Fixed rnn hidden shape bugs

12_2_hello_rnn.py

@@ -44,14 +44,14 @@ def forward(self, hidden, x):
 
         # Propagate input through RNN
         # Input: (batch, seq_len, input_size)
-        # hidden: (batch, num_layers * num_directions, hidden_size)
+        # hidden: (num_layers * num_directions, batch, hidden_size)
         out, hidden = self.rnn(x, hidden)
         return hidden, out.view(-1, num_classes)
 
     def init_hidden(self):
         # Initialize hidden and cell states
-        # (batch, num_layers * num_directions, hidden_size) for batch_first=True
-        return Variable(torch.zeros(batch_size, num_layers, hidden_size))
+        # (num_layers * num_directions, batch, hidden_size)
+        return Variable(torch.zeros(num_layers, batch_size, hidden_size))
 
 
 # Instantiate RNN model

12_3_hello_rnn_seq.py

@@ -46,16 +46,16 @@ def __init__(self, num_classes, input_size, hidden_size, num_layers):
 
     def forward(self, x):
         # Initialize hidden and cell states
-        # (batch, num_layers * num_directions, hidden_size) for batch_first=True
+        # (num_layers * num_directions, batch, hidden_size) for batch_first=True
         h_0 = Variable(torch.zeros(
-            x.size(0), self.num_layers, self.hidden_size))
+            self.num_layers, x.size(0), self.hidden_size))
 
         # Reshape input
         x.view(x.size(0), self.sequence_length, self.input_size)
 
         # Propagate input through RNN
         # Input: (batch, seq_len, input_size)
-        # h_0: (batch, num_layers * num_directions, hidden_size)
+        # h_0: (num_layers * num_directions, batch, hidden_size)
 
         out, _ = self.rnn(x, h_0)
         return out.view(-1, num_classes)

12_4_hello_rnn_emb.py

@@ -36,16 +36,16 @@ def __init__(self):
 
     def forward(self, x):
         # Initialize hidden and cell states
-        # (batch, num_layers * num_directions, hidden_size) for batch_first=True
+        # (num_layers * num_directions, batch, hidden_size)
         h_0 = Variable(torch.zeros(
-            x.size(0), num_layers, hidden_size))
+            self.num_layers, x.size(0), self.hidden_size))
 
         emb = self.embedding(x)
         emb = emb.view(batch_size, sequence_length, -1)
 
         # Propagate embedding through RNN
         # Input: (batch, seq_len, embedding_size)
-        # h_0: (batch, num_layers * num_directions, hidden_size)
+        # h_0: (num_layers * num_directions, batch, hidden_size)
         out, _ = self.rnn(emb, h_0)
         return self.fc(out.view(-1, num_classes))