def __init__(self, jdata):
args = ClassArg()\
.add('a', dict, must = True) \
.add('r', dict, must = True)
class_data = args.parse(jdata)
self.param_a = class_data['a']
self.param_r = class_data['r']
self.descrpt_a = DescrptSeA(self.param_a)
self.descrpt_r = DescrptSeR(self.param_r)
assert (self.descrpt_a.get_ntypes() == self.descrpt_r.get_ntypes())
def test (self) :
jdata = j_loader(input_json)
jdata = jdata['model']
descrpt = DescrptSeA(jdata['descriptor'])
fitting = EnerFitting(jdata['fitting_net'], descrpt)
avgs = [0, 10]
stds = [2, 0.4]
sys_natoms = [10, 100]
sys_nframes = [5, 2]
all_data = _make_fake_data(sys_natoms, sys_nframes, avgs, stds)
frefa, frefs = _brute_fparam(all_data, len(avgs))
arefa, arefs = _brute_aparam(all_data, len(avgs))
fitting.compute_input_stats(all_data, protection = 1e-2)
# print(frefa, frefs)
for ii in range(len(avgs)):
self.assertAlmostEqual(frefa[ii], fitting.fparam_avg[ii])
self.assertAlmostEqual(frefs[ii], fitting.fparam_std[ii])
self.assertAlmostEqual(arefa[ii], fitting.aparam_avg[ii])
self.assertAlmostEqual(arefs[ii], fitting.aparam_std[ii])
def test_model(self):
jfile = 'water_se_a.json'
with open(jfile) as fp:
jdata = json.load(fp)
run_opt = RunOptions(None)
systems = j_must_have(jdata, 'systems')
set_pfx = j_must_have(jdata, 'set_prefix')
batch_size = j_must_have(jdata, 'batch_size')
test_size = j_must_have(jdata, 'numb_test')
batch_size = 1
test_size = 1
stop_batch = j_must_have(jdata, 'stop_batch')
rcut = j_must_have(jdata['model']['descriptor'], 'rcut')
data = DataSystem(systems,
set_pfx,
batch_size,
test_size,
rcut,
run_opt=None)
test_data = data.get_test()
numb_test = 1
descrpt = DescrptSeA(jdata['model']['descriptor'])
fitting = EnerFitting(jdata['model']['fitting_net'], descrpt)
model = Model(jdata['model'], descrpt, fitting)
# model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']])
input_data = {
'coord': [test_data['coord']],
'box': [test_data['box']],
'type': [test_data['type']],
'natoms_vec': [test_data['natoms_vec']],
'default_mesh': [test_data['default_mesh']]
}
model._compute_dstats(input_data)
model.bias_atom_e = data.compute_energy_shift()
t_prop_c = tf.placeholder(tf.float32, [5], name='t_prop_c')
t_energy = tf.placeholder(global_ener_float_precision, [None],
name='t_energy')
t_force = tf.placeholder(global_tf_float_precision, [None],
name='t_force')
t_virial = tf.placeholder(global_tf_float_precision, [None],
name='t_virial')
t_atom_ener = tf.placeholder(global_tf_float_precision, [None],
name='t_atom_ener')
t_coord = tf.placeholder(global_tf_float_precision, [None],
name='i_coord')
t_type = tf.placeholder(tf.int32, [None], name='i_type')
t_natoms = tf.placeholder(tf.int32, [model.ntypes + 2],
name='i_natoms')
t_box = tf.placeholder(global_tf_float_precision, [None, 9],
name='i_box')
t_mesh = tf.placeholder(tf.int32, [None], name='i_mesh')
is_training = tf.placeholder(tf.bool)
t_fparam = None
model_pred \
= model.build (t_coord,
t_type,
t_natoms,
t_box,
t_mesh,
t_fparam,
suffix = "se_a",
reuse = False)
energy = model_pred['energy']
force = model_pred['force']
virial = model_pred['virial']
atom_ener = model_pred['atom_ener']
feed_dict_test = {
t_prop_c: test_data['prop_c'],
t_energy: test_data['energy'][:numb_test],
t_force: np.reshape(test_data['force'][:numb_test, :], [-1]),
