calc_new/calc')
# Should start with existing variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
# Open existing without specifying new name.
calc = Amp(
load='calc',
descriptor=descriptor,
regression=regression,
fortran=fortran,
)
# Should start with existing variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
os.chdir(pwd)
shutil.rmtree(testdir, ignore_errors=True)
del calc, regression
