use numpy.allclose instead of numpy.all in test.

theano/tensor/tests/test_basic.py

@@ -2471,7 +2471,7 @@ class test_tensordot(unittest.TestCase):
         f2 = inplace_func([avec,bmat],c)
         aval = self.rand(5);
         bval = self.rand(8,5);
-        self.failUnless(numpy.all(numpy.tensordot(aval,bval,axes) == \
+        self.failUnless(numpy.allclose(numpy.tensordot(aval,bval,axes),
                                        f2(aval,bval)))
         utt.verify_grad(TensorDot(axes), [aval,bval])
 
@@ -2482,7 +2482,7 @@ class test_tensordot(unittest.TestCase):
         f3 = inplace_func([amat,bmat],c)
         aval = self.rand(4,7);
         bval = self.rand(7,9);
-        self.failUnless(numpy.all(numpy.tensordot(aval,bval,axes) == \
+        self.failUnless(numpy.allclose(numpy.tensordot(aval,bval,axes),
                                        f3(aval,bval)))
         utt.verify_grad(TensorDot(axes), [aval,bval])
 
@@ -2493,7 +2493,7 @@ class test_tensordot(unittest.TestCase):
         f4 = inplace_func([atens,bmat],c)
         aval = self.rand(1,2,3,4);
         bval = self.rand(2,3);
-        self.failUnless(numpy.all(numpy.tensordot(aval,bval,axes) == \
+        self.failUnless(numpy.allclose(numpy.tensordot(aval,bval,axes),
                                        f4(aval,bval)))
         utt.verify_grad(TensorDot(axes), [aval,bval])
 
@@ -2505,14 +2505,14 @@ class test_tensordot(unittest.TestCase):
         f5 = inplace_func([atens,btens],c)
         aval = self.rand(4,3,5,2);
         bval = self.rand(3,4,2);
-        self.failUnless(numpy.all(numpy.tensordot(aval,bval,axes) == \
+        self.failUnless(numpy.allclose(numpy.tensordot(aval,bval,axes),
                                        f5(aval,bval)))
         utt.verify_grad(TensorDot(axes), [aval,bval])
         
         axes = (axes[1],axes[0])
         c = tensordot(axes)(btens, atens)
         f6 = inplace_func([btens,atens],c)
-        self.failUnless(numpy.all(numpy.tensordot(bval,aval,axes) == \
+        self.failUnless(numpy.allclose(numpy.tensordot(bval,aval,axes),
                                        f6(bval,aval)))
         utt.verify_grad(TensorDot(axes), [bval,aval])
 
@@ -2554,7 +2554,7 @@ class test_tensordot(unittest.TestCase):
         f3 = inplace_func([amat,bmat],c)
         aval = self.rand(4,7);
         bval = self.rand(7,9);
-        self.failUnless(numpy.all(numpy.tensordot(aval,bval,axes) == \
+        self.failUnless(numpy.allclose(numpy.tensordot(aval,bval,axes),
                                        f3(aval,bval)))
         utt.verify_grad(TensorDot(axes), [aval,bval])
 
@@ -2567,7 +2567,7 @@ class test_tensordot(unittest.TestCase):
         bval = numpy.random.rand(5,3)
         c = tensordot(axes)(amat, bmat)
         f3 = inplace_func([amat,bmat],c)
-        self.failUnless(numpy.all(numpy.tensordot(aval,bval,axes) == \
+        self.failUnless(numpy.allclose(numpy.tensordot(aval,bval,axes),
                                        f3(aval,bval)))
         utt.verify_grad(TensorDot(axes), [aval,bval])
 
@@ -2579,7 +2579,7 @@ class test_tensordot(unittest.TestCase):
         bval = self.rand(4,5,3)
         c = tensordot(axes)(amat, bmat)
         f3 = inplace_func([amat,bmat],c)
-        self.failUnless(numpy.all(numpy.tensordot(aval,bval,axes) == \
+        self.failUnless(numpy.allclose(numpy.tensordot(aval,bval,axes),
                                        f3(aval,bval)))
         utt.verify_grad(TensorDot(axes), [aval,bval])
 
@@ -2592,7 +2592,7 @@ class test_tensordot(unittest.TestCase):
         bval = self.rand(5,4)
         c = tensordot(axes)(amat, bmat)
         f3 = inplace_func([amat,bmat],c)
-        self.failUnless(numpy.all(numpy.tensordot(aval,bval,axes) == \
+        self.failUnless(numpy.allclose(numpy.tensordot(aval,bval,axes),
                                        f3(aval,bval)))
         utt.verify_grad(TensorDot(axes), [aval,bval])
 
@@ -2610,8 +2610,8 @@ class test_tensordot(unittest.TestCase):
         f2 = inplace_func([amat,bmat,gzmat],tensordot_grad(((1,),(0,)))(amat, bmat, gzmat))
         o1=f1(aval,bval,gzval)
         o2=f2(aval,bval,gzval)
-        self.failUnless(numpy.all(o1[0]==o2[0]))
-        self.failUnless(numpy.all(o1[1]==o2[1]))
+        self.failUnless(numpy.allclose(o1[0],o2[0]))
+        self.failUnless(numpy.allclose(o1[1],o2[1]))
 
 def test_smallest_stack():
     sx, sy = dscalar(), dscalar()