def gradcheck_naive(f, x):
""" Gradient check for a function f.
Arguments:
f -- a function that takes a single argument and outputs the
cost and its gradients
x -- the point (numpy array) to check the gradient at
"""
rndstate = random.getstate()
random.setstate(rndstate)
fx, grad = f(x) # Evaluate function value at original point
h = 1e-4 # Do not change this!
# Iterate over all indexes in x
it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
while not it.finished:
ix = it.multi_index
# Try modifying x[ix] with h defined above to compute
# numerical gradients. Make sure you call random.setstate(rndstate)
# before calling f(x) each time. This will make it possible
# to test cost functions with built in randomness later.
### YOUR CODE HERE:
old_value = x[ix]
random.setstate(rndstate)
x[ix] = old_value + h
fxh_left, _ = f(x)
random.setstate(rndstate)
x[ix] = old_value - h
fxh_right, _ = f(x)
numgrad = (fxh_left - fxh_right) / (2 * h)
x[ix] = old_value
### END YOUR CODE
# Compare gradients
reldiff = abs(numgrad - grad[ix]) / max(1, abs(numgrad), abs(grad[ix]))
if reldiff > 1e-5:
print("Gradient check failed.")
print("First gradient error found at index %s" % str(ix))
print("Your gradient: %f \t Numerical gradient: %f" % (
grad[ix], numgrad))
return
it.iternext() # Step to next dimension
print("Gradient check passed!")
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