Merge pull request #44 from choosewhatulike/master

add MLP decoder

fastNLP/modules/decoder/MLP.py

@@ -0,0 +1,56 @@
+import torch
+import torch.nn as nn
+
+class MLP(nn.Module):
+    def __init__(self, size_layer, num_class=2, activation='relu'):
+        """Multilayer Perceptrons as a decoder
+
+        Args:
+            size_layer: list of int, define the size of MLP layers
+            num_class: int, num of class in output, should be 2 or the last layer's size
+            activation: str or function, the activation function for hidden layers
+        """
+        super(MLP, self).__init__()
+        self.hiddens = nn.ModuleList()
+        self.output = None
+        for i in range(1, len(size_layer)):
+            if i + 1 == len(size_layer):
+                self.output = nn.Linear(size_layer[i-1], size_layer[i])
+            else:
+                self.hiddens.append(nn.Linear(size_layer[i-1], size_layer[i]))
+
+        if num_class == 2:
+            self.out_active = nn.LogSigmoid()
+        elif num_class == size_layer[-1]:
+            self.out_active = nn.LogSoftmax(dim=1)
+        else:
+            raise ValueError("should set output num_class correctly: {}".format(num_class))
+        
+        actives = {
+            'relu': nn.ReLU(),
+            'tanh': nn.Tanh()
+        }
+        if activation in actives:
+            self.hidden_active = actives[activation]
+        elif isinstance(activation, callable):
+            self.hidden_active = activation
+        else:
+            raise ValueError("should set activation correctly: {}".format(activation))
+
+    def forward(self, x):
+        for layer in self.hiddens:
+            x = self.hidden_active(layer(x))
+        x = self.out_active(self.output(x))
+        return x
+
+
+
+if __name__ == '__main__':
+    net1 = MLP([5,10,5])
+    net2 = MLP([5,10,5], 5)
+    for net in [net1, net2]:
+        x = torch.randn(5, 5)
+        y = net(x)
+        print(x)
+        print(y)
+