Whitespace

theano/compile/tests/test_pfunc.py

@@ -945,4 +945,3 @@ class Test_aliasing_rules(unittest.TestCase):
 if __name__ == '__main__':
     theano.config.mode = 'FAST_COMPILE'
     Test_pfunc().test_default_scalar_container()
-

theano/compile/tests/test_shared.py

@@ -42,7 +42,7 @@ class Test_SharedVariable(unittest.TestCase):
         # generic can hold anything even when strict=True
 
         u = shared('asdf', strict=False)
-        v = shared('asdf', strict=True) 
+        v = shared('asdf', strict=True)
 
         u.set_value(88)
         v.set_value(88)
@@ -102,7 +102,7 @@ class Test_SharedVariable(unittest.TestCase):
         assert numpy.all(u.get_value() == [3,4])
 
         # check that assignments of nonsense fail
-        try: 
+        try:
             u.set_value('adsf')
             assert 0
         except ValueError:
@@ -124,7 +124,7 @@ class Test_SharedVariable(unittest.TestCase):
         b = shared(numpy.int32(7), strict=True)
         assert b.type == theano.tensor.iscalar
         self.failUnlessRaises(TypeError, f, b, 8.23)
-        
+
         b = shared(numpy.int16(7), strict=True)
         assert b.type == theano.tensor.wscalar
         self.failUnlessRaises(TypeError, f, b, 8.23)
@@ -235,7 +235,7 @@ class Test_SharedVariable(unittest.TestCase):
         assert b.type == theano.tensor.dscalar
         f(b,8)
         assert b.get_value()==8
-        
+
         b = shared(numpy.float32(7.234), allow_downcast=True)
         assert b.type == theano.tensor.fscalar
         f(b,8)
@@ -307,6 +307,3 @@ class Test_SharedVariable(unittest.TestCase):
 
         c = shared(numpy.zeros((5,5), dtype='float32'), allow_downcast=True)
         self.failUnlessRaises(TypeError, f, b, numpy.random.rand(5,5))
-
-
-

theano/sandbox/cuda/tests/test_blas.py

@@ -133,7 +133,7 @@ if 0:
                 dmatrix4 = tensor.TensorType("float32", (False, False, False, False))
                 b = dmatrix4()
                 f = pfunc([b], [a(b)], mode=mode_with_gpu)
-                
+
                 bval = numpy.arange(0,d0*d1).reshape(1,1,d0,d1)
                 r = f(bval)[0]
     #            print bval, bval.shape, border
@@ -143,7 +143,7 @@ if 0:
 def test_downsample():
     import random
     shps = [ (1, 1, 1, 12),
-            (1, 1, 2, 2), 
+            (1, 1, 2, 2),
             (1, 1, 1, 1),
             (1,1,4,4),
             (1, 1, 10, 11),
@@ -181,7 +181,7 @@ def test_downsample():
                 assert any([isinstance(node.op, tcn.blas.GpuDownsampleFactorMax) for node in
                             f.maker.env.toposort()])
                 assert numpy.allclose(f(),f2())
-                
+
                 g = pfunc([], tensor.grad(ds_op(tensor.as_tensor_variable(a)).sum(),a), mode=mode_with_gpu)
                 g2 = pfunc([], tensor.grad(ds_op(tensor.as_tensor_variable(a)).sum(),a), mode=mode_without_gpu)
                 assert any([isinstance(node.op, tcn.blas.GpuDownsampleFactorMaxGrad)
@@ -190,6 +190,3 @@ def test_downsample():
 
                 #We already check that the gpu version return the same value as the gpu version
                 #for GpuDownsampleFactorMaxGrad. So no need to call verify_grad here.
-
-
-

theano/tests/test_scan.py

@@ -61,7 +61,7 @@ class multiple_outputs_numeric_grad:
         # something else ( a random state ? ) with which we shouldn't really
         # mess up
         if not ndarray_mask:
-                ndarray_mask = [True for x in pt ]
+            ndarray_mask = [True for x in pt ]
 
         dtype_eps = multiple_outputs_numeric_grad.type_eps['float64']
 
@@ -70,7 +70,7 @@ class multiple_outputs_numeric_grad:
                 pt[i] = numpy.array(p)
                 _eps = multiple_outputs_numeric_grad.type_eps[str(pt[i].dtype)]
                 if _eps > dtype_eps:
-                        dtype_eps = _eps
+                    dtype_eps = _eps
 
 
         # Compute clean output:
@@ -1050,7 +1050,7 @@ class T_Scan(unittest.TestCase):
         f1      = theano.tensor.dscalar('f1')
 
         def scanStep(prev, seq, f1):
-           return prev + f1 * seq
+            return prev + f1 * seq
 
         scanned, _ = theano.scan(fn = scanStep, \
                                 sequences    = [seq], \
@@ -1074,8 +1074,8 @@ class T_Scan(unittest.TestCase):
         inpt    = theano.tensor.matrix('inpt')
 
         def one_step(x_t, h_tm1, W):
-          expr = T.dot(h_tm1, W) + x_t
-          return expr
+            expr = T.dot(h_tm1, W) + x_t
+            return expr
 
         expr, _ = theano.scan(
           fn=one_step,
@@ -1114,7 +1114,7 @@ class T_Scan(unittest.TestCase):
 
         floatX = theano.config.floatX
         def one_step( h_tm1):
-          return h_tm1 + numpy.asarray(1., dtype=floatX)
+            return h_tm1 + numpy.asarray(1., dtype=floatX)
 
         h, _ = theano.scan(
           fn=one_step,
@@ -1131,4 +1131,3 @@ class T_Scan(unittest.TestCase):
 
 if __name__ == '__main__':
     unittest.main()
-