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BergEC-jl
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Adjust parameters

This commit is contained in:
Nuwan Yapa 2025-04-27 21:00:01 -04:00
parent b2c2b80a83
commit 66701bab38
1 changed files with 3 additions and 3 deletions
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calculations/PMM.py
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@ -17,7 +17,7 @@ train_ks = torch.tensor(train_data['k'].to_numpy(), dtype=torch.complex128)
#%% #%%
# hyperparameters # hyperparameters
N = 9 N = 5
# initialize random Hamiltonians # initialize random Hamiltonians
H0 = torch.randn(N, N, dtype=torch.complex128) H0 = torch.randn(N, N, dtype=torch.complex128)
@ -29,11 +29,11 @@ H1 = (H1 + torch.transpose(H1, 0, 1)).requires_grad_() # symmetric
# training # training
# generate a set of c values to follow by subdividing the training cs # generate a set of c values to follow by subdividing the training cs
subdivisions = 3 subdivisions = 2
c_steps = np.concatenate([np.linspace(start, stop, subdivisions, endpoint=False) for (start, stop) in zip(train_cs, train_cs[1:])]) c_steps = np.concatenate([np.linspace(start, stop, subdivisions, endpoint=False) for (start, stop) in zip(train_cs, train_cs[1:])])
c_steps = np.append(c_steps, train_cs[-1]) c_steps = np.append(c_steps, train_cs[-1])
lr = 0.05 lr = 0.01
epochs = 100000 epochs = 100000
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)