t_virial: np.reshape(test_data['virial'][:numb_test, :], [-1]),
t_atom_ener: np.reshape(test_data['atom_ener'][:numb_test, :],
[-1]),
t_coord: np.reshape(test_data['coord'][:numb_test, :], [-1]),
t_box: test_data['box'][:numb_test, :],
t_type: np.reshape(test_data['type'][:numb_test, :], [-1]),
t_natoms: test_data['natoms_vec'],
t_mesh: test_data['default_mesh'],
is_training: False
}
sess = tf.Session()
sess.run(tf.global_variables_initializer())
[e, f, v] = sess.run([energy, force, virial], feed_dict=feed_dict_test)
e = e.reshape([-1])
f = f.reshape([-1])
v = v.reshape([-1])
refe = [6.135449167779321300e+01]
reff = [
7.799691562262310585e-02, 9.423098804815030483e-02,
3.790560997388224204e-03, 1.432522403799846578e-01,
1.148392791403983204e-01, -1.321871172563671148e-02,
-7.318966526325138000e-02, 6.516069212737778116e-02,
5.406418483320515412e-04, 5.870713761026503247e-02,
-1.605402669549013672e-01, -5.089516979826595386e-03,
-2.554593467731766654e-01, 3.092063507347833987e-02,
1.510355029451411479e-02, 4.869271842355533952e-02,
-1.446113274345035005e-01, -1.126524434771078789e-03
]
refv = [
-6.076776685178300053e-01, 1.103174323630009418e-01,
1.984250991380156690e-02, 1.103174323630009557e-01,
-3.319759402259439551e-01, -6.007404107650986258e-03,
1.984250991380157036e-02, -6.007404107650981921e-03,
-1.200076017439753642e-03
]
refe = np.reshape(refe, [-1])
reff = np.reshape(reff, [-1])
refv = np.reshape(refv, [-1])
places = 10
for ii in range(e.size):
self.assertAlmostEqual(e[ii], refe[ii], places=places)
for ii in range(f.size):
self.assertAlmostEqual(f[ii], reff[ii], places=places)
for ii in range(v.size):
self.assertAlmostEqual(v[ii], refv[ii], places=places)
class DescrptSeAR():
def __init__(self, jdata):
args = ClassArg()\
.add('a', dict, must = True) \
.add('r', dict, must = True)
class_data = args.parse(jdata)
self.param_a = class_data['a']
self.param_r = class_data['r']
self.descrpt_a = DescrptSeA(self.param_a)
self.descrpt_r = DescrptSeR(self.param_r)
assert (self.descrpt_a.get_ntypes() == self.descrpt_r.get_ntypes())
self.davg = None
self.dstd = None
def get_rcut(self):
return np.max([self.descrpt_a.get_rcut(), self.descrpt_r.get_rcut()])
def get_ntypes(self):
return self.descrpt_r.get_ntypes()
def get_dim_out(self):
return (self.descrpt_a.get_dim_out() + self.descrpt_r.get_dim_out())
def get_nlist_a(self):
return self.descrpt_a.nlist, self.descrpt_a.rij, self.descrpt_a.sel_a, self.descrpt_a.sel_r
def get_nlist_r(self):
return self.descrpt_r.nlist, self.descrpt_r.rij, self.descrpt_r.sel_a, self.descrpt_r.sel_r
def compute_input_stats(self, data_coord, data_box, data_atype, natoms_vec,
mesh):
self.descrpt_a.compute_input_stats(data_coord, data_box, data_atype,
natoms_vec, mesh)
self.descrpt_r.compute_input_stats(data_coord, data_box, data_atype,
natoms_vec, mesh)
self.davg = [self.descrpt_a.davg, self.descrpt_r.davg]
self.dstd = [self.descrpt_a.dstd, self.descrpt_r.dstd]
def build(self, coord_, atype_, natoms, box, mesh, suffix='', reuse=None):
davg = self.davg
dstd = self.dstd
if davg is None:
davg = [
np.zeros([self.descrpt_a.ntypes, self.descrpt_a.ndescrpt]),
np.zeros([self.descrpt_r.ntypes, self.descrpt_r.ndescrpt])
]
if dstd is None:
dstd = [
np.ones([self.descrpt_a.ntypes, self.descrpt_a.ndescrpt]),
