added pprint (as printing) to the theano package root

examples/logistic_regression.py

@@ -4,7 +4,8 @@ import theano
 from theano import tensor as T
 from theano.tensor import nnet_ops
 from theano.compile import module
-from theano.sandbox import pprint
+from theano import printing, pprint
+from theano import compile
 
 import numpy as N
 
@@ -17,6 +18,7 @@ class LogisticRegressionN(module.FancyModule):
             self.w = N.random.randn(n_in, n_out)
             self.b = N.random.randn(n_out)
             self.lr = 0.01
+            self.__hide__ = ['params']
 
     def __init__(self, x = None, targ = None):
         super(LogisticRegressionN, self).__init__() #boilerplate
@@ -84,8 +86,8 @@ class LogisticRegression2(module.FancyModule):
 
 
 if __name__ == '__main__':
-    pprint.pp.assign(nnet_ops.crossentropy_softmax_1hot_with_bias_dx, pprint.FunctionPrinter('xsoftmaxdx'))
-    pprint.pp.assign(nnet_ops.crossentropy_softmax_argmax_1hot_with_bias, pprint.FunctionPrinter('nll', 'softmax', 'argmax'))
+    pprint.assign(nnet_ops.crossentropy_softmax_1hot_with_bias_dx, printing.FunctionPrinter('xsoftmaxdx'))
+    pprint.assign(nnet_ops.crossentropy_softmax_argmax_1hot_with_bias, printing.FunctionPrinter('nll', 'softmax', 'argmax'))
     if 1:
         lrc = LogisticRegressionN()
 
@@ -94,17 +96,21 @@ if __name__ == '__main__':
         print '================'
         print lrc.update.pretty(mode = theano.Mode('py', 'fast_run'))
         print '================'
+#         print lrc.update.pretty(mode = compile.FAST_RUN.excluding('inplace'))
+#         print '================'
 
 #        sys.exit(0)
 
         lr = lrc.make(10, 2, mode=theano.Mode('c|py', 'fast_run'))
+        #lr = lrc.make(10, 2, mode=compile.FAST_RUN.excluding('fast_run'))
         #lr = lrc.make(10, 2, mode=theano.Mode('py', 'merge')) #'FAST_RUN')
 
         data_x = N.random.randn(5, 10)
         data_y = (N.random.randn(5) > 0)
 
         for i in xrange(10000):
-            xe = lr.update(data_x, data_y)
+            lr.lr = 0.02
+            xe = lr.update(data_x, data_y) 
             if i % 100 == 0:
                 print i, xe
 

theano/__init__.py

@@ -41,13 +41,19 @@ from compile import \
     SymbolicOutput, Out, \
     Mode, \
     predefined_modes, predefined_linkers, predefined_optimizers, \
-    FunctionMaker, function, OpFromGraph #, eval_outputs, fast_compute
+    FunctionMaker, function, OpFromGraph, \
+    Component, External, Member, KitComponent, Method, \
+    Composite, ComponentList, Module, FancyModule
+
+from printing import \
+    pprint, pp
 
 import tensor
 import scalar
 import sparse
 import gradient
 
+
 ## import scalar_opt
 
 import subprocess as _subprocess

theano/compile/mode.py

@@ -102,12 +102,19 @@ class Mode(object):
             optimizer = predefined_optimizers[optimizer]
         if isinstance(optimizer, gof.Query):
             self.provided_optimizer = optimizer
-            optimizer = optdb.query(optimizer)
-        self.optimizer = optimizer
+        self._optimizer = optimizer
 
     def __str__(self):
         return "Mode(linker = %s, optimizer = %s)" % (self.provided_linker, self.provided_optimizer)
 
+    def __get_optimizer(self):
+        if isinstance(self._optimizer, gof.Query):
+            return optdb.query(self._optimizer)
+        else:
+            return self._optimizer
+
+    optimizer = property(__get_optimizer)
+
     def including(self, *tags):
         return Mode(self.provided_linker, self.provided_optimizer.including(*tags))
 

theano/compile/module.py

 
 from .. import gof
+from ..printing import pprint
 from collections import defaultdict
 from itertools import chain
 from functools import partial
 from copy import copy
-import mode
+import io
 import function_module as F
-#from ..sandbox import pprint
 
 
 def join(*args):
@@ -117,7 +117,7 @@ class External(_RComponent):
     def pretty(self, **kwargs):
         rval = super(External, self).pretty()
         if self.r.owner:
-            rval += '\n= %s' % (pprint.pp2.process(self.r, dict(target = self.r)))
+            rval += '\n= %s' % (pprint(self.r, dict(target = self.r)))
         return rval
 
