Optimized new code and improved the tests.

theano/sandbox/cuda/basic_ops.py

@@ -2314,12 +2314,12 @@ class GpuReshape(tensor.Reshape, GpuOp):
                              ' has incorrect length %i'
                              ', should be %i' % (len(shp), self.ndim), shp)
 
-        m1_idx = -1
         if shp.prod() != x.size:
             # We need to do check here to raise the same error as NumPy.
             # We should make pygpu do the same.
             ss = 1
             nb_m1 = 0
+            m1_idx = -1
             for idx, i in enumerate(shp):
                 if i == -1:
                     nb_m1 += 1
@@ -2331,12 +2331,13 @@ class GpuReshape(tensor.Reshape, GpuOp):
             elif nb_m1 == 1:
                 if (x.size % ss) != 0:
                     raise ValueError("When using -1 in new shape, the computed new shape must be an multiple of the original shape.")
+                shp_new = numpy.copy(shp)
+                shp_new[m1_idx] = x.size/ss
+                shp = shp_new
+
             else:
                 raise ValueError("total size of new array must be unchanged")
 
-        if m1_idx != -1:
-            shp[m1_idx] = x.size/(-1*shp.prod())
-
         out[0] = x.reshape(tuple(shp))
 
 

theano/sandbox/cuda/tests/test_basic_ops.py

@@ -339,19 +339,22 @@ def test_reshape():
 
     # Test for appropriate handling of -1 indices
     x = T.tensor3('x')
-    f_reshp = theano.function([x], x.reshape((-1, 1, 1)), mode=mode_with_gpu)
-    y = f_reshp(
-        numpy.array([[[1, 0], [0, 1]], [[0, 1], [1, 0]]], dtype='float32')
-    )
+    dim = T.scalar('dim_val', dtype='int32')
+    reshp_val = numpy.array([[[1, 0], [0, 1]], [[0, 1], [1, 0]]], dtype='float32')
+    f_reshp = theano.function([x, dim], x.reshape((dim, 1, 1)), mode=mode_with_gpu)
+    y = f_reshp(reshp_val, -1)
     assert y.shape == (8, 1, 1)
 
-    assert_raises(ValueError,
-                  f_reshp=theano.function(
-                      [x],
-                      x.reshape((-1, -1, 1)),
-                      mode=mode_with_gpu
-                  )
+    f_reshp=theano.function(
+        [x, dim],
+        x.reshape((dim, dim, 1)),
+        mode=mode_with_gpu
     )
+    try:
+        f_reshp(reshp_val, 4)
+        raise('Only one -1 is accepted in the new shape')
+    except ValueError:
+        pass
 
 
 def test_elemwise_empty():