np.ones([self.descrpt_r.ntypes, self.descrpt_r.ndescrpt])
]
# dout
self.dout_a = self.descrpt_a.build(coord_,
atype_,
natoms,
box,
mesh,
suffix=suffix + '_a',
reuse=reuse)
self.dout_r = self.descrpt_r.build(coord_,
atype_,
natoms,
box,
mesh,
suffix=suffix,
reuse=reuse)
self.dout_a = tf.reshape(self.dout_a,
[-1, self.descrpt_a.get_dim_out()])
self.dout_r = tf.reshape(self.dout_r,
[-1, self.descrpt_r.get_dim_out()])
self.dout = tf.concat([self.dout_a, self.dout_r], axis=1)
self.dout = tf.reshape(self.dout, [-1, natoms[0] * self.get_dim_out()])
return self.dout
def prod_force_virial(self, atom_ener, natoms):
f_a, v_a, av_a = self.descrpt_a.prod_force_virial(atom_ener, natoms)
f_r, v_r, av_r = self.descrpt_r.prod_force_virial(atom_ener, natoms)
force = f_a + f_r
virial = v_a + v_r
atom_virial = av_a + av_r
return force, virial, atom_virial
def test_model(self):
jfile = 'polar_se_a.json'
with open(jfile) as fp:
jdata = json.load(fp)
run_opt = RunOptions(None)
systems = j_must_have(jdata, 'systems')
set_pfx = j_must_have(jdata, 'set_prefix')
batch_size = j_must_have(jdata, 'batch_size')
test_size = j_must_have(jdata, 'numb_test')
batch_size = 1
test_size = 1
stop_batch = j_must_have(jdata, 'stop_batch')
rcut = j_must_have(jdata['model']['descriptor'], 'rcut')
data = DataSystem(systems,
set_pfx,
batch_size,
test_size,
rcut,
run_opt=None)
test_data = data.get_test()
numb_test = 1
descrpt = DescrptSeA(jdata['model']['descriptor'])
fitting = PolarFittingSeA(jdata['model']['fitting_net'], descrpt)
model = PolarModel(jdata['model'], descrpt, fitting)
model._compute_dstats([test_data['coord']], [test_data['box']],
[test_data['type']], [test_data['natoms_vec']],
[test_data['default_mesh']])
t_prop_c = tf.placeholder(tf.float32, [5], name='t_prop_c')
t_energy = tf.placeholder(global_ener_float_precision, [None],
name='t_energy')
t_force = tf.placeholder(global_tf_float_precision, [None],
name='t_force')
t_virial = tf.placeholder(global_tf_float_precision, [None],
name='t_virial')
t_atom_ener = tf.placeholder(global_tf_float_precision, [None],
name='t_atom_ener')
t_coord = tf.placeholder(global_tf_float_precision, [None],
name='i_coord')
t_type = tf.placeholder(tf.int32, [None], name='i_type')
t_natoms = tf.placeholder(tf.int32, [model.ntypes + 2],
name='i_natoms')
t_box = tf.placeholder(global_tf_float_precision, [None, 9],
name='i_box')
t_mesh = tf.placeholder(tf.int32, [None], name='i_mesh')
is_training = tf.placeholder(tf.bool)
t_fparam = None
model_pred \
= model.build (t_coord,
t_type,
t_natoms,
t_box,
t_mesh,
t_fparam,
suffix = "polar_se_a",
reuse = False)
polar = model_pred['polar']
feed_dict_test = {
t_prop_c: test_data['prop_c'],
t_coord: np.reshape(test_data['coord'][:numb_test, :], [-1]),
t_box: test_data['box'][:numb_test, :],
t_type: np.reshape(test_data['type'][:numb_test, :], [-1]),
t_natoms: test_data['natoms_vec'],
t_mesh: test_data['default_mesh'],
is_training: False
}
sess = tf.Session()
sess.run(tf.global_variables_initializer())
[p] = sess.run([polar], feed_dict=feed_dict_test)
p = p.reshape([-1])
refp = [
3.39695248e+01, 2.16564043e+01, 8.18501479e-01, 2.16564043e+01,
1.38211789e+01, 5.22775159e-01, 8.18501479e-01, 5.22775159e-01,
1.97847218e-02, 8.08467431e-01, 3.42081126e+00, -2.01072261e-01,
3.42081126e+00, 1.54924596e+01, -9.06153697e-01, -2.01072261e-01,
-9.06153697e-01, 5.30193262e-02
]
places = 6
for ii in range(p.size):
self.assertAlmostEqual(p[ii], refp[ii], places=places)
def _init_param(self, jdata):
# model config
model_param = j_must_have(jdata, 'model')
descrpt_param = j_must_have(model_param, 'descriptor')
fitting_param = j_must_have(model_param, 'fitting_net')
# descriptor
descrpt_type = j_must_have(descrpt_param, 'type')
if descrpt_type == 'loc_frame':
self.descrpt = DescrptLocFrame(descrpt_param)
elif descrpt_type == 'se_a':
self.descrpt = DescrptSeA(descrpt_param)
elif descrpt_type == 'se_r':
self.descrpt = DescrptSeR(descrpt_param)
elif descrpt_type == 'se_ar':
self.descrpt = DescrptSeAR(descrpt_param)
else:
raise RuntimeError('unknow model type ' + descrpt_type)
# fitting net
try:
fitting_type = fitting_param['type']
except:
fitting_type = 'ener'
if fitting_type == 'ener':
self.fitting = EnerFitting(fitting_param, self.descrpt)
elif fitting_type == 'wfc':
self.fitting = WFCFitting(fitting_param, self.descrpt)
elif fitting_type == 'dipole':
if descrpt_type == 'se_a':
self.fitting = DipoleFittingSeA(fitting_param, self.descrpt)
else:
raise RuntimeError(
'fitting dipole only supports descrptors: se_a')
elif fitting_type == 'polar':
if descrpt_type == 'loc_frame':
self.fitting = PolarFittingLocFrame(fitting_param,
self.descrpt)
elif descrpt_type == 'se_a':
self.fitting = PolarFittingSeA(fitting_param, self.descrpt)
else:
raise RuntimeError(
'fitting polar only supports descrptors: loc_frame and se_a'
)
elif fitting_type == 'global_polar':
if descrpt_type == 'se_a':
self.fitting = GlobalPolarFittingSeA(fitting_param,
self.descrpt)
else:
raise RuntimeError(
'fitting global_polar only supports descrptors: loc_frame and se_a'
)
else:
raise RuntimeError('unknow fitting type ' + fitting_type)
# init model
# infer model type by fitting_type
if fitting_type == Model.model_type:
self.model = Model(model_param, self.descrpt, self.fitting)
elif fitting_type == 'wfc':
self.model = WFCModel(model_param, self.descrpt, self.fitting)
elif fitting_type == 'dipole':
self.model = DipoleModel(model_param, self.descrpt, self.fitting)
elif fitting_type == 'polar':
self.model = PolarModel(model_param, self.descrpt, self.fitting)
elif fitting_type == 'global_polar':
self.model = GlobalPolarModel(model_param, self.descrpt,
self.fitting)
else:
raise RuntimeError('get unknown fitting type when building model')
# learning rate
lr_param = j_must_have(jdata, 'learning_rate')
try:
lr_type = lr_param['type']
except:
lr_type = 'exp'
if lr_type == 'exp':
self.lr = LearningRateExp(lr_param)
else:
raise RuntimeError('unknown learning_rate type ' + lr_type)
# loss
# infer loss type by fitting_type
try:
loss_param = jdata['loss']
loss_type = loss_param.get('type', 'std')
except:
loss_param = None
loss_type = 'std'
if fitting_type == 'ener':
if loss_type == 'std':
self.loss = EnerStdLoss(
loss_param, starter_learning_rate=self.lr.start_lr())
elif loss_type == 'ener_dipole':
self.loss = EnerDipoleLoss(
loss_param, starter_learning_rate=self.lr.start_lr())
else:
raise RuntimeError('unknow loss type')
elif fitting_type == 'wfc':
self.loss = TensorLoss(loss_param,
model=self.model,
tensor_name='wfc',
tensor_size=self.model.get_out_size(),
label_name='wfc')
elif fitting_type == 'dipole':
self.loss = TensorLoss(loss_param,
model=self.model,
tensor_name='dipole',
tensor_size=3,
label_name='dipole')
elif fitting_type == 'polar':
self.loss = TensorLoss(loss_param,
model=self.model,
tensor_name='polar',
tensor_size=9,
label_name='polarizability')
elif fitting_type == 'global_polar':
self.loss = TensorLoss(loss_param,
model=self.model,
tensor_name='global_polar',
tensor_size=9,
atomic=False,
label_name='polarizability')
else:
raise RuntimeError(
'get unknown fitting type when building loss function')
# training
training_param = j_must_have(jdata, 'training')
tr_args = ClassArg()\
.add('numb_test', int, default = 1)\
.add('disp_file', str, default = 'lcurve.out')\
.add('disp_freq', int, default = 100)\
.add('save_freq', int, default = 1000)\
.add('save_ckpt', str, default = 'model.ckpt')\
.add('display_in_training', bool, default = True)\
.add('timing_in_training', bool, default = True)\
.add('profiling', bool, default = False)\
.add('profiling_file',str, default = 'timeline.json')\
.add('sys_probs', list )\
.add('auto_prob_style', str, default = "prob_sys_size")
tr_data = tr_args.parse(training_param)
self.numb_test = tr_data['numb_test']
self.disp_file = tr_data['disp_file']
self.disp_freq = tr_data['disp_freq']
self.save_freq = tr_data['save_freq']
self.save_ckpt = tr_data['save_ckpt']
self.display_in_training = tr_data['display_in_training']
self.timing_in_training = tr_data['timing_in_training']
self.profiling = tr_data['profiling']
self.profiling_file = tr_data['profiling_file']
self.sys_probs = tr_data['sys_probs']
self.auto_prob_style = tr_data['auto_prob_style']
self.useBN = False
if fitting_type == 'ener' and self.fitting.get_numb_fparam() > 0:
self.numb_fparam = self.fitting.get_numb_fparam()
else:
self.numb_fparam = 0
def test_model(self):
jfile = 'water_se_a_aparam.json'
with open(jfile) as fp:
jdata = json.load(fp)
run_opt = RunOptions(None)
systems = j_must_have(jdata, 'systems')
set_pfx = j_must_have(jdata, 'set_prefix')
batch_size = j_must_have(jdata, 'batch_size')
test_size = j_must_have(jdata, 'numb_test')
batch_size = 1
test_size = 1
stop_batch = j_must_have(jdata, 'stop_batch')
rcut = j_must_have(jdata['model']['descriptor'], 'rcut')
data = DataSystem(systems,
set_pfx,
batch_size,
test_size,
rcut,
run_opt=None)
test_data = data.get_test()
# manually set aparam
test_data['aparam'] = np.load('system/set.000/aparam.npy')
numb_test = 1
descrpt = DescrptSeA(jdata['model']['descriptor'])
fitting = EnerFitting(jdata['model']['fitting_net'], descrpt)
model = Model(jdata['model'], descrpt, fitting)
# model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']])
input_data = {
'coord': [test_data['coord']],
'box': [test_data['box']],
'type': [test_data['type']],
'natoms_vec': [test_data['natoms_vec']],
'default_mesh': [test_data['default_mesh']],
'aparam': [test_data['aparam']],
}
model._compute_dstats(input_data)
model.bias_atom_e = data.compute_energy_shift()
t_prop_c = tf.placeholder(tf.float32, [5], name='t_prop_c')
t_energy = tf.placeholder(global_ener_float_precision, [None],
name='t_energy')
t_force = tf.placeholder(global_tf_float_precision, [None],
name='t_force')
t_virial = tf.placeholder(global_tf_float_precision, [None],
name='t_virial')
t_atom_ener = tf.placeholder(global_tf_float_precision, [None],
name='t_atom_ener')