 
@@ -196,13 +196,15 @@ class Method(Component):
                 else:
                     return gof.Container(r, storage = [None])
         inputs = self.inputs
-        inputs = [mode.In(result = input,
-                          value = get_storage(input))
+        inputs = [io.In(result = input,
+                        value = get_storage(input),
+                        mutable = False)
                   for input in inputs]
-        inputs += [mode.In(result = k,
-                           update = v,
-                           value = get_storage(k, True),
-                           strict = True)
+        inputs += [io.In(result = k,
+                         update = v,
+                         value = get_storage(k, True),
+                         mutable = True,
+                         strict = True)
                    for k, v in self.updates.iteritems()]
         outputs = self.outputs
         _inputs = [x.result for x in inputs]
@@ -210,8 +212,9 @@ class Method(Component):
                                       + [x.update for x in inputs if getattr(x, 'update', False)],
                                       blockers = _inputs):
             if input not in _inputs and not isinstance(input, gof.Value):
-                inputs += [mode.In(result = input,
-                                   value = get_storage(input, True))]
+                inputs += [io.In(result = input,
+                                 value = get_storage(input, True),
+                                 mutable = False)]
         inputs += [(kit, get_storage(kit, True)) for kit in self.kits]
         return F.function(inputs, outputs, mode)
 
@@ -234,11 +237,14 @@ class Method(Component):
             nup = len(k)
             eff_in = tuple(inputs) + tuple(k)
             eff_out = tuple(outputs) + tuple(v)
-            env = gof.Env(*gof.graph.clone(eff_in + tuple(gof.graph.inputs(eff_out)),
+            supp_in = tuple(gof.graph.inputs(eff_out))
+            env = gof.Env(*gof.graph.clone(eff_in + supp_in,
                                            eff_out))
+            sup = F.Supervisor(set(env.inputs).difference(env.inputs[len(inputs):len(eff_in)]))
+            env.extend(sup)
             mode.optimizer.optimize(env)
             inputs, outputs, updates = env.inputs[:nin], env.outputs[:nout], dict(zip(env.inputs[nin:], env.outputs[nout:]))
-        rval += pprint.pp.process_graph(inputs, outputs, updates, False)
+        rval += pprint(inputs, outputs, updates, False)
         return rval
 
     def __str__(self):

theano/gof/toolbox.py

@@ -101,7 +101,7 @@ class ReplaceValidate(History, Validator):
             try:
                 env.replace(r, new_r)
             except Exception, e:
-                if not 'The type of the replacement must be the same' in str(e) or not 'does not belong to this Env' in str(e):
+                if 'The type of the replacement must be the same' not in str(e) and 'does not belong to this Env' not in str(e):
                     print >>sys.stderr, "<<!! BUG IN ENV.REPLACE OR A LISTENER !!>>", type(e), e
                 env.revert(chk) # this might fail if the error is in a listener: (env.replace kinda needs better internal error handling)
                 raise

theano/sandbox/pprint.py → theano/printing.py

 
-from .. import tensor as T
-from .. import scalar as S
-from .. import gof
+import gof
 from copy import copy
 import sys
 
@@ -88,7 +86,7 @@ class FunctionPrinter:
             raise TypeError("function %s cannot represent a result with no associated operation" % self.names)
         idx = node.outputs.index(output)
         name = self.names[idx]
-        return "%s(%s)" % (name, ", ".join([pprinter.process(input, pstate.clone(precedence = 1000))
+        return "%s(%s)" % (name, ", ".join([pprinter.process(input, pstate.clone(precedence = -1000))
                                             for input in node.inputs]))
 
 class MemberPrinter:
@@ -119,65 +117,6 @@ class IgnorePrinter:
         return "%s" % pprinter.process(input, pstate)
 
