Merge pull request graykode#34 from lvyilin/master

fix graykode#33

2-1.TextCNN/TextCNN-Torch.py

@@ -46,18 +46,19 @@ def __init__(self):
         self.W = nn.Parameter(torch.empty(vocab_size, embedding_size).uniform_(-1, 1)).type(dtype)
         self.Weight = nn.Parameter(torch.empty(self.num_filters_total, num_classes).uniform_(-1, 1)).type(dtype)
         self.Bias = nn.Parameter(0.1 * torch.ones([num_classes])).type(dtype)
+        self.filter_list = nn.ModuleList(
+            [nn.Conv2d(1, num_filters, (size, embedding_size), bias=True) for size in filter_sizes])
 
     def forward(self, X):
         embedded_chars = self.W[X] # [batch_size, sequence_length, sequence_length]
         embedded_chars = embedded_chars.unsqueeze(1) # add channel(=1) [batch, channel(=1), sequence_length, embedding_size]
 
         pooled_outputs = []
-        for filter_size in filter_sizes:
+        for i, conv in enumerate(self.filter_list):
             # conv : [input_channel(=1), output_channel(=3), (filter_height, filter_width), bias_option]
-            conv = nn.Conv2d(1, num_filters, (filter_size, embedding_size), bias=True)(embedded_chars)
-            h = F.relu(conv)
+            h = F.relu(conv(embedded_chars))
             # mp : ((filter_height, filter_width))
-            mp = nn.MaxPool2d((sequence_length - filter_size + 1, 1))
+            mp = nn.MaxPool2d((sequence_length - filter_sizes[i] + 1, 1))
             # pooled : [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3)]
             pooled = mp(h).permute(0, 3, 2, 1)
             pooled_outputs.append(pooled)