Global change to new stack interface.

theano/sandbox/cuda/opt.py

@@ -828,7 +828,7 @@ def local_gpu_careduce(node):
                             new_in_shp.append(x_shape[i])
 
                     new_greduce = GpuCAReduce(new_mask, scalar_op)
-                    reshaped_x = x.reshape(tensor.stack(*new_in_shp))
+                    reshaped_x = x.reshape(tensor.stack(new_in_shp))
                     gpu_reshaped_x = as_cuda_ndarray_variable(reshaped_x)
                     reshaped_gpu_inputs = [gpu_reshaped_x]
                     if new_greduce.supports_c_code(reshaped_gpu_inputs):
@@ -837,7 +837,7 @@ def local_gpu_careduce(node):
 
                         if reduce_reshaped_x.ndim != out.ndim:
                             rval = reduce_reshaped_x.reshape(
-                                tensor.stack(*shape_of[out]))
+                                tensor.stack(shape_of[out]))
                         else:
                             rval = reduce_reshaped_x
                     else:

theano/sandbox/gpuarray/opt.py

@@ -572,7 +572,7 @@ def local_gpua_careduce(node):
                 dtype=getattr(node.op, 'dtype', None),
                 acc_dtype=getattr(node.op, 'acc_dtype', None))
 
-            reshaped_x = x.reshape(tensor.stack(*new_in_shp))
+            reshaped_x = x.reshape(tensor.stack(new_in_shp))
             gpu_reshaped_x = gpu_from_host(reshaped_x)
             gvar = greduce(gpu_reshaped_x)
             # We need to have the make node called, otherwise the mask can
@@ -584,7 +584,7 @@ def local_gpua_careduce(node):
 
                 if reduce_reshaped_x.ndim != node.outputs[0].ndim:
                     unreshaped_reduce = reduce_reshaped_x.reshape(
-                        tensor.stack(*shape_of[node.outputs[0]]))
+                        tensor.stack(shape_of[node.outputs[0]]))
                 else:
                     unreshaped_reduce = reduce_reshaped_x
                 return [unreshaped_reduce]

theano/sparse/basic.py

@@ -3013,8 +3013,8 @@ class HStack(gof.op.Op):
 
         split = tensor.Split(len(inputs))(gz, 1,
                                           tensor.stack(
-                                              *[x.shape[1]
-                                                for x in inputs]))
+                                              [x.shape[1]
+                                               for x in inputs]))
         if not isinstance(split, list):
             split = [split]
 
@@ -3094,8 +3094,8 @@ class VStack(HStack):
 
         split = tensor.Split(len(inputs))(gz, 0,
                                           tensor.stack(
-                                              *[x.shape[0]
-                                                for x in inputs]))
+                                              [x.shape[0]
+                                               for x in inputs]))
         if not isinstance(split, list):
             split = [split]
 

theano/tensor/basic.py

@@ -185,7 +185,7 @@ def as_tensor_variable(x, name=None, ndim=None):
     if isinstance(x, (tuple, list)) and python_any(isinstance(xi, Variable)
                                                    for xi in x):
         try:
-            return stack(*x)
+            return stack(x)
         except (TypeError, ValueError):
             pass
 
@@ -1672,7 +1672,7 @@ def smallest(*args):
         a, b = args
         return switch(a < b, a, b)
     else:
-        return min(stack(*args), axis=0)
+        return min(stack(args), axis=0)
 
 
 @constructor
@@ -1687,7 +1687,7 @@ def largest(*args):
         a, b = args
         return switch(a > b, a, b)
     else:
-        return max(stack(*args), axis=0)
+        return max(stack(args), axis=0)
 
 
 ##########################
@@ -3806,8 +3806,8 @@ class Join(Op):
         if 'float' in out_dtype or 'complex' in out_dtype:
             # assume that this is differentiable
             split = Split(len(tensors))
-            split_gz = split(gz, axis, stack(*[shape(x)[axis]
-                                               for x in tensors]))
+            split_gz = split(gz, axis, stack([shape(x)[axis]
+                                              for x in tensors]))
             # If there is only one split, it might not be in a list.
             if not isinstance(split_gz, list):
                 split_gz = [split_gz]
@@ -5691,7 +5691,7 @@ def stacklists(arg):
 
     """
     if isinstance(arg, (tuple, list)):
-        return stack(*list(map(stacklists, arg)))
+        return stack(list(map(stacklists, arg)))
     else:
         return arg
 

theano/tensor/fourier.py

@@ -83,7 +83,7 @@ class Fourier(gof.Op):
                          list(shape_a[axis.data + 1:]))
         else:
             l = len(shape_a)
-            shape_a = tensor.stack(*shape_a)
+            shape_a = tensor.stack(shape_a)
             out_shape = tensor.concatenate((shape_a[0: axis], [n],
                                             shape_a[axis + 1:]))
             n_splits = [1] * l

theano/tensor/raw_random.py

@@ -365,7 +365,7 @@ def _infer_ndim_bcast(ndim, shape, *args):
         if len(pre_v_shape) == 0:
             v_shape = tensor.constant([], dtype='int32')
         else:
-            v_shape = tensor.stack(*pre_v_shape)
+            v_shape = tensor.stack(pre_v_shape)
 
     elif shape is None:
         # The number of drawn samples will be determined automatically,

theano/tensor/tests/test_basic.py

@@ -3465,6 +3465,7 @@ class T_Join_and_Split(unittest.TestCase):
         with warnings.catch_warnings(record=True) as w:
             s = stack([a, b])
             s = stack([a, b], 1)
+            s = stack([a, b], axis=1)
             s = stack(tensors=[a, b])
             s = stack(tensors=[a, b], axis=1)
             assert not w