 
-class DimShufflePrinter:
-
-    def __p(self, new_order, pstate, r):
-        if new_order != () and  new_order[0] == 'x':
-#            return "%s" % self.__p(new_order[1:], pstate, r)
-            return "[%s]" % self.__p(new_order[1:], pstate, r)
-        if list(new_order) == range(r.type.ndim):
-            return pstate.pprinter.process(r)
-        if list(new_order) == list(reversed(range(r.type.ndim))):
-            return "%s.T" % pstate.pprinter.process(r)
-        return "DimShuffle{%s}(%s)" % (", ".join(map(str, new_order)), pstate.pprinter.process(r))
-
-    def process(self, r, pstate):
-        if r.owner is None:
-            raise TypeError("Can only print DimShuffle.")
-        elif isinstance(r.owner.op, T.DimShuffle):
-            ord = r.owner.op.new_order
-            return self.__p(ord, pstate, r.owner.inputs[0])            
-        else:
-            raise TypeError("Can only print DimShuffle.")
-
-
-class SubtensorPrinter:
-
-    def process(self, r, pstate):
-        if r.owner is None:
-            raise TypeError("Can only print Subtensor.")
-        elif isinstance(r.owner.op, T.Subtensor):
-            idxs = r.owner.op.idx_list
-            inputs = list(r.owner.inputs)
-            input = inputs.pop()
-            sidxs = []
-            inbrack_pstate = pstate.clone(precedence = -1000)
-            for entry in idxs:
-                if isinstance(entry, int):
-                    sidxs.append(str(entry))
-                elif isinstance(entry, S.Scalar):
-                    sidxs.append(inbrack_pstate.pprinter.process(inputs.pop()))
-                elif isinstance(entry, slice):
-                    sidxs.append("%s:%s%s" % ("" if entry.start is None or entry.start == 0 else entry.start,
-                                              "" if entry.stop is None or entry.stop == sys.maxint else entry.stop,
-                                              "" if entry.step is None else ":%s" % entry.step))
-            return "%s[%s]" % (pstate.pprinter.process(input, pstate.clone(precedence = 1000)),
-                               ", ".join(sidxs))
-        else:
-            raise TypeError("Can only print Subtensor.")
-
-
-class MakeVectorPrinter:
-
-    def process(self, r, pstate):
-        if r.owner is None:
-            raise TypeError("Can only print make_vector.")
-        elif isinstance(r.owner.op, T.MakeVector):
-            return "[%s]" % ", ".join(pstate.pprinter.process(input, pstate.clone(precedence = 1000)) for input in r.owner.inputs)
-        else:
-            raise TypeError("Can only print make_vector.")
-
-
 class DefaultPrinter:
 
     def __init__(self):
@@ -263,6 +202,16 @@ class PPrinter:
         strings.sort()
         return "\n".join(s[1] for s in strings)
 
+    def __call__(self, *args):
+        if len(args) == 1:
+            return self.process(*args)
+        elif len(args) == 2 and isinstance(args[1], (PrinterState, dict)):
+            return self.process(*args)
+        elif len(args) > 2:
+            return self.process_graph(*args)
+        else:
+            raise TypeError('Not enough arguments to call.')
+
 
 
 
@@ -276,47 +225,10 @@ greek = dict(alpha    = u"\u03B1",
              epsilon  = u"\u03B5")
 
