Merge pull request #1627 from nouiz/cleanup

theano/scan_module/tests/test_scan.py

@@ -3660,8 +3660,8 @@ class T_Scan(unittest.TestCase):
         #Bug reported by Bitton Tenessi
 
         W_flat = tensor.fvector(name='W')
-        W_flat.tag.test_value=numpy.ones((8,), dtype=numpy.float32)
-        W = W_flat.reshape((2,2,2))
+        W_flat.tag.test_value = numpy.ones((8,), dtype=numpy.float32)
+        W = W_flat.reshape((2, 2, 2))
 
         def loss_outer(i_outer, sum_outer, W):
 
@@ -3669,20 +3669,22 @@ class T_Scan(unittest.TestCase):
 
                 return sum_inner + (W**2).sum().sum().sum()
 
-            result_inner, _ = theano.scan(fn=loss_inner,
-               outputs_info = tensor.as_tensor_variable(
-                   numpy.asarray(0, dtype=numpy.float32)),
-               sequences=tensor.arange(1, dtype='int32'),
-               non_sequences=[W],
-               )
+            result_inner, _ = theano.scan(
+                fn=loss_inner,
+                outputs_info=tensor.as_tensor_variable(
+                    numpy.asarray(0, dtype=numpy.float32)),
+                sequences=tensor.arange(1, dtype='int32'),
+                non_sequences=[W],
+            )
             return sum_outer + result_inner[-1]
 
-        result_outer, _ = theano.scan(fn=loss_outer,
-               outputs_info = tensor.as_tensor_variable(
-                   numpy.asarray(0, dtype=numpy.float32)),
-               sequences=tensor.arange(1, dtype='int32'),
-               non_sequences=[W],
-               )
+        result_outer, _ = theano.scan(
+            fn=loss_outer,
+            outputs_info=tensor.as_tensor_variable(
+                numpy.asarray(0, dtype=numpy.float32)),
+            sequences=tensor.arange(1, dtype='int32'),
+            non_sequences=[W],
+        )
 
         cost = result_outer[-1]
         H = theano.gradient.hessian(cost, W_flat)

theano/tensor/var.py

@@ -608,7 +608,7 @@ class TensorConstantSignature(tuple):
             # The following 2 lines are needede as in Python 3.3 with NumPy
             # 1.7.1, numpy.ndarray and numpy.memmap aren't hashable.
             if type(self._sum) is numpy.memmap:
-                self._sum = numpy.asarray(self._sum).sum()
+                self._sum = numpy.asarray(self._sum).item()
             if self.has_nan and self.no_nan.mask.all():
                 # In this case the sum is not properly computed by numpy.
                 self._sum = 0