Merge pull request yunjey#7 from DingKe/master

make RNN definition independent of global variables

Author: yunjey

tutorials/06 - Recurrent Neural Network/main-gpu.py

@@ -38,13 +38,15 @@
 class RNN(nn.Module):
     def __init__(self, input_size, hidden_size, num_layers, num_classes):
         super(RNN, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
         self.fc = nn.Linear(hidden_size, num_classes)
 
     def forward(self, x):
         # Set initial states 
-        h0 = Variable(torch.zeros(num_layers, x.size(0), hidden_size).cuda()) 
-        c0 = Variable(torch.zeros(num_layers, x.size(0), hidden_size).cuda())
+        h0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size).cuda()) 
+        c0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size).cuda())
 
         # Forward propagate RNN
         out, _ = self.lstm(x, (h0, c0))  
@@ -90,4 +92,4 @@ def forward(self, x):
 print('Test Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total)) 
 
 # Save the Model
-torch.save(rnn, 'rnn.pkl')
+torch.save(rnn, 'rnn.pkl')

tutorials/06 - Recurrent Neural Network/main.py

@@ -38,13 +38,15 @@
 class RNN(nn.Module):
     def __init__(self, input_size, hidden_size, num_layers, num_classes):
         super(RNN, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
         self.fc = nn.Linear(hidden_size, num_classes)
 
     def forward(self, x):
         # Set initial states 
-        h0 = Variable(torch.zeros(num_layers, x.size(0), hidden_size)) 
-        c0 = Variable(torch.zeros(num_layers, x.size(0), hidden_size))
+        h0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size)) 
+        c0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size))
 
         # Forward propagate RNN
         out, _ = self.lstm(x, (h0, c0))  

tutorials/07 - Bidirectional Recurrent Neural Network/main-gpu.py

@@ -38,14 +38,16 @@
 class BiRNN(nn.Module):
     def __init__(self, input_size, hidden_size, num_layers, num_classes):
         super(BiRNN, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, 
                             batch_first=True, bidirectional=True)
         self.fc = nn.Linear(hidden_size*2, num_classes)  # 2 for bidirection 
 
     def forward(self, x):
         # Set initial states
-        h0 = Variable(torch.zeros(num_layers*2, x.size(0), hidden_size)).cuda() # 2 for bidirection 
-        c0 = Variable(torch.zeros(num_layers*2, x.size(0), hidden_size)).cuda()
+        h0 = Variable(torch.zeros(self.num_layers*2, x.size(0), self.hidden_size)).cuda() # 2 for bidirection 
+        c0 = Variable(torch.zeros(self.num_layers*2, x.size(0), self.hidden_size)).cuda()
 
         # Forward propagate RNN
         out, _ = self.lstm(x, (h0, c0))
@@ -91,4 +93,4 @@ def forward(self, x):
 print('Test Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total)) 
 
 # Save the Model
-torch.save(rnn, 'rnn.pkl')
+torch.save(rnn, 'rnn.pkl')

tutorials/07 - Bidirectional Recurrent Neural Network/main.py

@@ -38,14 +38,16 @@
 class BiRNN(nn.Module):
     def __init__(self, input_size, hidden_size, num_layers, num_classes):
         super(BiRNN, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, 
                             batch_first=True, bidirectional=True)
         self.fc = nn.Linear(hidden_size*2, num_classes)  # 2 for bidirection 
 
     def forward(self, x):
         # Set initial states
-        h0 = Variable(torch.zeros(num_layers*2, x.size(0), hidden_size)) # 2 for bidirection 
-        c0 = Variable(torch.zeros(num_layers*2, x.size(0), hidden_size))
+        h0 = Variable(torch.zeros(self.num_layers*2, x.size(0), self.hidden_size)) # 2 for bidirection 
+        c0 = Variable(torch.zeros(self.num_layers*2, x.size(0), self.hidden_size))
 
         # Forward propagate RNN
         out, _ = self.lstm(x, (h0, c0))
@@ -91,4 +93,4 @@ def forward(self, x):
 print('Test Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total)) 
 
 # Save the Model
-torch.save(rnn, 'rnn.pkl')
+torch.save(rnn, 'rnn.pkl')