Merged

README.txt

@@ -27,7 +27,7 @@ Theano (current directory) is the distribution directory.
         * scalar depends upon core
         * tensor depends upon scalar
         * sparse depends upon tensor
-        * sandbox can depends on everything else
+        * sandbox can depend on everything else
     * Theano/examples are copies of the example on the wiki
     * Theano/benchmark, Theano/bin and Theano/examples are in the distribution,
       but not in the python package

doc/install.txt

@@ -554,14 +554,15 @@ use a compilation directory located somewhere else:
       [global]
       base_compiledir=path_to_a_directory_without_such_characters
 
-  You also need to add in the configuration file those lines:
+You also need to add in the configuration file those lines (make sure this
+is the correct Python installation path):
 
     .. code-block:: cfg
 
       [cuda]
       nvccflags=-LC:\Python26\libs
 
-  Then
+Then
   
   1) Install CUDA driver (32-bit on 32-bit Windows, idem for 64-bit).
   

theano/tensor/basic.py

@@ -33,6 +33,9 @@ def _info(*msg):
 def _warn(*msg):
     _logger.warn(' '.join(msg))
 
+#This is needed as we will hide it later
+python_complex=complex
+
 def check_equal_numpy(x, y):
     """
     Returns True iff x and y are equal (checks the dtype and
@@ -388,6 +391,20 @@ def get_constant_value(v):
                 ret = get_constant_value(ret)
                 #join can cast implicitly its input in some case.
                 return theano._asarray(ret, dtype=v.type.dtype)
+            if (v.owner.inputs[0].owner and
+                isinstance(v.owner.inputs[0].owner.op,
+                           theano.tensor.opt.MakeVector) and
+                # MakeVector normally accept only scalar as input.
+                # We put this check in case there is change in the future
+                all(var.ndim==0 for var in v.owner.inputs[0].owner.inputs)):
+
+                # The index list 'idx_list' should have length one
+                # since joining scalar variables results in a 1D vector.
+                assert len(v.owner.op.idx_list) == 1
+                ret = v.owner.inputs[0].owner.inputs[v.owner.op.idx_list[0]]
+                ret = get_constant_value(ret)
+                #MakeVector can cast implicitly its input in some case.
+                return theano._asarray(ret, dtype=v.type.dtype)
     raise TypeError(v)
 
 
@@ -3269,11 +3286,18 @@ def stack(*tensors):
         raise Exception('theano.tensor.stack(*tensors) must have at least one parameter')
     # If all tensors are scalars of the same type, call make_vector.
     # It makes the graph simpler, by not adding DimShuffles and Rebroadcasts
-    if numpy.all([isinstance(t, Variable) and\
-                  isinstance(t.type, TensorType) and\
-                  t.ndim==0 and t.type==tensors[0].type\
+    if isinstance(tensors[0], (numpy.number, float, int, python_complex)):
+        tensors=list(tensors)
+        tensors[0]=as_tensor_variable(tensors[0])
+    if numpy.all([isinstance(t, (numpy.number, float, int, python_complex))#in case their is direct int
+                  or (isinstance(t, Variable) and
+                      isinstance(t.type, TensorType) and
+                      t.ndim==0 and
+                      t.type.__class__==tensors[0].type.__class__)
                   for t in tensors]):
-        return theano.tensor.opt.MakeVector(scal.upcast(*[i.dtype for i in tensors]))(*tensors)
+        tensors = map(as_tensor_variable,tensors)#in case their is direct int
+        dtype = scal.upcast(*[i.dtype for i in tensors])
+        return theano.tensor.opt.MakeVector(dtype)(*tensors)
     return join(0, *[shape_padleft(t, 1) for t in tensors])
 
 @constructor

theano/tensor/tests/test_basic.py

@@ -1552,6 +1552,36 @@ class T_Join_and_Split(unittest.TestCase):
         assert len([n for n in e if isinstance(n, Join)]) == 0
         assert f.maker.env.outputs[0].dtype == config.floatX
 
