for i in range(1):
net.wf.fc.weight.requires_grad = True
net.wf.rbf.centers.requires_grad = False
pos, obs_dict = net.train(250,
batchsize=250,
pos=pos,
obs_dict=obs_dict,
resample=100,
ntherm=-1,
loss='variance',
sol=morse_sol,
fig=fig)
# net.wf.fc.weight.requires_grad = False
# net.wf.rbf.centers.requires_grad = True
# pos,obs_dict = net.train(50,
# batchsize=250,
# pos = pos,
# obs_dict = obs_dict,
# resample=100,
# ntherm=-1,
# loss = 'variance',
# sol=morse_sol,
# fig=fig)
net.plot_results(obs_dict, morse_sol, e0=-0.125)
fig = plt.figure()
for i in range(1):
net.wf.fc.weight.requires_grad = True
net.wf.rbf.centers.requires_grad = False
pos, obs_dict = net.train(1000,
batchsize=250,
pos=pos,
obs_dict=obs_dict,
resample=100,
ntherm=-1,
loss='variance',
fig=fig)
# net.wf.fc.weight.requires_grad = False
# net.wf.rbf.centers.requires_grad = True
# pos,obs_dict = net.train(50,
# batchsize=250,
# pos = pos,
# obs_dict = obs_dict,
# resample=100,
# ntherm=-1,
# loss = 'variance',
# sol=morse_sol,
# fig=fig)
net.plot_results(obs_dict)