Add warning for weight decay (#227)

examples/nnp_training.py

@@ -182,6 +182,13 @@ model = torch.nn.Sequential(aev_computer, nn).to(device)
 #   Also note that the weight decay only applies to weight in the training
 #   of ANI models, not bias.
 #
+# .. warning::
+#
+#   Currently TorchANI training with weight decay can not reproduce the training
+#   result of NeuroChem with the same training setup. If you really want to use
+#   weight decay, consider smaller rates and and make sure you do enough validation
+#   to check if you get expected result.
+#
 # .. _Decoupled Weight Decay Regularization:
 #   https://arxiv.org/abs/1711.05101
 optimizer = torchani.optim.AdamW([

torchani/neurochem/__init__.py

@@ -17,6 +17,8 @@ from ..nn import ANIModel, Ensemble, Gaussian
 from ..utils import EnergyShifter, ChemicalSymbolsToInts
 from ..aev import AEVComputer
 from ..optim import AdamW
+import warnings
+import textwrap
 
 
 class Constants(collections.abc.Mapping):
@@ -422,6 +424,8 @@ if sys.version_info[0] > 2:
             self.imports.BatchedANIDataset = BatchedANIDataset
             self.imports.AEVCacheLoader = AEVCacheLoader
 
+            self.warned = False
+
             self.filename = filename
             self.device = device
             self.aev_caching = aev_caching
@@ -632,6 +636,13 @@ if sys.version_info[0] > 2:
                         modules.append(activation)
                     del layer['activation']
                     if 'l2norm' in layer:
+                        if not self.warned:
+                            warnings.warn(textwrap.dedent("""
+                                Currently TorchANI training with weight decay can not reproduce the training
+                                result of NeuroChem with the same training setup. If you really want to use
+                                weight decay, consider smaller rates and and make sure you do enough validation
+                                to check if you get expected result."""))
+                            self.warned = True
                         if layer['l2norm'] == 1:
                             self.parameters.append({
                                 'params': [module.weight],