Merge pull request #9 from Yaroslav-Muravev/just_try

Add verbose display as parametr for DeepXDE

Author: Yaroslav-Muravev

.github/workflows/test-functional.yml

@@ -17,7 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         python-version: ["3.11"]
-        operator: ["L2LRFitness"]
+        operator: ["DeepXDEBasedFitness"]
         split: [1, 2, 3, 4]
 
     steps:

epde/integrate/deepxde_integration.py

@@ -62,12 +62,9 @@ def func(x):
                                 num_boundary=adapter.num_boundary,
                                 num_test=500)
 
-        layer_size = [1] + adapter.net + [len(var_names)]
-        net = dde.nn.FNN(layer_size, adapter.activation, adapter.kernel_initializer)
-        model = dde.Model(data_obj, net)
-        model.compile(adapter.optimizer, lr=adapter.lr)
+        model = adapter._get_or_create_model(data_obj, dim=1, var_count=len(var_names))
         try:
-            losshistory, train_state = model.train(epochs=adapter.epochs)
+            losshistory, train_state = model.train(iterations=adapter.iterations, verbose=adapter.verbose)
             final_loss = float(
                 losshistory.loss_train[-1][0]) if losshistory.loss_train else np.nan
         except Exception as e:
@@ -140,12 +137,9 @@ def func(x):
                                     num_initial=adapter.num_initial,
                                     num_test=500)
 
-        layer_size = [geomtime.dim] + adapter.net + [len(var_names)]
-        net = dde.nn.FNN(layer_size, adapter.activation, adapter.kernel_initializer)
-        model = dde.Model(data_obj, net)
-        model.compile(adapter.optimizer, lr=adapter.lr)
+        model = adapter._get_or_create_model(data_obj, dim=2, var_count=len(var_names))
         try:
-            losshistory, train_state = model.train(epochs=adapter.epochs)  # <-- ИСПРАВЛЕНО
+            losshistory, train_state = model.train(iterations=adapter.iterations, verbose=adapter.verbose)  # <-- ИСПРАВЛЕНО
             final_loss = float(losshistory.loss_train[-1][0]) if losshistory.loss_train else np.nan
         except Exception as e:
             print(f"Exception: {e}")
@@ -233,12 +227,9 @@ def func(x):
                                     num_initial=adapter.num_initial,
                                     num_test=500)
 
-        layer_size = [geomtime.dim] + adapter.net + [len(var_names)]
-        net = dde.nn.FNN(layer_size, adapter.activation, adapter.kernel_initializer)
-        model = dde.Model(data_obj, net)
-        model.compile(adapter.optimizer, lr=adapter.lr)
+        model = adapter._get_or_create_model(data_obj, dim=3, var_count=len(var_names))
         try:
-            losshistory, train_state = model.train(epochs=adapter.epochs)  # <-- ИСПРАВЛЕНО
+            losshistory, train_state = model.train(iterations=adapter.iterations, verbose=adapter.verbose)  # <-- ИСПРАВЛЕНО
             final_loss = float(losshistory.loss_train[-1][0]) if losshistory.loss_train else np.nan
         except Exception as e:
             print(f"Exception: {e}")
@@ -263,10 +254,12 @@ def __init__(self, pretrained_net=None, **config):
         self.num_domain = int(self.config.get('num_domain', 2000))
         self.num_boundary = int(self.config.get('num_boundary', 500))
         self.num_initial = int(self.config.get('num_initial', 500))
-        self.epochs = int(self.config.get('epochs', 10000))
+        #self.epochs = int(self.config.get('epochs', 10000))
+        self.iterations = int(self.config.get('iterations', 10000))
         # self.iterations = int(self.config.get('epochs', 5))
         self.bc_type = self.config.get('bc_type', 'Dirichlet')
         self.fallback_bc_value = self.config.get('fallback_bc_value', 0.0)
+        self.verbose = config.get('verbose', False)
 
         self.coordinate_mapping = self.config.get('coordinate_mapping', None)
         self.coord_names = None
@@ -278,6 +271,23 @@ def __init__(self, pretrained_net=None, **config):
             3: Solver3D(),
         }
 
+        self._model = None
+
+    def _get_or_create_model(self, data_obj, dim, var_count):
+        if self._model is None:
+            layer_size = [dim] + self.net + [var_count]
+            net = dde.nn.FNN(layer_size, self.activation, self.kernel_initializer)
+            model = dde.Model(data_obj, net)
+            model.compile(self.optimizer, lr=self.lr, verbose=self.verbose)
+            self._model = model
+        else:
+            def reset_weights(m):
+                if hasattr(m, 'reset_parameters'):
+                    m.reset_parameters()
+            self._model.net.apply(reset_weights)
+            self._model.data = data_obj
+        return self._model
+
     def _set_coordinate_info(self, coord_names):
         self.coord_names = coord_names
         if self.coordinate_mapping is not None:

epde/operators/common/fitness.py

@@ -340,7 +340,7 @@ def _apply_deepxde(self, objective, force_out_of_place):
                              penalty_coeff=self.params.get('penalty_coeff', 0.2),
                              for_rps=False)
         # Pack per-eq masked (solution, data) for DeepXDEError.
-        masked_solutions = [solution_list[i][mask_flat] for i in range(len(eqs))] # был solution_list
+        masked_solutions = [solution_list[i][mask_flat] for i in range(len(eqs))]
         masked_data = [data_list[i] for i in range(len(eqs))]
         sctx = SolverContext(solution=masked_solutions, loss_add=loss,
                              g_fun_vals=masked_data,

epde/operators/utils/parameters/default_parameters_multi_objective.json

@@ -31,7 +31,9 @@
             "num_domain": 1000,
             "num_boundary": 200,
             "num_initial": 200,
-            "epochs": 1000
+            "iterations": 1000,
+            "epochs": 1000,
+            "verbose": false
         }
  	},
 	"_comment_legacy_aliases": "Param-registry aliases kept ONLY so the functional test harness (tests/functional) can fetch params by the historical operator names; the production search builds the new SolverFreeFitness / SolverBasedFitness hosts.",