Update pages/19_RNN_Shakespeare.py

pages/19_RNN_Shakespeare.py

@@ -22,7 +22,7 @@ class LSTMModel(nn.Module):
 def generate_text(model, start_str, length, char_to_int, int_to_char, num_layers, hidden_size):
     model.eval()
     input_seq = [char_to_int[c] for c in start_str]
-    input_seq = torch.tensor(input_seq, dtype=torch.float32).unsqueeze(0).unsqueeze(-1)
+    input_seq = torch.tensor(input_seq, dtype=torch.long).unsqueeze(0)
     h = (torch.zeros(num_layers, 1, hidden_size), torch.zeros(num_layers, 1, hidden_size))
     generated_text = start_str
 
@@ -31,7 +31,7 @@ def generate_text(model, start_str, length, char_to_int, int_to_char, num_layers
         _, predicted = torch.max(output, 1)
         predicted_char = int_to_char[predicted.item()]
         generated_text += predicted_char
-        input_seq = torch.tensor([[char_to_int[predicted_char]]], dtype=torch.float32).unsqueeze(0).unsqueeze(-1)
+        input_seq = torch.tensor([[char_to_int[predicted_char]]], dtype=torch.long)
 
     return generated_text
 
@@ -70,7 +70,7 @@ if st.button("Train and Generate"):
         if len(dataX) == 0:
             st.error("Not enough data to create input-output pairs. Please provide more text data.")
         else:
-            X = np.reshape(dataX, (len(dataX), seq_length, 1))
+            X = np.reshape(dataX, (len(dataX), seq_length))
             X = X / float(len(chars))
             Y = np.array(dataY)
 
@@ -90,7 +90,7 @@ if st.button("Train and Generate"):
                 h = (torch.zeros(num_layers, 1, hidden_size), torch.zeros(num_layers, 1, hidden_size))
                 epoch_loss = 0
                 for i in range(len(dataX)):
-                    inputs = X_tensor[i].unsqueeze(0)
+                    inputs = X_tensor[i].unsqueeze(0).unsqueeze(-1)
                     targets = Y_tensor[i].unsqueeze(0)
 
                     # Forward pass