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Feed_Forward.py

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+from torch import nn
+import config
+class PoswiseFeedForwardNet(nn.Module):
+    def __init__(self):
+        super(PoswiseFeedForwardNet, self).__init__()
+        self.fc = nn.Sequential(
+            nn.Linear(config.input_dim, config.d_ff1, bias=config.bias),
+            nn.ReLU(),
+            nn.Linear(config.d_ff1, config.input_dim, bias=config.bias))
+
+    def forward(self, inputs):  # inputs: [batch_size, seq_len, d_model]
+        residual = inputs
+
+        output = self.fc(inputs)
+
+        return nn.LayerNorm(config.input_dim).to(config.device)(output + residual)  # [batch_size, seq_len, d_model]