LSTM.py gelöscht
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bb3d1629f6
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0e63e58705
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LSTM.py
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LSTM.py
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader
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from sklearn.metrics import mean_squared_error, r2_score
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from torch.optim.lr_scheduler import ReduceLROnPlateau
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import time
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from tqdm import tqdm
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from Datasets import GloveDataset as HumorDataset
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import Datasets
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import dataset_helper
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class LSTMNetwork(nn.Module):
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def __init__(self, input_dim, hidden_dim, num_layers, output_dim, dropout=0.3):
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super(LSTMNetwork, self).__init__()
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self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, dropout=dropout, batch_first=True)
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self.fc = nn.Linear(hidden_dim, output_dim)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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lstm_out, _ = self.lstm(x)
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# print(lstm_out)
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return self.fc(self.dropout(lstm_out))
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def compute_metrics(predictions, labels):
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mse = mean_squared_error(labels, predictions)
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r2 = r2_score(labels, predictions)
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return mse, r2
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def train_model(model, train_loader, val_loader, test_loader, epochs=10, device='cuda'):
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criterion = nn.MSELoss()
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=3, verbose=True)
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best_val_loss = float('inf')
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best_test_r2 = -float('inf')
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patience = 3
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counter = 0
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history = {'train_loss': [], 'val_loss': [], 'test_r2': [], 'test_mse': []}
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for epoch in range(epochs):
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model.train()
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total_loss = 0
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start_time = time.time()
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train_preds, train_labels = [], []
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for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs}", ncols=100):
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optimizer.zero_grad()
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inputs = batch[0].float().to(device)#batch['input_ids'].to(device)
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labels = batch[1].float().to(device)#batch['labels'].to(device)
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outputs = model(inputs)
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loss = criterion(outputs.squeeze(), labels)
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loss.backward()
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optimizer.step()
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total_loss += loss.item()
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train_preds.extend(outputs.squeeze().detach().cpu().numpy())
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train_labels.extend(labels.cpu().numpy())
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avg_train_loss = total_loss / len(train_loader)
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model.eval()
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val_loss = 0
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val_preds, val_labels = [], []
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with torch.no_grad():
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for batch in val_loader:
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inputs = batch[0].float().to(device)#batch['input_ids'].to(device)
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labels = batch[1].float().to(device)#batch['labels'].to(device)
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outputs = model(inputs)
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val_loss += criterion(outputs.squeeze(), labels).item()
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val_preds.extend(outputs.squeeze().cpu().numpy())
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val_labels.extend(labels.cpu().numpy())
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avg_val_loss = val_loss / len(val_loader)
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test_preds, test_labels = [], []
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with torch.no_grad():
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for batch in test_loader:
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inputs = batch[0].float().to(device)#batch['input_ids'].to(device)
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labels = batch[1].float().to(device)#batch['labels'].to(device)
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outputs = model(inputs)
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test_preds.extend(outputs.squeeze().cpu().numpy())
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test_labels.extend(labels.cpu().numpy())
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test_mse, test_r2 = compute_metrics(test_preds, test_labels)
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history['train_loss'].append(avg_train_loss)
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history['val_loss'].append(avg_val_loss)
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history['test_r2'].append(test_r2)
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history['test_mse'].append(test_mse)
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scheduler.step(avg_val_loss)
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epoch_time = time.time() - start_time
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print(f'Epoch {epoch+1}/{epochs} | Time: {epoch_time:.2f}s')
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print(f'Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}')
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print(f'Test MSE: {test_mse:.4f} | Test R2: {test_r2:.4f}\n')
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if test_r2 > best_test_r2:
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best_test_r2 = test_r2
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torch.save(model.state_dict(), "best_lstm_model.pth")
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print(f"New best model saved (R2: {test_r2:.4f})")
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if avg_val_loss < best_val_loss:
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best_val_loss = avg_val_loss
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counter = 0
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else:
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counter += 1
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if counter >= patience:
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print("Early stopping triggered!")
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break
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return history
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if __name__ == "__main__":
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input_dim = 128
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hidden_dim = 1024
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num_layers = 2
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output_dim = 1
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dropout = 0.2
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batch_size = 256
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epochs = 5
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DATA_PATH = "data/hack.csv"
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GLOVE_PATH = "data/glove.6b.100d.txt"
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EMBEDDING_DIM = 100
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TEST_SIZE = 0.1
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VAL_SIZE = 0.1
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params = {"max_len":128}
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# data_path = 'data/embedded_padded'
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embedding_matrix, word_index, vocab_size, d_model = dataset_helper.get_embedding_matrix(
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gloVe_path=GLOVE_PATH, emb_len=EMBEDDING_DIM)
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X, y = dataset_helper.load_preprocess_data(path_data=DATA_PATH, verbose=True)
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# Aufteilen der Daten
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data_split = dataset_helper.split_data(X, y, test_size=TEST_SIZE, val_size=VAL_SIZE)
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# Dataset und DataLoader
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train_dataset = Datasets.GloveDataset(data_split['train']['X'], data_split['train']['y'], word_index, max_len=params["max_len"])
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val_dataset = Datasets.GloveDataset(data_split['val']['X'], data_split['val']['y'], word_index, max_len=params["max_len"])
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test_dataset = Datasets.GloveDataset(data_split['test']['X'], data_split['test']['y'], word_index, max_len=params["max_len"])
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
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test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = LSTMNetwork(input_dim=input_dim, hidden_dim=hidden_dim, num_layers=num_layers, output_dim=output_dim, dropout=dropout).to(device)
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history = train_model(model, train_loader, val_loader, test_loader, epochs=epochs, device=device)
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