t_coord = tf.placeholder(global_tf_float_precision, [None],
name='i_coord')
t_type = tf.placeholder(tf.int32, [None], name='i_type')
t_natoms = tf.placeholder(tf.int32, [model.ntypes + 2],
name='i_natoms')
t_box = tf.placeholder(global_tf_float_precision, [None, 9],
name='i_box')
t_mesh = tf.placeholder(tf.int32, [None], name='i_mesh')
t_aparam = tf.placeholder(global_tf_float_precision, [None],
name='i_aparam')
is_training = tf.placeholder(tf.bool)
input_dict = {}
input_dict['aparam'] = t_aparam
model_pred\
= model.build (t_coord,
t_type,
t_natoms,
t_box,
t_mesh,
input_dict,
suffix = "se_a_aparam",
reuse = False)
energy = model_pred['energy']
force = model_pred['force']
virial = model_pred['virial']
atom_ener = model_pred['atom_ener']
feed_dict_test = {
t_prop_c: test_data['prop_c'],
t_energy: test_data['energy'][:numb_test],
t_force: np.reshape(test_data['force'][:numb_test, :], [-1]),
t_virial: np.reshape(test_data['virial'][:numb_test, :], [-1]),
t_atom_ener: np.reshape(test_data['atom_ener'][:numb_test, :],
[-1]),
t_coord: np.reshape(test_data['coord'][:numb_test, :], [-1]),
t_box: test_data['box'][:numb_test, :],
t_type: np.reshape(test_data['type'][:numb_test, :], [-1]),
t_natoms: test_data['natoms_vec'],
t_mesh: test_data['default_mesh'],
t_aparam: np.reshape(test_data['aparam'][:numb_test, :], [-1]),
is_training: False
}
sess = tf.Session()
sess.run(tf.global_variables_initializer())
[e, f, v] = sess.run([energy, force, virial], feed_dict=feed_dict_test)
e = e.reshape([-1])
f = f.reshape([-1])
v = v.reshape([-1])
refe = [61.35473702079649]
reff = [
7.789591210641927388e-02, 9.411176646369459609e-02,
3.785806413688173194e-03, 1.430830954178063386e-01,
1.146964190520970150e-01, -1.320340288927138173e-02,
-7.308720494747594776e-02, 6.508269338140809657e-02,
5.398739145542804643e-04, 5.863268336973800898e-02,
-1.603409523950408699e-01, -5.083084610994957619e-03,
-2.551569799443983988e-01, 3.087934885732580501e-02,
1.508590526622844222e-02, 4.863249399791078065e-02,
-1.444292753594846324e-01, -1.125098094204559241e-03
]
refv = [
-6.069498397488943819e-01, 1.101778888191114192e-01,
1.981907430646132409e-02, 1.101778888191114608e-01,
-3.315612988100872793e-01, -5.999739184898976799e-03,
1.981907430646132756e-02, -5.999739184898974197e-03,
-1.198656608172396325e-03
]
refe = np.reshape(refe, [-1])
reff = np.reshape(reff, [-1])
refv = np.reshape(refv, [-1])
places = 10
for ii in range(e.size):
self.assertAlmostEqual(e[ii], refe[ii], places=places)
for ii in range(f.size):
self.assertAlmostEqual(f[ii], reff[ii], places=places)
for ii in range(v.size):
self.assertAlmostEqual(v[ii], refv[ii], places=places)
def test_model(self):
jfile = 'water_se_a_fparam.json'
with open(jfile) as fp:
jdata = json.load(fp)
run_opt = RunOptions(None)
systems = j_must_have(jdata, 'systems')
set_pfx = j_must_have(jdata, 'set_prefix')
batch_size = j_must_have(jdata, 'batch_size')
test_size = j_must_have(jdata, 'numb_test')
batch_size = 1
test_size = 1
stop_batch = j_must_have(jdata, 'stop_batch')
rcut = j_must_have(jdata['model']['descriptor'], 'rcut')
data = DataSystem(systems,
set_pfx,
batch_size,
test_size,
rcut,
run_opt=None)
test_data = data.get_test()
numb_test = 1
descrpt = DescrptSeA(jdata['model']['descriptor'])
fitting = EnerFitting(jdata['model']['fitting_net'], descrpt)
