afdee1c/symbolic-regression
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Silviu Marian Udrescu 2020-04-07 02:31:51 -04:00 • committed by GitHub
parent def13c9018
commit ae3d4e9a8d
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2 changed files with 17 additions and 9 deletions
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20
Code/S_NN_train.py
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@ -61,8 +61,7 @@ def NN_train(pathdir, filename, epochs=1000, lrs=1e-2, N_red_lr=4, pretrained_pa
if n_variables==0 or n_variables==1: if n_variables==0 or n_variables==1:
print("Solved!")#, variables[0]) print("Solved!")#, variables[0])
return 0 return 0
#elif n_variables==1:
# variables = np.reshape(variables,(len(variables),1))
else: else:
for j in range(1,n_variables): for j in range(1,n_variables):
v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,)) v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
@ -117,9 +116,7 @@ def NN_train(pathdir, filename, epochs=1000, lrs=1e-2, N_red_lr=4, pretrained_pa
if pretrained_path!="": if pretrained_path!="":
model_feynman.load_state_dict(torch.load(pretrained_path)) model_feynman.load_state_dict(torch.load(pretrained_path))
max_loss = 10000 check_es_loss = 10000
print("EPOCH", epochs)
for i_i in range(N_red_lr): for i_i in range(N_red_lr):
optimizer_feynman = optim.Adam(model_feynman.parameters(), lr = lrs) optimizer_feynman = optim.Adam(model_feynman.parameters(), lr = lrs)
@ -136,12 +133,17 @@ def NN_train(pathdir, filename, epochs=1000, lrs=1e-2, N_red_lr=4, pretrained_pa
prd = data[1].float() prd = data[1].float()
loss = rmse_loss(model_feynman(fct),prd) loss = rmse_loss(model_feynman(fct),prd)
if loss < max_loss:
torch.save(model_feynman.state_dict(), "results/NN_trained_models/models/" + filename + ".h5")
max_loss = loss
loss = rmse_loss(model_feynman(fct),prd)
loss.backward() loss.backward()
optimizer_feynman.step() optimizer_feynman.step()
# Early stopping
if epoch%20==0:
if check_es_loss < loss:
break
else:
torch.save(model_feynman.state_dict(), "results/NN_trained_models/models/" + filename + ".h5")
check_es_loss = loss
print(loss) print(loss)
lrs = lrs/10 lrs = lrs/10

6
Code/S_separability.py
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@ -230,7 +230,13 @@ def check_separability_multiply(pathdir, filename):
f_dependent = np.loadtxt(pathdir+filename, usecols=(n_variables,)) f_dependent = np.loadtxt(pathdir+filename, usecols=(n_variables,))
# Pick only data which is close enough to the maximum value (5 times less or higher)
max_output = np.max(abs(f_dependent))
use_idx = np.where(abs(f_dependent)>=max_output/5)
f_dependent = f_dependent[use_idx]
f_dependent = np.reshape(f_dependent,(len(f_dependent),1)) f_dependent = np.reshape(f_dependent,(len(f_dependent),1))
variables = variables[use_idx]
factors = torch.from_numpy(variables) factors = torch.from_numpy(variables)
if is_cuda: if is_cuda: