joddiy commented on a change in pull request #484: SINGA -475 add Div operator
implementation to singa
URL: https://github.com/apache/incubator-singa/pull/484#discussion_r310486283
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File path: test/python/test_operation.py
##########
@@ -34,6 +34,35 @@
singa.Gaussian(0.0, 1.0, dy)
+def eval_numerical_gradient_b(f, x, y, reverse = False):
+ h = 0.00001
+ grad = np.zeros(x.shape)
+ if not reverse:
+ fx = f(x, y)
+ it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
+ while not it.finished:
+ ix = it.multi_index
+ old_value = x[ix]
+ x[ix] = old_value + h # increment by h
+ fxh = f(x,y) # evaluate f(x + h)
+ x[ix] = old_value # restore to previous value (very important!)
+ grad[ix] = (fxh - fx) / h # the slope
+ it.iternext() # step to next dimension
+ return grad
+ else:
+ fx = f(x, y)
+ it = np.nditer(y, flags=['multi_index'], op_flags=['readwrite'])
+ while not it.finished:
+ iy = it.multi_index
+ old_value = y[iy]
+ y[iy] = old_value + h # increment by h
+ fyh = f(x,y) # evaluate f(y + h)
+ y[iy] = old_value # restore to previous value (very important!)
+ grad[iy] = (fyh - fx) / h
+ it.iternext()
+ return grad
+
Review comment:
can we optimize this part as:
```
def eval_numerical_gradient_b(f, x, y, reverse = False):
h = 0.00001
grad = np.zeros(x.shape)
t = y if reverse else x
fx = f(x, y)
it = np.nditer(t, flags=['multi_index'], op_flags=['readwrite'])
while not it.finished:
_it = it.multi_index
old_value = t[_it]
t[_it] = old_value + h # increment by h
fth = f(x, y) # evaluate f(x + h)
t[_it] = old_value # restore to previous value (very important!)
grad[_it] = (fth - fx) / h # the slope
it.iternext() # step to next dimension
return grad
```
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