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From GitBox <...@apache.org>
Subject [GitHub] [incubator-singa] joddiy commented on a change in pull request #496: SINGA-474 Mean operator
Date Mon, 05 Aug 2019 08:26:35 GMT
joddiy commented on a change in pull request #496: SINGA-474 Mean operator
URL: https://github.com/apache/incubator-singa/pull/496#discussion_r310489393
 
 

 ##########
 File path: test/python/test_operation.py
 ##########
 @@ -322,6 +335,48 @@ def test_LeakyRelu(self):
         np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT)
         self.check_shape(dx.shape(), (3, 2))
 
+    def test_Mean_gpu(self):
+        x0 = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
+        x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3, 2).astype(np.float32)
+        y = (x0+x1)/2
+        lossf =lambda x,y:np.sum((x+y)/2)
+        grad=eval_numerical_gradient(lossf,x0,x1)
+        grad1=eval_numerical_gradient(lossf,x1,x0)
 
 Review comment:
   I guess you can use this function:
   ```
   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
   ```
   if the reverse if False, this function gets the grads of x based on f(x,y), if reverse
if true, it gets the grads of y still based on f(x,y)

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