+    def test_stack_scalar_make_vector_dtype(self):
+        '''Test that calling stack() on scalars instantiates MakeVector,
+        event when the scalar don't have the same dtype.'''
+        a = tensor.iscalar('a')
+        b = tensor.lscalar('b')
+        s = stack(a, b, a, b)
+        f = function([a,b], s)
+        val = f(1,2)
+        self.failUnless(numpy.all(val == [1,2,1,2]))
+        e = f.maker.env.toposort()
+        assert len([n for n in e if isinstance(n.op,opt.MakeVector)]) > 0
+        assert len([n for n in e if isinstance(n, Join)]) == 0
+        assert f.maker.env.outputs[0].dtype == 'int64'
+
+    def test_stack_scalar_make_vector_constant(self):
+        '''Test that calling stack() on scalars instantiates MakeVector,
+        event when the scalar are simple int type.'''
+        a = tensor.iscalar('a')
+        b = tensor.lscalar('b')
+        #test when the constant is the first element.
+        #The first element is used in a special way
+        s = stack(10,a,b, numpy.int8(3))
+        f = function([a,b], s)
+        val = f(1,2)
+        self.failUnless(numpy.all(val == [10,1,2,3]))
+        e = f.maker.env.toposort()
+        assert len([n for n in e if isinstance(n.op,opt.MakeVector)]) > 0
+        assert len([n for n in e if isinstance(n, Join)]) == 0
+        assert f.maker.env.outputs[0].dtype == 'int64'
+
     def test_join_vector(self):
         a = as_tensor_variable(numpy.array([1, 2, 3]))
         b = as_tensor_variable(numpy.array([7, 8, 9]))
@@ -3440,6 +3470,28 @@ def test_dimshuffle_duplicate():
 
     assert success
 
+class T_get_constant_value(unittest.TestCase):
+
+    def test_get_constant_value(self):
+        a = tensor.stack(1,2,3)
+        assert get_constant_value(a[0])==1
+        assert get_constant_value(a[1])==2
+        assert get_constant_value(a[2])==3
+
+        b = tensor.iscalar()
+        a = tensor.stack(b,2,3)
+        self.assertRaises(TypeError, get_constant_value, a[0])
+        assert get_constant_value(a[1])==2
+        assert get_constant_value(a[2])==3
+
+        #For now get_constant_value got throught only MakeVector and Join of scalar.
+        v = tensor.ivector()
+        a = tensor.stack(v,2,3)
+        self.assertRaises(TypeError, get_constant_value, a[0])
+        self.assertRaises(TypeError, get_constant_value, a[1])
+        self.assertRaises(TypeError, get_constant_value, a[2])
+
+
 if __name__ == '__main__':
     if 1:
         unittest.main()
@@ -3449,5 +3501,3 @@ if __name__ == '__main__':
         suite = unittest.TestLoader()
         suite = suite.loadTestsFromTestCase(testcase)
         unittest.TextTestRunner(verbosity=2).run(suite)
-
-

theano/tensor/tests/test_sharedvar.py

@@ -316,20 +316,20 @@ def makeSharedTester(shared_constructor_,
 
             #Test that we forward the input
             specify_shape_fct = theano.function([],x1_specify_shape)
-            theano.printing.debugprint(specify_shape_fct)
+            #theano.printing.debugprint(specify_shape_fct)
             assert numpy.all(self.ref_fct(specify_shape_fct())
                              ==self.ref_fct(x1_2))
             topo_specify = specify_shape_fct.maker.env.toposort()
             if theano.config.mode!='FAST_COMPILE':
-                assert len(topo_specify)==6
+                assert len(topo_specify)==4
 
             #Test that we put the shape info into the graph
             shape_constant_fct = theano.function([],x1_specify_shape.shape)
-            theano.printing.debugprint(shape_constant_fct)
+            #theano.printing.debugprint(shape_constant_fct)
             assert numpy.all(shape_constant_fct()==shape_op_fct())
             topo_cst = shape_constant_fct.maker.env.toposort()
             if theano.config.mode!='FAST_COMPILE':
-                assert len(topo_cst)==6
+                assert len(topo_cst)==2
 
             #Test that we can replace with values of the different shape
             # but that will raise an error in some case, but not all