failed assertion `Completed handler provided after commit call'.
how to clear this error any. when i run with cpu i am getting storage error so i tried with GPU.
partial code
#PositionalEncoding
class PositionalEncoding(nn.Module):
def init(self, d_model, max_len, dropout_prob=0.1):
super(PositionalEncoding, self).init()
self.dropout = nn.Dropout(p=dropout_prob)
# Create positional encoding matrix
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
# Pad div_term with zeros if necessary
div_term_padded = torch.zeros(d_model)
div_term_padded[:div_term.size(0)] = div_term
pe[:, 0::2] = torch.sin(position * div_term_padded[0::2])
pe[:, 1::2] = torch.cos(position * div_term_padded[1::2])
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
#transformermodel class
class TransformerModel(nn.Module):
def init(self, input_size, hidden_size, num_layers, d_model, num_heads, dropout_prob, output_size, device, max_len):
super(TransformerModel, self).init()
self.device = device
self.hidden_size = hidden_size
self.d_model = d_model
self.num_heads = num_heads
#self.embedding = nn.Embedding(input_size, d_model).to(device)
self.embedding = nn.Linear(input_size, d_model).to(device)
self.pos_encoder = PositionalEncoding(d_model, max_len, dropout_prob).to(device)
self.transformer_encoder_layer = nn.TransformerEncoderLayer(d_model, num_heads, hidden_size, dropout_prob).to(device)
self.transformer_encoder = nn.TransformerEncoder(self.transformer_encoder_layer, num_layers).to(device)
self.decoder = nn.Linear(d_model, output_size).to(device)
self.to(device) # Ensure the model is on the correct device
def forward(self, x):
#x = x.long()
x = x.transpose(0, 1) # Transpose the input tensor to match the expected shape for the transformer
x = x.squeeze() # Remove the extra dimension from the input tensor
x = self.embedding(x) # Apply the input embedding
x = self.pos_encoder(x) # Add positional encoding
x = self.transformer_encoder(x) # Apply the transformer encoder
x = self.decoder(x[:, -1, :]) # Decode the last time step's output to get the final prediction
return x
#train transformer model class
def train_transformer_model(train_X_scaled, train_y, input_size, d_model, hidden_size, num_layers, output_size, learning_rate, num_epochs, num_heads, dropout_prob, device, n_accumulation_steps=32):
train_X_tensor = torch.from_numpy(train_X_scaled).float().to(device)
train_y_tensor = torch.from_numpy(train_y).float().unsqueeze(1).to(device)
# Create the dataset and DataLoader
train_data = TensorDataset(train_X_tensor, train_y_tensor)
train_loader = DataLoader(train_data, batch_size=8, shuffle=True)
# Compute the maximum length of the input sequences
max_len = train_X_tensor.size(1)
# Create the model
model = TransformerModel(input_size, hidden_size, num_layers, d_model, num_heads, dropout_prob, output_size, device, max_len).to(device)
q = 0.5
criterion = lambda y_pred, y_true: quantile_loss(q, y_true, y_pred)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(1, num_epochs + 1):
model.train()
print(f"Transformer inputs shape: {train_X_tensor.shape}, targets shape: {train_y_tensor.shape}")
for epoch in range(1, num_epochs + 1):
model.train()
print(f"transformer Epoch {epoch}/{num_epochs}")
for i, (batch_X, batch_y) in enumerate(train_loader):
batch_X = batch_X.to(device)
print("transformer batch_X shape:", batch_X.shape)
batch_y = batch_y.to(device)
print("transformer batch_Y shape:", batch_y.shape)
optimizer.zero_grad()
batch_X = batch_X.transpose(0, 1)
train_pred = model(batch_X.squeeze(0)).to(device)
print("train_pred=",train_pred)
loss = criterion(train_pred, batch_y).to(device)
loss.backward()
# Gradient accumulation
if (i + 1) % n_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
print(f"transformer Epoch {epoch}/{num_epochs}, Step {i+1}/{len(train_loader)}, Loss: {loss.item():.6f}")
return model
kapsystk.
