Hello all, I’m trying to calculate the differential of the output with respect to one of the column in the dataset. I am not able to produce the differential. It is a regression problem and the network has two outputs. There are more than 2000 columns and only 1 column is required in the calculation of differential.

**Error produced is as follows:**

RuntimeError: One of the differentiated Tensors appears to not have been used in the graph. Set allow_unused=True if this is the desired behavior.

**Model :**

class Model2(nn.Module):

`def __init__(self, input_dim): super(Model2, self).__init__() self.layer1 = nn.Linear(input_dim, 512) self.layer2 = nn.Linear(512, 256) self.layer3 = nn.Linear(256, 128) self.layer4 = nn.Linear(128, 64) self.layer5 = nn.Linear(64, 2) def forward(self, x): x = F.relu(self.layer1(x)) x = F.relu(self.layer2(x)) x = F.relu(self.layer3(x)) x = F.relu(self.layer4(x)) x = self.layer5(x) return x`

**Training**:

EPOCHS = 1000

fourth_loss_train = np.zeros((EPOCHS,))

fourth_loss_val = np.zeros((EPOCHS,))

fourth_train_mape = np.zeros((EPOCHS,))

fourth_val_mape = np.zeros((EPOCHS,))x1 = X_train[:,-2].requires_grad_(True)

x2 = 1 - x1 #.requires_grad_(True)for epoch in tqdm.trange(EPOCHS, position=0, leave=True):

`model_PINN_5.train() y_pred = model_PINN_5(X_train) loss1 = loss_fn(y_pred, y_train) d_lngamma1_x1 = torch.autograd.grad(y_pred[:,0], x1, torch.ones_like(y_pred[:,0]), create_graph=True)[0] d_lngamma2_x2 = torch.autograd.grad(y_pred[:,1], x2, torch.ones_like(y_pred[:,1]), create_graph=True)[0] physics = x1@d_lngamma1_x1 # - x2@d_lngamma2_x2 loss2 = (1e-4)*torch.mean(physics**2) loss = loss1 + loss2 fourth_loss_train[epoch] = loss.item() # Zero gradients loss.backward() optimizer.step() optimizer.zero_grad() # Calculate train accuracy fourth_train_mape[epoch] = mean_absolute_percentage_error(y_train, y_pred) with torch.no_grad(): model_PINN_5.eval() y_pred = model_PINN_5(X_val) val_loss = loss_fn(y_pred, y_val) fourth_loss_val[epoch] = val_loss fourth_val_mape[epoch] = mean_absolute_percentage_error(y_val, y_pred)`