Merge pull request #2225 from caglar/fix_grad_clip

theano/scalar/basic.py

@@ -1792,7 +1792,7 @@ class Clip(ScalarOp):
 
     def grad(self, (x, mn, mx), (gz, )):
         assert gz.type not in complex_types
-        gx = ((x > mn) & (x < mx)) * gz
+        gx = ((x >= mn) & (x <= mx)) * gz
         gmn = (x < mn) * gz
         gmx = (x > mx) * gz
 

theano/scalar/tests/test_basic.py

@@ -14,13 +14,14 @@ import unittest
 import theano
 from theano.gof import FunctionGraph
 from theano import gof
+from theano.tests import unittest_tools as utt
 
 from theano.scalar.basic import (floats, float32, float64,
                                  ints, int8, int32, complex64,
                                  ComplexError, IntDiv, TrueDiv,
-                                 Composite, add, div_proxy,
+                                 Composite, add, div_proxy, clip,
                                  and_, eq, neq, invert, mul)
-
+import numpy
 
 def inputs():
     return floats('xyz')
@@ -56,6 +57,43 @@ class test_ScalarOps(unittest.TestCase):
                     ):
             self.assertTrue(fn(a,b) == a%b, (a,))
 
+
+    def test_clip_grad(self):
+        #This is testing for the issue #633
+        x, y = floats('xy')
+        a = theano.tensor.clip(x, y, x)
+        g = theano.gradient.grad(a, x)
+        fn = gof.DualLinker().accept(FunctionGraph([x, y], [g])).make_function()
+
+        # Test the other way around as well
+        a2 = theano.tensor.clip(x, x, y)
+        g2 = theano.gradient.grad(a2, x)
+        fn2 = gof.DualLinker().accept(FunctionGraph([x, y], [g2])).make_function()
+
+        # Test for the equal case too .
+        a3 = theano.tensor.clip(x, x, x)
+        g3 = theano.gradient.grad(a3, x)
+        fn3 = gof.DualLinker().accept(FunctionGraph([x], [g3])).make_function()
+
+        rng = numpy.random.RandomState(utt.fetch_seed())
+
+        ntests = 50
+        for i in xrange(ntests):
+            xval = rng.rand(1)
+            #To ensure that the min < x .
+            yval_mn = rng.rand(1) - 1.0
+
+            #To ensure that the max > x.
+            yval_mx = rng.rand(1) + 1.0
+
+            aval = fn(xval, yval_mn)
+            aval2 = fn2(xval, yval_mx)
+            aval3 = fn3(xval)
+            self.assertTrue(aval == 1.)
+            self.assertTrue(aval2 == 1.)
+            self.assertTrue(aval3 == 1.)
+
+
 class test_composite(unittest.TestCase):
 
     def test_straightforward(self):