Is it possible to do the integration of neural network

1

I want to do the integration over one input of the neural network. Is it possible?

2

Could you elaborate more? What do you want to integrate exactly?

3

Thank you for your reply.

def fun(x):
#print("calling this function\t ",x_val)
d_x=x[:, 0]
in_Q2=[d_x[i].item() for i in range(len(d_x))]
in_xy=[[in_Q2[i],x_val] for i in range(len(in_Q2))]
in_xy=torch.tensor(in_xy,dtype=torch.float32).clone().detach()
output_net=model(in_xy)
output_net.requires_grad_(True)
#print(f"Out put of the model:\t {list(model.parameters())} ",)

            return output_net

pred = torch.tensor([mc.integrate(fun,dim=1,N=100,integration_domain=[[0, 1]],backend=“torch”,).item()],requires_grad=True).float()

loss = loss_fn(pred, val_yb)
opt.zero_grad()
loss.backward()
opt.step()

sorry for the late reply. I have done the integration of the neural network but now the problem is that not updating the model parameters(weights and biases)