Dropout

network.py

@@ -291,7 +291,7 @@ class SecondaryNet(Net):
 
 
 class MetaCell(nn.Module):
-    def __init__(self, name, interface):
+    def __init__(self, name, interface, weight_interface=5, weight_hidden_size=2, weight_output_size=1):
         super().__init__()
         self.name = name
         self.interface = interface
@@ -342,7 +342,8 @@ class MetaCell(nn.Module):
 
 
 class MetaLayer(nn.Module):
-    def __init__(self, name, interface=4, width=4, residual_skip=True):
+    def __init__(self, name, interface=4, width=4, residual_skip=True,
+                 weight_interface=5, weight_hidden_size=2, weight_output_size=1):
         super().__init__()
         self.residual_skip = residual_skip
         self.name = name
@@ -351,7 +352,9 @@ class MetaLayer(nn.Module):
 
         self.meta_cell_list = nn.ModuleList()
         self.meta_cell_list.extend([MetaCell(name=f'{self.name}_C{cell_idx}',
-                                             interface=interface
+                                             interface=interface,
+                                             weight_interface=weight_interface, weight_hidden_size=weight_hidden_size,
+                                             weight_output_size=weight_output_size,
                                              ) for cell_idx in range(self.width)]
                                    )
 
@@ -371,26 +374,42 @@ class MetaLayer(nn.Module):
 
 class MetaNet(nn.Module):
 
-    def __init__(self, interface=4, depth=3, width=4, out=1, activation=None, residual_skip=True):
+    def __init__(self, interface=4, depth=3, width=4, out=1, activation=None, residual_skip=True, dropout=0,
+                 weight_interface=5, weight_hidden_size=2, weight_output_size=1,):
         super().__init__()
+        self.dropout = dropout
         self.activation = activation
         self.out = out
         self.interface = interface
         self.width = width
         self.depth = depth
+        self.weight_interface = weight_interface
+        self.weight_hidden_size = weight_hidden_size
+        self.weight_output_size = weight_output_size
 
         self._meta_layer_list = nn.ModuleList()
         self._meta_layer_list.append(MetaLayer(name=f'L{0}',
                                                interface=self.interface,
-                                               width=self.width, residual_skip=residual_skip)
+                                               width=self.width, residual_skip=residual_skip,
+                                               weight_interface=weight_interface,
+                                               weight_hidden_size=weight_hidden_size,
+                                               weight_output_size=weight_output_size)
                                      )
         self._meta_layer_list.extend([MetaLayer(name=f'L{layer_idx + 1}',
-                                                interface=self.width, width=self.width, residual_skip=residual_skip
+                                                interface=self.width, width=self.width, residual_skip=residual_skip,
+                                                weight_interface=weight_interface,
+                                                weight_hidden_size=weight_hidden_size,
+                                                weight_output_size=weight_output_size,
                                                 ) for layer_idx in range(self.depth - 2)]
                                      )
         self._meta_layer_list.append(MetaLayer(name=f'L{len(self._meta_layer_list)}',
-                                               interface=self.width, width=self.out, residual_skip=residual_skip)
+                                               interface=self.width, width=self.out, residual_skip=residual_skip,
+                                               weight_interface=weight_interface,
+                                               weight_hidden_size=weight_hidden_size,
+                                               weight_output_size=weight_output_size,
+                                               )
                                      )
+        self.dropout_layer = nn.Dropout(p=self.dropout)
 
     def replace_with_zero(self, ident_key):
         replaced_particles = 0
@@ -406,6 +425,8 @@ class MetaNet(nn.Module):
     def forward(self, x):
         tensor = x
         for meta_layer in self._meta_layer_list:
+            if self.dropout:
+                tensor = self.dropout_layer(tensor)
             tensor = meta_layer(tensor)
         return tensor
 
@@ -423,6 +444,10 @@ class MetaNet(nn.Module):
             losses.append(F.mse_loss(output, target_data))
         return torch.hstack(losses).sum(dim=-1, keepdim=True)
 
+    @property
+    def hyperparams(self):
+        return {key: val for key, val in self.__dict__.items() if not key.startswith('_')}
+
 
 class MetaNetCompareBaseline(nn.Module):
 
@@ -437,7 +462,7 @@ class MetaNetCompareBaseline(nn.Module):
         self._meta_layer_list = nn.ModuleList()
 
         self._meta_layer_list.append(nn.Linear(self.interface, self.width, bias=False))
-        self._meta_layer_list.extend([ nn.Linear(self.width, self.width, bias=False) for _ in range(self.depth - 2)])
+        self._meta_layer_list.extend([nn.Linear(self.width, self.width, bias=False) for _ in range(self.depth - 2)])
         self._meta_layer_list.append(nn.Linear(self.width, self.out, bias=False))
 
     def forward(self, x):