merged

core.py

 
 import gof
-from gof import current_mode, set_mode, build_mode, eval_mode, pop_mode, UNCOMPUTED, UNDEFINED, PythonR
+from gof import current_mode, set_mode, build_mode, eval_mode, build_eval_mode, pop_mode, UNCOMPUTED, UNDEFINED, PythonR
 
 import numpy
 
@@ -165,6 +165,7 @@ class NumpyR(gof.PythonR):
             self.data = value
         else:
             self.data = numpy.array(value)
+        self.up_to_date = True
 
     def  __add__(self, y): return add(self, y)
     def __radd__(self, x): return add(x, self)
@@ -208,14 +209,13 @@ zeros = wrap_producer(numpy.zeros)
 ones = wrap_producer(numpy.ones)
 
 
-
 # Wrapper to ensure that all inputs to the function impl have the same size (foils numpy's broadcasting)
 def assert_same_shapes(impl):
     def ret(x, *rest):
         shape = x.shape
         for other in rest:
             if other.shape != shape:
-                raise TypeError("The dimensions of the inputs do not match.")
+                raise ValueError("The dimensions of the inputs do not match.")
         return impl(x, *rest)
     return ret
 
@@ -223,7 +223,7 @@ def assert_same_shapes(impl):
 def tensor_scalar_op(impl):
     def ret(x, a):
         if a.shape:
-            raise TypeError("The second argument to %s must be a scalar." % impl)
+            raise ValueError("The second argument to %s must be a scalar." % impl)
         return impl(x, a)
     return ret
 
@@ -240,7 +240,6 @@ class add_elemwise(proto_add_elemwise):
 class iadd_elemwise(proto_add_elemwise, inplace):
     impl = assert_same_shapes(numpy.ndarray.__iadd__)
 
-
 class proto_add_scalar(omega_op):
     def grad(x, a, gz):
         return gz, sum(gz)
@@ -355,7 +354,6 @@ class div_elemwise(proto_div_elemwise):
 class idiv_elemwise(proto_div_elemwise, inplace):
     impl = assert_same_shapes(numpy.ndarray.__idiv__)
 
-
 def div_scalar_r(x, a):
     return scale(x, inv_elemwise(a))
 

test.py

@@ -156,8 +156,10 @@ class sigmoid(core.omega_op):
 
 numpy.random.seed(1)
 
+core.build_eval_mode()
 x = core.zeros((1, 10))
 w = core.input(numpy.random.rand(10, 15))
+core.pop_mode()
 
 # x = numpy.zeros((1, 10))
 # w = numpy.random.rand(10, 15)
@@ -188,6 +190,8 @@ f = compile.to_func([w, x], [w2, rec_error])
 #f = compile.single(w2, rec_error)
 
 for i in dataset_1hot(x.data, numpy.ndarray((1, )), 10000):
+#     w.up_to_date = True
+#     x.up_to_date = True
     w2, rec_error = f(w.data, x.data)
     if not(i % 1000):
         print rec_error
@@ -223,24 +227,25 @@ print w.data
 
 ############################
 
-
 x = core.ones((2, 2))
 y = core.zeros((1, 1))
 
 #print "?", gof.graph.ops([], [x + y])
 
 
-print x
-
-x + x
-print "1", gof.eval_env#.ops()
-y + y
-print "2", gof.eval_env#.ops()
-x + x
-print "3", gof.eval_env#.ops()
-
+# x + x
+# print "1", gof.eval_env#.ops()
+# y + y
+# print "2", gof.eval_env#.ops()
+# x + x
+# print "3", gof.eval_env#.ops()
 
-x += (x + x)
+core.build_eval_mode()
+x = core.ones((2, 2))
+y = core.ones((2, 2)) * 2
+x += y.T
+# z = core.iadd(x, y)
+# core.iadd(x, y)
 print x
-
+core.pop_mode()