add decay for learning rate

pinnicle/parameter.py

@@ -372,6 +372,10 @@ def set_default(self):
         self.additional_loss = {} 
         # learning rate
         self.learning_rate = 0
+        # decay steps
+        self.decay_steps = 0
+        # decay rate
+        self.decay_rate = 0.0
         # list of the weights
         self.loss_weights = []
         # setting the callbacks

pinnicle/pinn.py

@@ -34,7 +34,7 @@ def check_path(self, path, loadOnly=False):
             os.makedirs(path, exist_ok=True)
         return path
 
-    def compile(self, opt=None, loss=None, lr=None, loss_weights=None):
+    def compile(self, opt=None, loss=None, lr=None, loss_weights=None, decay=None):
         """ compile the model  
         """
         # load from params
@@ -47,11 +47,14 @@ def compile(self, opt=None, loss=None, lr=None, loss_weights=None):
         if lr is None:
             lr = self.params.training.learning_rate
 
+        if (decay is None) and (self.params.training.decay_steps > 0) and (self.params.training.decay_rate>0.0):
+            decay = ("inverse time", self.params.training.decay_steps, self.params.training.decay_rate)
+
         if loss_weights is None:
             loss_weights = self.params.training.loss_weights
 
         # compile the model
-        self.model.compile(opt, loss=loss, lr=lr, loss_weights=loss_weights)
+        self.model.compile(opt, loss=loss, lr=lr, decay=decay, loss_weights=loss_weights)
 
     def load_model(self, path="", epochs=-1, subfolder="pinn", name="model", fileformat=""):
         """laod the neural network from saved model

tests/test_parameters.py

@@ -156,7 +156,11 @@ def test_dummy_equation_parameters():
 
 def test_training_parameters():
     hp =  {}
+    hp['decay_steps'] = 10
+    hp['decay_rate'] = 0.3
     p = TrainingParameter(hp)
+    assert p.decay_steps == 10
+    assert p.decay_rate == 0.3
     assert p.additional_loss == {}
     u_loss = {}
     u_loss['name'] = "vel log"

tests/test_pinn.py

@@ -142,6 +142,20 @@ def test_train(tmp_path):
     hp_local["data"] = {"ISSM": issm}
     hp_local["equations"] = {"SSA":SSA}
     experiment = pinn.PINN(params=hp_local)
+    experiment.compile(decay=("inverse time", 5, 0.3))
+    experiment.train()
+    assert experiment.loss_names == ['fSSA1', 'fSSA2', 'u', 'v', 's', 'H', 'C']
+
+def test_train_decay(tmp_path):
+    hp_local = dict(hp)
+    hp_local["is_save"] = False
+    hp_local["num_collocation_points"] = 100
+    issm["data_size"] = {"u":None, "v":100, "s":100, "H":100, "C":None}
+    hp_local["data"] = {"ISSM": issm}
+    hp_local["equations"] = {"SSA":SSA}
+    hp_local["decay_steps"] = 5
+    hp_local["decay_rate"]= 0.3
+    experiment = pinn.PINN(params=hp_local)
     experiment.compile()
     experiment.train()
     assert experiment.loss_names == ['fSSA1', 'fSSA2', 'u', 'v', 's', 'H', 'C']