model = Model(jdata['model'], descrpt, fitting)
model._compute_dstats([test_data['coord']], [test_data['box']],
[test_data['type']], [test_data['natoms_vec']],
[test_data['default_mesh']])
model.bias_atom_e = data.compute_energy_shift()
t_prop_c = tf.placeholder(tf.float32, [5], name='t_prop_c')
t_energy = tf.placeholder(global_ener_float_precision, [None],
name='t_energy')
t_force = tf.placeholder(global_tf_float_precision, [None],
name='t_force')
t_virial = tf.placeholder(global_tf_float_precision, [None],
name='t_virial')
t_atom_ener = tf.placeholder(global_tf_float_precision, [None],
name='t_atom_ener')
t_coord = tf.placeholder(global_tf_float_precision, [None],
name='i_coord')
t_type = tf.placeholder(tf.int32, [None], name='i_type')
t_natoms = tf.placeholder(tf.int32, [model.ntypes + 2],
name='i_natoms')
t_box = tf.placeholder(global_tf_float_precision, [None, 9],
name='i_box')
t_mesh = tf.placeholder(tf.int32, [None], name='i_mesh')
t_fparam = tf.placeholder(global_tf_float_precision, [None],
name='i_fparam')
is_training = tf.placeholder(tf.bool)
input_dict = {}
input_dict['fparam'] = t_fparam
model_pred\
= model.build (t_coord,
t_type,
t_natoms,
t_box,
t_mesh,
input_dict,
suffix = "se_a_fparam",
reuse = False)
energy = model_pred['energy']
force = model_pred['force']
virial = model_pred['virial']
atom_ener = model_pred['atom_ener']
feed_dict_test = {
t_prop_c: test_data['prop_c'],
t_energy: test_data['energy'][:numb_test],
t_force: np.reshape(test_data['force'][:numb_test, :], [-1]),
t_virial: np.reshape(test_data['virial'][:numb_test, :], [-1]),
t_atom_ener: np.reshape(test_data['atom_ener'][:numb_test, :],
[-1]),
t_coord: np.reshape(test_data['coord'][:numb_test, :], [-1]),
t_box: test_data['box'][:numb_test, :],
t_type: np.reshape(test_data['type'][:numb_test, :], [-1]),
t_natoms: test_data['natoms_vec'],
t_mesh: test_data['default_mesh'],
t_fparam: np.reshape(test_data['fparam'][:numb_test, :], [-1]),
is_training: False
}
sess = tf.Session()
sess.run(tf.global_variables_initializer())
[e, f, v] = sess.run([energy, force, virial], feed_dict=feed_dict_test)
e = e.reshape([-1])
f = f.reshape([-1])
v = v.reshape([-1])
refe = [6.135136929183754972e+01]
reff = [
7.761477777656561328e-02, 9.383013575207051205e-02,
3.776776376267230399e-03, 1.428268971463224069e-01,
1.143858253900619654e-01, -1.318441687719179231e-02,
-7.271897092708884403e-02, 6.494907553857684479e-02,
5.355599592111062821e-04, 5.840910251709752199e-02,
-1.599042555763417750e-01, -5.067165555590445389e-03,
-2.546246315216804113e-01, 3.073296814647456451e-02,
1.505994759166155023e-02, 4.849282500878367153e-02,
-1.439937492508420736e-01, -1.120701494357654411e-03
]
refv = [
-6.054303146013112480e-01, 1.097859194719944115e-01,
1.977605183964963390e-02, 1.097859194719943976e-01,
-3.306167096812382966e-01, -5.978855662865613894e-03,
1.977605183964964083e-02, -5.978855662865616497e-03,
-1.196331922996723236e-03
]
refe = np.reshape(refe, [-1])
reff = np.reshape(reff, [-1])
refv = np.reshape(refv, [-1])
places = 10
for ii in range(e.size):
self.assertAlmostEqual(e[ii], refe[ii], places=places)
for ii in range(f.size):
self.assertAlmostEqual(f[ii], reff[ii], places=places)
for ii in range(v.size):
self.assertAlmostEqual(v[ii], refv[ii], places=places)
