-
Notifications
You must be signed in to change notification settings - Fork 56
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Questions for computing of derivatives #1
Comments
Hi, The way you suggested won't work because tf.gradient() cumulates all the partial derivatives for a particular input dimension. Ideally tf.gradient(tf.gradient(Y, A), A) should be the hessian of size size(tf.gradient(Y, A)) x size(A). However, you would get a vector of size(A). Hope that clears things up? Get back if you have any more concernts. |
I cannot understand your code for computing derivatives:
My questions are,
second derivative:
tf.gradient(tf.gradient(Y, A), A)
triple derivative:
tf.gradient(tf.gradient(tf.gradient(Y, A), A), A)
Can you help me?
The text was updated successfully, but these errors were encountered: