save B for fft automatically

pinnicle/parameter.py

@@ -209,7 +209,9 @@ def check_consistency(self):
             if self.input_size != self.num_fourier_feature*2:
                 raise ValueError("'input_size' does not match the number of fourier feature")
             if self.B is not None:
-                if self.B.shape[1] != self.num_fourier_feature:
+                if not isinstance(self.B, list):
+                    raise TypeError("'B' matrix need to be input in a list")
+                if len(self.B[0]) != self.num_fourier_feature:
                     raise ValueError("Number of columns of 'B' matrix does not match the number of fourier feature")
         else:
             # input size of nn equals to dependent in physics

pinnicle/pinn.py

@@ -158,6 +158,9 @@ def setup(self):
         self._update_ub_lb_in_nn(self.model_data)
         # define the neural network in use
         self.nn = FNN(self.params.nn)
+        # save B if it is not defined by the user and generated by FFT
+        if (self.params.nn.B is None) and (self.params.nn.fft):
+            self.params.param_dict.update({"B": dde.backend.to_numpy(self.nn.B).tolist()})
 
         # Step 7: setup the deepxde PINN model
         self.model = dde.Model(self.dde_data, self.nn.net)

tests/test_nn.py

@@ -55,7 +55,7 @@ def test_input_fft_nn():
     z = y**2
     assert np.all(abs(z[:,1:10]+z[:,11:20]) <= 1.0+np.finfo(float).eps)
 
-    hp['B'] = np.array([[1,2,3]])
+    hp['B'] = [[1,2,3]]
     hp['num_fourier_feature'] = 3
     d = NNParameter(hp)
     p = pinn.nn.FNN(d)

tests/test_parameters.py

@@ -83,11 +83,11 @@ def test_nn_parameter():
     assert d.is_input_scaling()
     assert d.B is None
 
-    d = NNParameter({"fft":True, "num_fourier_feature":4, "B":np.array([[1,2,3,4]])})
+    d = NNParameter({"fft":True, "num_fourier_feature":4, "B":[[1,2,3,4]]})
     assert d.B is not None
     with pytest.raises(Exception):
-        d = NNParameter({"fft":True, "num_fourier_feature":4, "B":np.array([[1,2]])})
         d = NNParameter({"fft":True, "num_fourier_feature":4, "B":1})
+        d = NNParameter({"fft":True, "num_fourier_feature":4, "B":[[1,2]]})
 
 def test_parameters():
     p = Parameters()

tests/test_pinn.py

@@ -146,6 +146,24 @@ def test_train(tmp_path):
     experiment.train()
     assert experiment.loss_names == ['fSSA1', 'fSSA2', 'u', 'v', 's', 'H', 'C']
 
+def test_fft_training(tmp_path):
+    hp_local = dict(hp)
+    hp_local['fft'] = True
+    hp_local["is_save"] = False
+    hp_local["num_collocation_points"] = 10
+    issm["data_size"] = {"u":10, "v":10, "s":10, "H":10, "C":None}
+    hp_local["data"] = {"ISSM": issm}
+    hp_local["equations"] = {"SSA":SSA}
+    experiment = pinn.PINN(params=hp_local)
+    experiment.save_setting(path=tmp_path)
+    assert experiment.params.param_dict == experiment.load_setting(path=tmp_path)
+    assert experiment.params.nn.B is None
+    assert os.path.isdir(f"{tmp_path}/pinn/")
+    experiment2 = pinn.PINN(loadFrom=tmp_path)
+    assert experiment.params.param_dict == experiment2.params.param_dict
+    assert len(experiment2.params.nn.B) == 2
+    assert len(experiment2.params.nn.B[1]) == 10    
+
 @pytest.mark.skipif(backend_name in ["jax"], reason="save model is not implemented in deepxde for jax")
 def test_train_PFNN(tmp_path):
     hp_local = dict(hp)