# Loss function depending on the derivative of a neural network with respect to the input in tensorflow

I have a neural network $$x \mapsto f(x, \theta)$$, and I can access predictions in my code with out = model(X). Imagine that I have a loss function $$l(x,y) = (y-\frac{\partial f(x, \theta)}{\partial x}\cdot C)^2$$ where $$C$$ is a constant vector. I think that I can approximate the derivative and write the code like this

eps = 0.001
pred = ... # compute the gradient with a formula like (f(x(1+eps))-f(x(1-eps)))/2x*eps
# and make the dot product with C
loss = tf.math.squared_difference(y, pred) # loss for one data point



Is it possible to compute the gradient directly with respect to the input such that the output is still a function of $$\theta$$ and can be optimized by tensorflow? If so, what should I write in the line pred = ...?

Thanks

• Have you considered using PyTorch? Because this can be easily done using the torch.autograd package in PyTorch. Aug 29, 2020 at 18:59
• Thanks, I will look at it Aug 29, 2020 at 19:27