Merge pull request #1860 from daemonmaker/issue1780

theano/scalar/basic.py

@@ -1712,6 +1712,7 @@ class Pow(BinaryScalarOp):
         first_part = gz * y * x ** (y - 1)
 
         second_part = gz * log(x) * x ** y
+        second_part = switch(eq(x, 0), 0, second_part)
 
         return (first_part, second_part)
 

theano/tensor/tests/test_basic.py

@@ -922,23 +922,36 @@ _grad_broadcast_pow_normal = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand
                                   #complex3 = (rand(2,3),randcomplex(2,3)),
                                   #empty1 = (numpy.asarray([]), numpy.asarray([1])),
                                   #empty2 = (numpy.asarray([0]), numpy.asarray([])),
+                                  x_eq_zero = (
+                                      numpy.asarray([0.], dtype=config.floatX),
+                                      numpy.asarray([2.], dtype=config.floatX)
+                                  ),  # Test for issue 1780
                                   )
 #empty2 case is not supported by numpy.
 _good_broadcast_pow_normal_float_pow = copy(_good_broadcast_pow_normal_float)
 del _good_broadcast_pow_normal_float_pow["empty2"]
 
+# Disable NAN checking for pow operator per issue #1780
+m = copy(theano.compile.get_default_mode())
+m.check_isfinite = False
+
 PowTester = makeBroadcastTester(
     op=pow,
     expected=lambda x, y: check_floatX((x, y), x ** y),
     good=_good_broadcast_pow_normal_float,
     grad=_grad_broadcast_pow_normal,
-        name='Pow')
+    name='Pow',
+    mode=m
+)
 
-PowInplaceTester = makeBroadcastTester(op=inplace.pow_inplace,
+PowInplaceTester = makeBroadcastTester(
+    op=inplace.pow_inplace,
     expected=lambda x, y: x ** y,
-                                       good = _good_broadcast_pow_normal_float_pow,
-                                       grad = _grad_broadcast_pow_normal,
-                                       inplace = True)
+    good=_good_broadcast_pow_normal_float_pow,
+    grad=_grad_broadcast_pow_normal,
+    inplace=True,
+    mode=m
+)
 
 #Those are corner case when rounding. Their is many rounding algo.
 #c round() fct and numpy round are not the same!