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BergEC-jl
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Major bug fix

This commit is contained in:
Nuwan Yapa 2025-04-30 01:05:53 -05:00
parent b30ee6e1fb
commit 336a6a1020
1 changed files with 3 additions and 3 deletions
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6
calculations/PMM.py
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@ -50,7 +50,7 @@ c_steps = np.delete(c_steps, 0) # remove the first point (c=0)
optimizer = torch.optim.Adam([H0, H1]) optimizer = torch.optim.Adam([H0, H1])
# Training loop # Training loop
epochs = 200000 epochs = 50000
for epoch in range(epochs): for epoch in range(epochs):
ks = torch.empty(len(train_data), dtype=torch.complex128) ks = torch.empty(len(train_data), dtype=torch.complex128)
kvs = torch.empty(len(train_data), dtype=torch.complex128) kvs = torch.empty(len(train_data), dtype=torch.complex128)
@ -70,10 +70,10 @@ for epoch in range(epochs):
loss = ((ks - train_ks).abs() ** 2).sum() loss = ((ks - train_ks).abs() ** 2).sum()
# push virtual states towards (-)bound and then to the imaginary axis # push virtual states towards (-)bound and then to the imaginary axis
if epoch/epochs < 0.25: if epoch/epochs < 0.75:
loss += ((kvs + train_ks).abs() ** 2).sum() loss += ((kvs + train_ks).abs() ** 2).sum()
else: else:
loss += (kvs.imag ** 2).sum() loss += (kvs.real ** 2).sum()
if epoch % 1000 == 0: if epoch % 1000 == 0:
print(f"Training {(epoch+1)/epochs:.1%} \t Loss: {loss}") print(f"Training {(epoch+1)/epochs:.1%} \t Loss: {loss}")