 
-ppow = OperatorPrinter('**', 1, 'right')
-pneg = OperatorPrinter('-',  0, 'either')
-pmul = OperatorPrinter('*', -1, 'either')
-pdiv = OperatorPrinter('/', -1, 'left')
-padd = OperatorPrinter('+', -2, 'either')
-psub = OperatorPrinter('-', -2, 'left')
-pdot = OperatorPrinter(special['middle_dot'], -1, 'left')
-psum = OperatorPrinter(special['big_sigma']+' ', -2, 'left')
-
-from ..tensor import inplace as I
-
-def pprinter():
-    pp = PPrinter()
-    pp.assign(lambda pstate, r: True, DefaultPrinter())
-    pp.assign(T.add, padd)
-    pp.assign(T.mul, pmul)
-    pp.assign(T.sub, psub)
-    pp.assign(T.neg, pneg)
-    pp.assign(T.div, pdiv)
-    pp.assign(T.pow, ppow)
-    pp.assign(T.dot, pdot)
-    pp.assign(T.Sum(), FunctionPrinter('sum'))
-    pp.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, T.DimShuffle), DimShufflePrinter())
-    pp.assign(T.tensor_copy, IgnorePrinter())
-    pp.assign(T.log, FunctionPrinter('log'))
-    pp.assign(T.tanh, FunctionPrinter('tanh'))
-    pp.assign(I.transpose_inplace, MemberPrinter('T'))
-    pp.assign(T.abs_, PatternPrinter(('|%(0)s|', -1000)))
-    pp.assign(T.sgn, FunctionPrinter('sgn'))
-    pp.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, T.Filler) and r.owner.op.value == 0, FunctionPrinter('seros'))
-    pp.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, T.Filler) and r.owner.op.value == 1, FunctionPrinter('ones'))
-    pp.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, T.Subtensor), SubtensorPrinter())
-    pp.assign(T.shape, MemberPrinter('shape'))
-    pp.assign(T.fill, FunctionPrinter('fill'))
-    #pp.assign(T.vertical_stack, FunctionPrinter('vstack'))
-    pp.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, T.MakeVector), MakeVectorPrinter())
-    return pp
-
-pp = pprinter()
-
-pp2 = pprinter()
-pp2.assign(lambda pstate, r: hasattr(pstate, 'target') and pstate.target is not r and r.name is not None,
-           LeafPrinter())
+pprint = PPrinter()
+pprint.assign(lambda pstate, r: True, DefaultPrinter())
+pprint.assign(lambda pstate, r: hasattr(pstate, 'target') and pstate.target is not r and r.name is not None,
+              LeafPrinter())
+
+pp = pprint
 

theano/scalar/basic.py

@@ -252,17 +252,26 @@ def upcast_out(*types):
     return Scalar(dtype = Scalar.upcast(*types)),
 def same_out(type):
     return type,
-class transfer_type:
-    def __init__(self, i):
-        assert type(i) == int
-        self.i = i
+class transfer_type(gof.utils.object2):
+    def __init__(self, *transfer):
+        assert all(type(x) == int for x in transfer)
+        self.transfer = transfer
     def __call__(self, *types):
-        return types[self.i],
-class specific_out:
+        upcast = upcast_out(*types)
+        return [upcast if i is None else types[i] for i in self.transfer]
+    def __eq__(self, other):
+        return type(self) == type(other) and self.transfer == other.transfer
+    def __hash__(self):
+        return hash(self.transfer)
+class specific_out(gof.utils.object2):
     def __init__(self, *spec):
         self.spec = spec
     def __call__(self, *types):
         return self.spec
+    def __eq__(self, other):
+        return type(self) == type(other) and self.spec == other.spec
+    def __hash__(self):
+        return hash(self.spec)
 def int_out(*types):
     return int64,
 def float_out(*types):
@@ -328,9 +337,10 @@ class ScalarOp(Op):
         raise AbstractFunctionError()
 
     def __eq__(self, other):
-        return type(self) == type(other) \
+        test =  type(self) == type(other) \
             and getattr(self, 'output_types_preference', None) \
             == getattr(other, 'output_types_preference', None)
+        return test
 
     def __hash__(self):
         return hash(getattr(self, 'output_types_preference', 0))

theano/tensor/basic.py

@@ -20,7 +20,8 @@ import elemwise
 from .. import scalar as scal
 from ..gof.python25 import partial
 
-from .. import compile
+from .. import compile, printing
+from ..printing import pprint
 
 
 ### set up the external interface
@@ -614,6 +615,8 @@ def _scal_elemwise(symbol):
     rval.__epydoc_asRoutine = symbol
     rval.__module__ = 'tensor'
 
+    pprint.assign(rval, printing.FunctionPrinter(symbolname))
+
     return rval
 
 
@@ -661,33 +664,34 @@ def cast(t, dtype):
     return mapping[dtype](t)
 
 #to be removed as we get the epydoc routine-documenting thing going -JB 20080924
-def _conversion(real_value):
+def _conversion(real_value, name):
     __oplist_tag(real_value, 'casting')
     real_value.__module__='tensor.basic'
+    pprint.assign(real_value, printing.FunctionPrinter(name))
     return real_value
 
