added a warning related to the model name (needs to be a error)

pytorch_widedeep/initializers.py

 import torch
+import warnings
 
 from torch import nn
 from fnmatch import fnmatch
 
 from .wdtypes import *
 
+
 class Initializer(object):
 
     def __call__(self, model:TorchModel):
@@ -23,12 +25,13 @@ class MultipleInitializers(object):
 			try:
 				child.apply(self._initializers[model_name])
 			except KeyError:
-				raise Exception('Model name has to be one of: {}'.format(str([child.__class__.__name__.lower() for child in children])))
-
+			    warnings.warn(
+			    	'Model name has to be one of: {}'.format(str([child.__class__.__name__.lower()
+			    		for child in children])), ValueError)
 
 class Normal(Initializer):
 
-	def __init__(self, mean=0.0, std=0.02, bias=False, pattern='*', pattern_in=True):
+	def __init__(self, mean=0.0, std=0.02, bias=False, pattern='*'):
 	    self.mean = mean
 	    self.std = std
 	    self.bias = bias
@@ -37,10 +40,10 @@ class Normal(Initializer):
 
 	def __call__(self, submodel:TorchModel):
 		for n,p in submodel.named_parameters():
-			if fnmatch(n, self.pattern) and pattern_in:
+			if fnmatch(n, self.pattern):
 				if self.bias and ('bias' in n):
 					nn.init.normal_(p, mean=self.mean, std=self.std)
-				elif ('bias' in n):
+				elif 'bias' in n:
 					continue
 				elif p.requires_grad:
 					nn.init.normal_(p, mean=self.mean, std=self.std)
@@ -48,7 +51,7 @@ class Normal(Initializer):
 
 class Uniform(Initializer):
 
-	def __init__(self, a=0, b=1, bias=False, pattern='*', pattern_in=True):
+	def __init__(self, a=0, b=1, bias=False, pattern='*'):
 	    self.a = a
 	    self.b = b
 	    self.bias = bias
@@ -57,10 +60,10 @@ class Uniform(Initializer):
 
 	def __call__(self, submodel:TorchModel):
 		for n,p in submodel.named_parameters():
-			if fnmatch(n, self.pattern) and pattern_in:
+			if fnmatch(n, self.pattern):
 				if self.bias and ('bias' in n):
 					nn.init.uniform_(p, a=self.a, b=self.b)
-				elif ('bias' in n):
+				elif 'bias' in n:
 					continue
 				elif p.requires_grad:
 					nn.init.uniform_(p, a=self.a, b=self.b)
@@ -76,12 +79,12 @@ class ConstantInitializer(Initializer):
 
 	def __call__(self, submodel:TorchModel):
 		for n,p in submodel.named_parameters():
-			if fnmatch(n, self.pattern) and pattern_in:
+			if fnmatch(n, self.pattern):
 				if self.bias and ('bias' in n):
 					nn.init.constant_(p, val=self.value)
 				elif ('bias' in n):
 					continue
-				elif:
+				elif p.requires_grad:
 					p.requires_grad: nn.init.constant_(p, val=self.value)
 
 
@@ -94,7 +97,7 @@ class XavierUniform(Initializer):
 
 	def __call__(self, submodel:TorchModel):
 		for n,p in submodel.named_parameters():
-			if fnmatch(n, self.pattern) and pattern_in:
+			if fnmatch(n, self.pattern):
 				if 'bias' in n: nn.init.constant_(p, val=0)
 				elif p.requires_grad: nn.init.xavier_uniform_(p, gain=self.gain)
 
@@ -108,7 +111,7 @@ class XavierNormal(Initializer):
 
 	def __call__(self, submodel:TorchModel):
 		for n,p in submodel.named_parameters():
-			if fnmatch(n, self.pattern) and pattern_in:
+			if fnmatch(n, self.pattern):
 				if 'bias' in n: nn.init.constant_(p, val=0)
 				elif p.requires_grad: nn.init.xavier_normal_(p, gain=self.gain)
 
@@ -123,7 +126,7 @@ class KaimingUniform(Initializer):
 
 	def __call__(self, submodel:TorchModel):
 		for n,p in submodel.named_parameters():
-			if fnmatch(n, self.pattern) and pattern_in:
+			if fnmatch(n, self.pattern):
 				if 'bias' in n: nn.init.constant_(p, val=0)
 				elif p.requires_grad: nn.init.kaiming_uniform_(p, a=self.a, mode=self.mode)
 
@@ -138,7 +141,7 @@ class KaimingNormal(Initializer):
 
 	def __call__(self, submodel:TorchModel):
 		for n,p in submodel.named_parameters():
-			if fnmatch(n, self.pattern) and pattern_in:
+			if fnmatch(n, self.pattern):
 				if 'bias' in n: nn.init.constant_(p, val=0)
 				elif p.requires_grad: nn.init.kaiming_normal_(p, a=self.a, mode=self.mode)
 
@@ -152,6 +155,6 @@ class Orthogonal(Initializer):
 
 	def __call__(self, submodel:TorchModel):
 		for n,p in submodel.named_parameters():
-			if fnmatch(n, self.pattern) and pattern_in:
+			if fnmatch(n, self.pattern):
 				if 'bias' in n: nn.init.normal_(p, val=0)
 				elif p.requires_grad: nn.init.orthogonal_(p, gain=self.gain)
\ No newline at end of file