def train_reax(writelib=1000,
print_step=10,
step=500,
opt=None,
cons=None,
lr=1.0e-4,
convergence=0.97,
lossConvergence=100.0,
batch=50):
rn = ReaxFF(libfile='ffield.json',
direcs=direcs,
dft='siesta',
optword='nocoul',
opt=None,
cons=None,
batch_size=batch,
losFunc='n2',
lambda_bd=10000.0,
lambda_me=0.01,
weight={
'al4': 2.0,
'others': 2.0
},
convergence=convergence,
lossConvergence=lossConvergence
) # Loss Functon can be n2,abs,mse,huber
# GradientDescentOptimizer AdamOptimizer AdagradOptimizer RMSPropOptimizer
loss, accu, accMax, i, zpe = rn.run(learning_rate=lr,
step=step,
print_step=print_step,
writelib=writelib)
libstep = int(i - i % writelib)
if i == libstep:
libstep = libstep - writelib
if libstep <= 0:
ffd = 'ffield.json'
else:
ffd = 'ffield_' + str(libstep) + '.json'
if loss == 0.0 and accu == 0.0:
send_msg(
'- Warning: the loss is NaN, parameters from %s changed auomatically ...'
% ffd)
return 0.0, 1.0, 1.0, None, None, i
# with open(ffd,'r') as fj:
# j = js.load(fj)
# ic = Init_Check(nanv=nanv)
# j['p'] = ic.auto(j['p'])
# ic.close()
#with open('ffield.json','w') as fj:
# js.dump(j,fj,sort_keys=True,indent=2)
p = rn.p_
ME = rn.MolEnergy_
rn.close()
return loss, accu, accMax, p, ME, i
def z():
opt = ['atomic', 'ovun5', 'Desi', 'Depi', 'Depp', 'Devdw', 'Dehb'],
rn = ReaxFF(libfile='ffield',
direcs=direcs,
dft='siesta',
atomic=True,
optword='nocoul',
opt=['atomic'],
nn=False,
cons=None,
pkl=True,
batch_size=batch,
losFunc='n2',
bo_penalty=10000.0)
# tc.session(learning_rate=1.0e-4,method='AdamOptimizer')
# GradientDescentOptimizer AdamOptimizer AdagradOptimizer RMSPropOptimizer
rn.run(learning_rate=100, step=1000, print_step=10, writelib=1000)
rn.close()
def r():
rn = ReaxFF(libfile='ffield',
direcs=direcs,
dft='siesta',
atomic=True,
InitCheck=False,
optword='nocoul',
VariablesToOpt=vto,
pkl=True,
batch_size=batch,
losFunc='n2',
bo_penalty=10.0)
# tc.session(learning_rate=1.0e-4,method='AdamOptimizer')
# GradientDescentOptimizer AdamOptimizer AdagradOptimizer RMSPropOptimizer
rn.run(
learning_rate=1.0e-4,
step=10000,
method='AdamOptimizer', # SGD
print_step=10,
writelib=1000)
rn.close()