dedupe creating RNN

Author: spro

char-rnn-classification/train.py

@@ -6,7 +6,10 @@
 import math
 
 n_hidden = 128
-rnn = RNN(n_letters, n_hidden, n_categories)
+n_epochs = 100000
+print_every = 5000
+plot_every = 1000
+learning_rate = 0.005 # If you set this too high, it might explode. If too low, it might not learn
 
 def categoryFromOutput(output):
     top_n, top_i = output.data.topk(1) # Tensor out of Variable with .data
@@ -16,46 +19,31 @@ def categoryFromOutput(output):
 def randomChoice(l):
     return l[random.randint(0, len(l) - 1)]
 
-def randomTrainingPair():                                                                                                               
+def randomTrainingPair():
     category = randomChoice(all_categories)
     line = randomChoice(category_lines[category])
     category_tensor = Variable(torch.LongTensor([all_categories.index(category)]))
     line_tensor = Variable(lineToTensor(line))
     return category, line, category_tensor, line_tensor
 
+rnn = RNN(n_letters, n_hidden, n_categories)
+optimizer = torch.optim.SGD(rnn.parameters(), lr=learning_rate)
 criterion = nn.NLLLoss()
 
-learning_rate = 0.005 # If you set this too high, it might explode. If too low, it might not learn
-
 def train(category_tensor, line_tensor):
     hidden = rnn.initHidden()
-
-    rnn.zero_grad()
+    optimizer.zero_grad()
 
     for i in range(line_tensor.size()[0]):
         output, hidden = rnn(line_tensor[i], hidden)
 
     loss = criterion(output, category_tensor)
     loss.backward()
 
-    # Add parameters' gradients to their values, multiplied by learning rate
-    for p in rnn.parameters():
-        p.data.add_(-learning_rate, p.grad.data)
+    optimizer.step()
 
     return output, loss.data[0]
 
-n_epochs = 100000
-print_every = 5000
-plot_every = 1000
-
-rnn = RNN(n_letters, n_hidden, n_categories)
-
-n_epochs = 100000
-print_every = 5000
-plot_every = 1000
-
-rnn = RNN(n_letters, n_hidden, n_categories)
-
 # Keep track of losses for plotting
 current_loss = 0
 all_losses = []