Created
August 2, 2016 23:32
-
-
Save zzag/9cec9136dffd3b27f5ed62bc758b7794 to your computer and use it in GitHub Desktop.
Gradient checking
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import numpy as np | |
def eval_gradient_naive(f, x, dx=1e-4): | |
grad = np.zeros_like(x) | |
it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite']) | |
while not it.finished: | |
ix = it.multi_index | |
# save old value | |
oldval = x[ix] | |
# evaluate gradient | |
x[ix] = oldval - dx | |
fx1, _ = f(x) | |
x[ix] = oldval + dx | |
fx2, _ = f(x) | |
numgrad = (fx2 - fx1) / (2*dx) | |
# restore old value | |
x[ix] = oldval | |
grad[ix] = numgrad | |
it.iternext() | |
return grad | |
f = lambda x: (np.sum(x**2 + 3*x), 2*x + 3) # cost function | |
x = np.random.randn(3, 3) | |
num_grad = eval_gradient_naive(f, x) | |
_, grad = f(x) | |
print 'Numerical gradient:' | |
print num_grad | |
print 'Analytical gradient:' | |
print grad | |
print 'Difference: %f' % np.linalg.norm(num_grad - grad, ord='fro') |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment