I want to construct sobolev network for 3D input regression
In TensorFlow, the gradients of neural network model can be computed using tf.gradient like:
dfdx,dfdy,dfdz = tf.gradients(pred,[x,y,z]) Let M be a torch neural network with 5 layers. If X is a set of (x,y,z) (3dim data) and M.forward(X) is a 1 dim output
How can I compute like gradient of M.forward(X) with respect to X? Something like:
tf.gradient(M.forward(X),X) If you want to compute gradient of this function for example
y_i = 5*(x_i + 1)² Create tensor of size 2x1 filled with 1's that requires gradient
x = torch.ones(2, requires_grad=True) Simple linear equation with x tensor created
y = 5 * (x + 1) ** 2 Let take o as multiple dimension equation
o = 1/2 *sum(y_i) in python
o = (1/2) * torch.sum(y) you can compute grad with
o.backward() x.grad you can get more information here https://www.deeplearningwizard.com/deep_learning/practical_pytorch/pytorch_gradients/