-convert_to_int8  = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int8))))
+convert_to_int8  = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int8))), 'int8')
 """Cast to 8-bit integer"""
     
-convert_to_int16 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int16))))
+convert_to_int16 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int16))), 'int16')
 """Cast to 16-bit integer"""
 
-convert_to_int32 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int32))))
+convert_to_int32 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int32))), 'int32')
 """Cast to 32-bit integer"""
 
-convert_to_int64 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int64))))
+convert_to_int64 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int64))), 'int64')
 """Cast to 64-bit integer"""
 
-convert_to_float32 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.float32))))
+convert_to_float32 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.float32))), 'float32')
 """Cast to single-precision floating point"""
 
-convert_to_float64 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.float64))))
+convert_to_float64 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.float64))), 'float64')
 """Cast to double-precision floating point"""
 
-convert_to_complex64  = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.complex64))))
+convert_to_complex64  = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.complex64))), 'complex64')
 """Cast to single-precision complex"""
 
-convert_to_complex128 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.complex128))))
+convert_to_complex128 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.complex128))), 'complex128')
 """Cast to double-precision complex"""
 
 
@@ -713,6 +717,9 @@ class Shape(Op):
 def shape(a):
     pass
 
+pprint.assign(shape, printing.MemberPrinter('shape'))
+
+
 class MaxAndArgmax(Op):
     """Calculate the max and argmax over a given axis"""
     nin=2 # tensor, axis
@@ -834,6 +841,9 @@ def abs_(a):
 
     """
 
+pprint.assign(abs_, printing.PatternPrinter(('|%(0)s|', -1000)))
+
+
 @_scal_elemwise
 def exp(a):
     """e^`a`"""
@@ -902,6 +912,8 @@ def second(a, b):
     """Create a matrix by filling the shape of a with b"""
 
 fill = second
+pprint.assign(fill, printing.FunctionPrinter('fill'))
+
 
 @constructor
 def ones_like(model):
@@ -967,10 +979,15 @@ def one():
     """WRITEME"""
     return Ones(0)([])
 
+pprint.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, Filler) and r.owner.op.value == 0, printing.FunctionPrinter('zeros'))
+pprint.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, Filler) and r.owner.op.value == 1, printing.FunctionPrinter('ones'))
+
 
 @_redefine(elemwise.Elemwise(scal.identity))
 def tensor_copy(a):
     """Create a duplicate of `a` (with duplicated storage)"""
+pprint.assign(tensor_copy, printing.IgnorePrinter())
+
 
 @_redefine(elemwise.Elemwise(scal.identity, inplace_pattern = {0: [0]}))
 def view(a):
@@ -981,6 +998,9 @@ def sum(input, axis = None):
     """WRITEME"""
     return elemwise.Sum(axis)(input)
 
+pprint.assign(Sum(), printing.FunctionPrinter('sum'))
+
+
 @constructor
 def mean(input, axis = None):
     """WRITEME"""
@@ -1043,6 +1063,14 @@ def mod(a, b):
 def pow(a, b):
     """elementwise power"""
 
+pprint.assign(add, printing.OperatorPrinter('+', -2, 'either'))
+pprint.assign(mul, printing.OperatorPrinter('*', -1, 'either'))
+pprint.assign(sub, printing.OperatorPrinter('-', -2, 'left'))
+pprint.assign(neg, printing.OperatorPrinter('-',  0, 'either'))
+pprint.assign(div, printing.OperatorPrinter('/', -1, 'left'))
+pprint.assign(pow, printing.OperatorPrinter('**', 1, 'right'))
+
+
 
 ##########################
 # View Operations
@@ -1214,6 +1242,36 @@ class Subtensor(Op):
         return "%s{%s}" % (self.__class__.__name__, ", ".join(indices))
 
 
+
+class SubtensorPrinter:
+
+    def process(self, r, pstate):
+        if r.owner is None:
+            raise TypeError("Can only print Subtensor.")
+        elif isinstance(r.owner.op, Subtensor):
+            idxs = r.owner.op.idx_list
+            inputs = list(r.owner.inputs)
+            input = inputs.pop()
+            sidxs = []
+            inbrack_pstate = pstate.clone(precedence = -1000)
+            for entry in idxs:
+                if isinstance(entry, int):
+                    sidxs.append(str(entry))
+                elif isinstance(entry, scal.Scalar):
+                    sidxs.append(inbrack_pstate.pprinter.process(inputs.pop()))
+                elif isinstance(entry, slice):
+                    sidxs.append("%s:%s%s" % ("" if entry.start is None or entry.start == 0 else entry.start,
+                                              "" if entry.stop is None or entry.stop == sys.maxint else entry.stop,
+                                              "" if entry.step is None else ":%s" % entry.step))
+            return "%s[%s]" % (pstate.pprinter.process(input, pstate.clone(precedence = 1000)),
+                               ", ".join(sidxs))
+        else:
+            raise TypeError("Can only print Subtensor.")
+
+pprint.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, Subtensor), SubtensorPrinter())
+
+
+
 class SetSubtensor(Subtensor):
     """WRITEME"""
     view_map = {}
@@ -1474,6 +1532,11 @@ def join(axis, *tensors):
 
     """
 
+pprint.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, Join),
+              printing.FunctionPrinter('join'))
+
+
+
 @constructor
 def shape_padleft(tensor, n_ones):
     """Reshape `tensor` by left-padding the shape with `n_ones` 1s
@@ -1630,6 +1693,21 @@ make_lvector = MakeVector(lscalar)
 """WRITEME"""
 
 
+class MakeVectorPrinter:
+
+    def process(self, r, pstate):
+        if r.owner is None:
+            raise TypeError("Can only print make_vector.")
+        elif isinstance(r.owner.op, MakeVector):
+            return "[%s]" % ", ".join(pstate.pprinter.process(input, pstate.clone(precedence = 1000)) for input in r.owner.inputs)
+        else:
+            raise TypeError("Can only print make_vector.")
+
+pprint.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, MakeVector), MakeVectorPrinter())
+
+
+
+
 #########################
 # Linalg : Dot
 #########################
@@ -1696,6 +1774,7 @@ class Dot(Op):
     def __str__(self):
         return "dot"
 dot = Dot()
+pprint.assign(dot, printing.OperatorPrinter(printing.special['middle_dot'], -1, 'left'))
 
 class Outer(Op):
     """ Compute vector-vector outer product
@@ -1964,6 +2043,8 @@ class Gemm(Op):
 gemm = Gemm()
 
 
+pprint.assign(gemm, printing.FunctionPrinter('gemm'))
+
 
 #########################
 # Gradient

theano/tensor/elemwise.py

@@ -6,6 +6,8 @@ from .. import gof
 from ..gof import Op, Apply
 from .. import scalar
 from ..scalar import Scalar
+from .. import printing
+from ..printing import pprint
 from ..gof.python25 import all
 from copy import copy
 
@@ -182,6 +184,31 @@ class DimShuffle(Op):
         return DimShuffle(gz.type.broadcastable, grad_order)(gz),
 
 
+
+class DimShufflePrinter:
+
+    def __p(self, new_order, pstate, r):
+        if new_order != () and  new_order[0] == 'x':
+#            return "%s" % self.__p(new_order[1:], pstate, r)
+            return "[%s]" % self.__p(new_order[1:], pstate, r)
+        if list(new_order) == range(r.type.ndim):
+            return pstate.pprinter.process(r)
+        if list(new_order) == list(reversed(range(r.type.ndim))):
+            return "%s.T" % pstate.pprinter.process(r)
+        return "DimShuffle{%s}(%s)" % (", ".join(map(str, new_order)), pstate.pprinter.process(r))
+
+    def process(self, r, pstate):
+        if r.owner is None:
+            raise TypeError("Can only print DimShuffle.")
+        elif isinstance(r.owner.op, DimShuffle):
+            ord = r.owner.op.new_order
+            return self.__p(ord, pstate, r.owner.inputs[0])            
+        else:
+            raise TypeError("Can only print DimShuffle.")
+
+pprint.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, DimShuffle), DimShufflePrinter())
+
+
 ################
 ### Elemwise ###
 ################

theano/tensor/inplace.py

@@ -2,6 +2,8 @@
 from basic import _scal_elemwise, _transpose_inplace
 from .. import scalar as scal
 import elemwise
+from .. import printing
+from ..printing import pprint
 
 def _scal_inplace(symbol):
     """Replace a symbol definition with an elementwise version of the corresponding scalar Op"""
@@ -24,6 +26,16 @@ def _scal_inplace(symbol):
     rval.__epydoc_asRoutine = symbol
     rval.__module__ = 'theano.tensor.inplace'
 
