Create rnn.py

code/rnn.py

@@ -0,0 +1,33 @@
+import torch.nn as nn
+
+class RNN(nn.Module):
+    def __init__(self, n_classes):
+        super().__init__()
+        self.hid_fc = nn.Linear(185, 128)
+        self.out_fc = nn.Linear(185, n_classes)
+        self.softmax = nn.LogSoftmax()
+
+    def forward(self, inputs, hidden):
+        inputs = inputs.view(1,-1)
+        combined = torch.cat([inputs, hidden], dim=1)
+        hid_out = self.hid_fc(combined)
+        out = self.out_fc(combined)
+        out = self.softmax(out)
+        return out, hid_out
+
+def train(model, inputs, targets):
+    for i in range(len(inputs)):
+        target = Variable([targets[i]])
+        name = inputs[i]
+        hidden = Variable(torch.zeros(1,128))
+        model.zero_grad()
+
+        for char in name:
+            input_ = Variable(torch.FloatTensor(char))
+            out, hidden = model(input_, hidden)
+
+        loss = criterion(out, target)
+        loss.backward()
+
+        for p in model.parameters():
+            p.data.add_(-lr, p.grad.data)