Merged

doc/tutorial/debug_faq.txt

@@ -17,10 +17,10 @@ Isolating the problem/Testing Theano compiler
 You can run your Theano function in a DebugMode(:ref:`using_debugmode`). This test the Theano optimizations and help to find where NaN, inf and other problem come from.
 
 
-Can I get Theano to test my graph incrementally, as it's being built ?
------------------------------------------------------------------------
+Interactive Debugger
+--------------------
 
-Yes ! As of v.0.4.0, Theano has a new mechanism by which graphs are executed
+As of v.0.4.0, Theano has a new mechanism by which graphs are executed
 on-the-fly, before a theano.function is ever compiled. Since optimizations
 haven't been applied at this stage, it is easy for the user to locate the
 source of this bug. This functionality is enabled through the config flag

theano/compile/profilemode.py

@@ -411,7 +411,10 @@ class ProfileMode(Mode):
                     apply_time, op_cimpl, message, outputs_size,
                     other_time)
 
-        if outputs_size:
+        if not outputs_size:
+            print """\nProfile of Theano intermediate memory disabled.
+ To enabled, put the Theano flag ProfileMode.profile_memory to True."""
+        else:
             fct_memory={}#env->dict(node->(outputs size))
             var_mem = {}
             for node,val in outputs_size.items():
@@ -421,6 +424,7 @@ class ProfileMode(Mode):
                     var_mem[out]=v
             print
             print "Profile of Theano functions memory:"
+            print "(This check only the output of each apply node. It don't check the temporary memory used by the op in the apply node.)"
             nb_skipped = 0
             for env,nodes_mem in fct_memory.iteritems():
                 size_sum=sum([sum(val) for key,val in nodes_mem.iteritems()])

theano/gof/link.py

@@ -401,7 +401,7 @@ class PerformLinker(LocalLinker):
         for node in order:
             # Maker sure we don't use C version of the code, but rather only
             # the python version
-            node._op_use_c_code = False
+            node.op._op_use_c_code = False
             thunks += [node.op.make_thunk(node,
                                     storage_map,
                                     compute_map,

theano/misc/do_nightly_build

@@ -10,7 +10,10 @@ ls ${COMPILEDIR}|wc -l
 FLAGS=warn.argmax_pushdown_bug=False,warn.gpusum_01_011_0111_bug=False,warn.sum_sum_bug=False,warn.sum_div_dimshuffle_bug=False,compiledir=${COMPILEDIR}
 export PYTHONPATH=${ROOT_CWD}:$PYTHONPATH
 
-cd ${ROOT_CWD}
+cd ${ROOT_CWD}/Theano
+hg summary
+cd ..
+
 echo "executing nosetests with mode=FAST_COMPILE"
 THEANO_FLAGS=${FLAGS},mode=FAST_COMPILE ${NOSETESTS} Theano
 echo "nb element in the compiledir:"

theano/sandbox/neighbours.py

@@ -14,7 +14,7 @@ class Images2Neibs(Op):
     def __init__(self, mode='valid'):
         """
         Modes:
-            valid : Reshapes the input as a a 2D tensor where each row is a pooling example. 
+            valid : Reshapes the input as a a 2D tensor where each row is a pooling example.
                 Requires an input that is a multiple of the pooling factor (in each direction)
             ignore_borders : Same as valid, but will ignore the borders if the shape(s) of the input
                 is not a multiple of the pooling factor(s)
@@ -248,7 +248,7 @@ def neibs2images(neibs, neib_shape, original_shape, mode='valid'):
 
     new_neib_shape = T.stack(original_shape[-1] // neib_shape[1], neib_shape[1])
     output_2d = images2neibs(neibs.dimshuffle('x','x',0,1), new_neib_shape, mode=mode)
-    
+
     if mode == 'ignore_borders':
         valid_shape = list(original_shape)
         valid_shape[2]  = (valid_shape[2] // neib_shape[0]) * neib_shape[0]
@@ -261,7 +261,7 @@ def neibs2images(neibs, neib_shape, original_shape, mode='valid'):
             output_4d = T.concatenate([output_4d,T.zeros(pad_shape)],axis=d)
     else:
         output_4d = output_2d.reshape(original_shape)
-    
+
     return output_4d
 
 
@@ -539,7 +539,7 @@ class GpuImages2Neibs(Images2Neibs):
             dim3 n_blocks(std::min(32*1024,nb_block));
             int n_shared = 0;
 
-	    void (*f)(int, int, int ,int,
+            void (*f)(int, int, int ,int,
                       int, int, int ,int,
                       int, int,
                       int, int, int, int,
@@ -591,4 +591,3 @@ def use_gpu_images2neibs(node):
 
 if cuda_available:
     register_gpu_opt()(use_gpu_images2neibs)
-

theano/scalar/basic.py

@@ -1126,26 +1126,23 @@ int_div = IntDiv(upcast_out, name = 'int_div')
 floor_div = int_div
 
 
-def raise_complex_error():
-    raise ComplexError(
-                "Theano does not support the mod operator (%) on "
-                "complex numbers, since numpy deprecated it.")
-
-
 def mod_check(x, y):
     if (as_scalar(x).type in complex_types or
         as_scalar(y).type in complex_types):
         # Currently forbidden.
-        raise_complex_error()
+        raise Mod.complex_error
     else:
         return mod(x, y)
 
 
 class Mod(BinaryScalarOp):
+    complex_error = ComplexError(
+                "Theano does not support the mod operator (%) on "
+                "complex numbers, since numpy deprecated it.")
 
     def impl(self, x, y):
         if isinstance(x, numpy.complex) or isinstance(y, numpy.complex):
-            raise_complex_error()
+            raise self.complex_error
         return x % y
 
     def c_code_cache_version(self):
@@ -1184,7 +1181,7 @@ class Mod(BinaryScalarOp):
             x_mod_ypm = "fmod(%(x)s,-%(y)s)"%locals()
             x_mod_ymp = "fmod(-%(x)s,%(y)s)"%locals()
         elif str(t) in imap(str, complex_types):
-            raise_complex_error()
+            raise self.complex_error
         else:
             raise NotImplementedError('type not supported', type)
 

theano/tensor/basic.py

@@ -2630,7 +2630,7 @@ def mod_check(x, y):
     if (as_tensor_variable(x).dtype in complex_dtypes or
         as_tensor_variable(y).dtype in complex_dtypes):
         # Currently forbidden.
-        scal.raise_complex_error()
+        raise scal.Mod.complex_error
     else:
         return mod(x, y)