+    def chk(pstate, r):
+        if not r.owner:
+            return False
+        op = r.owner.op
+#         print op, rval, r.owner and op == rval
+#         print op.inplace_pattern, rval.inplace_pattern, op.inplace_pattern == rval.inplace_pattern
+#         print op.scalar_op, rval.scalar_op, op.scalar_op == rval.scalar_op
+        return r.owner.op == rval
+
+    pprint.assign(chk, printing.FunctionPrinter(symbolname.replace('_inplace', '=')))
     return rval
 
 
@@ -132,6 +144,8 @@ def second_inplace(a):
     """Fill `a` with `b`"""
 
 fill_inplace = second_inplace
+pprint.assign(fill_inplace, printing.FunctionPrinter('fill='))
+
 
 @_scal_inplace
 def add_inplace(a, b):
@@ -157,7 +171,17 @@ def mod_inplace(a, b):
 def pow_inplace(a, b):
     """elementwise power (inplace on `a`)"""
 
+pprint.assign(add_inplace, printing.OperatorPrinter('+=', -2, 'either'))
+pprint.assign(mul_inplace, printing.OperatorPrinter('*=', -1, 'either'))
+pprint.assign(sub_inplace, printing.OperatorPrinter('-=', -2, 'left'))
+pprint.assign(neg_inplace, printing.OperatorPrinter('-=',  0, 'either'))
+pprint.assign(div_inplace, printing.OperatorPrinter('/=', -1, 'left'))
+pprint.assign(pow_inplace, printing.OperatorPrinter('**=', 1, 'right'))
+
+
 transpose_inplace = _transpose_inplace
 """WRITEME"""
 
+pprint.assign(transpose_inplace, printing.MemberPrinter('T'))
+
 

theano/tensor/opt.py

@@ -31,7 +31,7 @@ def in2out(*local_opts):
 
 # gemm: (d,a,b,c,s) -> d = d*s + a*dot(b,c)
 # Transforms d -= a * dot(b, c) into gemm(d, -a, b, c, 1.0)
-gemm_pattern_1 = gof.PatternSub((I.sub_inplace,
+gemm_pattern_1 = gof.PatternSub((T.sub,
                                  'd',
                                  (T.mul,
                                   dict(pattern = (T.DimShuffle((), ['x', 'x'], inplace = True), 'a'),
@@ -77,7 +77,10 @@ def _insert_inplace_optimizer(env):
             for candidate_input in candidate_inputs:
                 inplace_pattern = dict(baseline, **{candidate_output: candidate_input})
                 try:
-                    new = Elemwise(op.scalar_op, inplace_pattern).make_node(*node.inputs)
+                    new = Elemwise(
+                        op.scalar_op.__class__(
+                            scalar.transfer_type(*[inplace_pattern.get(i, None) for i in xrange(len(node.outputs))])),
+                        inplace_pattern).make_node(*node.inputs)
                     env.replace_all_validate(zip(node.outputs, new.outputs))
                 except Exception, e:
                     continue
@@ -89,7 +92,7 @@ insert_inplace_optimizer = gof.optimizer(_insert_inplace_optimizer)
 
 inplace_optimizer = gof.InplaceOptimizer(
     gof.SeqOptimizer(out2in(gemm_pattern_1),
-                     out2in(dot_to_gemm),
+                     #out2in(dot_to_gemm),
                      insert_inplace_optimizer,
                      failure_callback = gof.keep_going))
 compile.optdb.register('inplace', inplace_optimizer, 99, 'fast_run')
@@ -537,7 +540,7 @@ def mul_calculate(num, denum, aslist = False):
             return [v]
     return v
 
-local_mul_canonizer = Canonizer(T.mul, T.div, T.inv, mul_calculate, False)
+local_mul_canonizer = Canonizer(T.mul, T.div, T.inv, mul_calculate)
 
 @gof.local_optimizer([T.neg])
 def local_neg_to_mul(node):