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)
 
 
@@ -1505,7 +1522,7 @@ class SpecifyShape(Op):
     L{Op} put into the graph the user provided shape
 
     In the case where this op stay in the final graph, we assert the shape.
-    For this the output of this op must be used in the graph. This is not 
+    For this the output of this op must be used in the graph. This is not
     the case most of the time if we only take the shape of the output.
     Maybe there is other optimization that will mess with this.
 
@@ -1524,12 +1541,12 @@ class SpecifyShape(Op):
             x = as_tensor_variable(x)
         shape = as_tensor_variable(shape)
         return Apply(self, [x, shape], [x.type()])
-    
+
     def perform(self, node, (x,shape ), (out, )):
         assert numpy.all(x.shape==shape), ("got shape", x.shape,
                                            "expected", shape)
         out[0] = x
-        
+
     def infer_shape(self, node, (xshape, sshape)):
         new_shape=[]
         for dim in range(node.inputs[0].ndim):
@@ -2276,7 +2293,7 @@ def std(input, axis=None):
     :type axis: None or int or (list of int) (see `Sum`)
     """
     return sqrt(var(input=input, axis=axis))
- 
+
 if 0:
     ## COMMENTED OUT FEB 17 2010
     ## TODO (DOCUMENT AND WRITE TESTS) OR DELETE
@@ -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