Adam optimizer

calculations/PMM.py

@@ -45,7 +45,10 @@ c_steps = np.concatenate([np.linspace(start, stop, subdivisions, endpoint=False)
 c_steps = np.append(c_steps, train_cs[-1])
 c_steps = np.delete(c_steps, 0) # remove the first point (c=0)
 
-lr = 0.01
+# Initialize the Adam optimizer
+optimizer = torch.optim.Adam([H0, H1])
+
+# Training loop
 epochs = 200000
 for epoch in range(epochs):
     ks = torch.empty(len(train_data), dtype=torch.complex128)
@@ -74,15 +77,12 @@ for epoch in range(epochs):
     if epoch % 1000 == 0:
         print(f"Training {(epoch+1)/epochs:.1%} \t Loss: {loss}")
 
-    if H0.grad is not None:
+    # Zero gradients, backpropagate, and update parameters
-        H0.grad.zero_()
+    optimizer.zero_grad()
-    if H1.grad is not None:
-        H1.grad.zero_()
     loss.backward()
+    optimizer.step()
 
-    with torch.no_grad():
+    # Enforce exceptional point constraints
-        H0 -= lr * H0.grad
-        H1 -= lr * H1.grad
     enforce_ep()
 
 # %%