out, _ = self.rnn(self.embedding(x), (h0, c0)) out = self.fc(out[:, -1, :]) return out George Estregan Bold Movies Link
# Initialize the model, optimizer, and loss function model = LanguageModel(vocab_size=10000, embedding_dim=128, hidden_dim=256, output_dim=10000) optimizer = optim.Adam(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() Tadpolexstudio 24 10 25 Madison Wilde — And Kay L Verified
def forward(self, x): h0 = torch.zeros(1, x.size(0), self.hidden_dim).to(x.device) c0 = torch.zeros(1, x.size(0), self.hidden_dim).to(x.device)
import torch import torch.nn as nn import torch.optim as optim
# Train the model for epoch in range(10): optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() print(f'Epoch epoch+1, Loss: loss.item()') This is a basic example, and there are many ways to improve it, such as using a more sophisticated architecture, increasing the size of the model, or using pre-trained models as a starting point.