Fix

jointContribution/PIRBN/analytical_solution.py

@@ -48,6 +48,16 @@ def output_fig(train_obj, mu, b, right_by, activation_function):
     plt.ylabel("Loss")
     plt.xlabel("Iteration")
 
+    # plt.subplot(2, 3, 3)
+    # loss_g = train_obj.his_a_g
+    # x = range(len(loss_g))
+    # plt.yscale("log")
+    # plt.plot(x, loss_g)
+    # plt.plot(x, train_obj.his_a_b)
+    # plt.legend(["a_g", "a_b"])
+    # plt.ylabel("Loss")
+    # plt.xlabel("Iteration")
+
     # Visualise NTK after initialisation, The normalised Kg at 0th iteration.
     plt.subplot(2, 3, 4)
     jac = train_obj.ntk_list[0]

jointContribution/PIRBN/train.py

@@ -19,19 +19,27 @@ def __init__(self, pirbn, x_train, y_train, learning_rate=0.001, maxiter=10000):
         self.iter = 0
         self.a_g = paddle.to_tensor(1.0)
         self.a_b = paddle.to_tensor(1.0)
+        self.his_a_g = []
+        self.his_a_b = []
         self.optimizer = paddle.optimizer.Adam(
             learning_rate=0.001, parameters=self.pirbn.parameters()
         )
         self.ntk_list = {}
+        # Update loss by calculate ntk
+        self.update_loss_by_ntk = True
+
+        # For test
+        # if self.pirbn.activation_function == "tanh":
+        #     self.update_loss_by_ntk = False
 
     def Loss(self, x, y, a_g, a_b):
         tmp = self.pirbn(x)
         loss_g = 0.5 * paddle.mean(paddle.square(tmp[0] - y[0]))
         loss_b = 0.5 * paddle.mean(paddle.square(tmp[1]))
-        if self.pirbn.activation_function == "tanh":
-            loss = loss_g + loss_b
-        else:
+        if self.update_loss_by_ntk:
             loss = loss_g * a_g + loss_b * a_b
+        else:
+            loss = loss_g + loss_b
         return loss, loss_g, loss_b
 
     def evaluate(self):
@@ -44,16 +52,20 @@ def evaluate(self):
         # boundary loss
         self.loss_b.append(loss_b_numpy)
         if self.iter % 200 == 0:
-            if self.pirbn.activation_function == "gaussian_function":
+            if self.update_loss_by_ntk:
                 self.a_g, self.a_b, _ = self.pirbn.cal_ntk(self.x_train)
                 print("\ta_g =", float(self.a_g), "\ta_b =", float(self.a_b))
                 print(
                     "Iter: ", self.iter, "\tL1 =", loss_g_numpy, "\tL2 =", loss_b_numpy
                 )
-            if self.pirbn.activation_function == "tanh":
+            else:
+                a_g, a_b, _ = self.pirbn.cal_ntk(self.x_train)
+                print("\ta_g =", float(a_g), "\ta_b =", float(a_b))
                 print(
                     "Iter: ", self.iter, "\tL1 =", loss_g_numpy, "\tL2 =", loss_b_numpy
                 )
+        self.his_a_g.append(self.a_g)
+        self.his_a_b.append(self.a_b)
 
         self.iter = self.iter + 1
         return loss