afdee1c/symbolic-regression
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Silviu Marian Udrescu 2020-03-23 03:35:19 -04:00 • committed by GitHub
parent 5a301e2f48
commit f0cc7dfcaa
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19 changed files with 1210 additions and 182 deletions
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Code/S_NN_train.py
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@ -41,7 +41,7 @@ def rmse_loss(pred, targ):
denom = torch.sqrt(denom.sum()/len(denom))
return torch.sqrt(F.mse_loss(pred, targ))/denom
def NN_train(pathdir, filename, epochs=-1):
def NN_train(pathdir, filename, epochs=1000, lrs=1e-2, N_red_lr=4, pretrained_path=""):
try:
os.mkdir("results/NN_trained_models/")
except:
@ -55,13 +55,14 @@ def NN_train(pathdir, filename, epochs=-1):
n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
epochs = epochs//4
epochs = epochs//N_red_lr
epochs = int(epochs)
if n_variables==0:
print("Solved! ", variables[0])
if n_variables==0 or n_variables==1:
print("Solved!")#, variables[0])
return 0
elif n_variables==1:
variables = np.reshape(variables,(len(variables),1))
#elif n_variables==1:
# variables = np.reshape(variables,(len(variables),1))
else:
for j in range(1,n_variables):
v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
@ -113,10 +114,14 @@ def NN_train(pathdir, filename, epochs=-1):
else:
model_feynman = SimpleNet(n_variables)
if pretrained_path!="":
model_feynman.load_state_dict(torch.load(pretrained_path))
max_loss = 10000
lrs = 1e-2
for i_i in range(4):
print("EPOCH", epochs)
for i_i in range(N_red_lr):
optimizer_feynman = optim.Adam(model_feynman.parameters(), lr = lrs)
for epoch in range(epochs):
model_feynman.train()