Move values_eq_approx_remove* methods to functions

theano/tensor/nnet/nnet.py

@@ -24,6 +24,7 @@ from theano.gof import Apply
 from theano.tensor.nnet.sigm import sigmoid, softplus
 from theano.gradient import DisconnectedType
 from theano.gradient import grad_not_implemented
+from theano.tensor.type import values_eq_approx_remove_nan
 
 
 ############
@@ -1965,7 +1966,7 @@ def make_out_pattern(X):
     out_var = stabilized_X - tensor.log(tensor.exp(stabilized_X).sum(
         axis=1)).dimshuffle(0, 'x')
     #tell DEBUG_MODE that it's OK if the original graph produced NaN and the optimized graph does not
-    out_var.values_eq_approx = out_var.type.values_eq_approx_remove_nan
+    out_var.values_eq_approx = values_eq_approx_remove_nan
     return out_var
 
 

theano/tensor/opt.py

@@ -35,6 +35,9 @@ from theano import scalar
 from theano.tensor import basic as T
 from theano import compile  # to register the optimizer built by this file
 from theano.compile.ops import Shape_i
+from theano.tensor.type import (values_eq_approx_remove_inf,
+                                values_eq_approx_remove_nan,
+                                values_eq_approx_remove_inf_nan)
 
 from theano.gof.python25 import any, all
 from theano.gof.opt import (Optimizer, pre_constant_merge,
@@ -2883,8 +2886,7 @@ def local_mul_switch_sink(node):
                     listmul = node.inputs[:idx] + node.inputs[idx + 1:]
                     fct = [T.switch(switch.inputs[0], 0,
                                     T.mul(*(listmul + [switch.inputs[2]])))]
-                    fct[0].values_eq_approx = fct[
-                        0].type.values_eq_approx_remove_nan
+                    fct[0].values_eq_approx = values_eq_approx_remove_nan
                     return fct
             except NotScalarConstantError:
                 pass
@@ -2894,8 +2896,7 @@ def local_mul_switch_sink(node):
                     listmul = node.inputs[:idx] + node.inputs[idx + 1:]
                     fct = [T.switch(switch.inputs[0],
                                     T.mul(*(listmul + [switch.inputs[1]])), 0)]
-                    fct[0].values_eq_approx = fct[
-                        0].type.values_eq_approx_remove_nan
+                    fct[0].values_eq_approx = values_eq_approx_remove_nan
                     return fct
             except NotScalarConstantError:
                 pass
@@ -2925,8 +2926,7 @@ def local_div_switch_sink(node):
             if get_scalar_constant_value(switch.inputs[1]) == 0.:
                 fct = [T.switch(switch.inputs[0], 0,
                                 op(switch.inputs[2], node.inputs[1]))]
-                fct[0].values_eq_approx = fct[
-                    0].type.values_eq_approx_remove_nan
+                fct[0].values_eq_approx = values_eq_approx_remove_nan
                 return fct
         except NotScalarConstantError:
             pass
@@ -2934,8 +2934,7 @@ def local_div_switch_sink(node):
             if get_scalar_constant_value(switch.inputs[2]) == 0.:
                 fct = [T.switch(switch.inputs[0],
                                 op(switch.inputs[1], node.inputs[1]), 0)]
-                fct[0].values_eq_approx = fct[
-                    0].type.values_eq_approx_remove_nan
+                fct[0].values_eq_approx = values_eq_approx_remove_nan
                 return fct
         except NotScalarConstantError:
             pass
@@ -4474,7 +4473,7 @@ def local_log_add(node):
 
                 ret = max_pre + T.log1p(T.exp(T.add(*[p - max_pre
                                                       for p in pre_exp])))
-                ret.values_eq_approx = ret.type.values_eq_approx_remove_inf
+                ret.values_eq_approx = values_eq_approx_remove_inf
                 return [ret]
 
 
@@ -4899,7 +4898,7 @@ def local_log_erfc(node):
         threshold = 26.641747557
 
     ret = T.switch(x < threshold, node.outputs[0], stab_value)
-    ret.values_eq_approx = ret.type.values_eq_approx_remove_inf
+    ret.values_eq_approx = values_eq_approx_remove_inf
     return [ret]
 
 
@@ -5046,7 +5045,7 @@ def local_grad_log_erfc_neg(node):
     elif x.dtype == 'float64':
         threshold = 26.641747557
     ret = T.switch(x < threshold, true_div_no_mul, stab_value) * y
-    ret.values_eq_approx = ret.type.values_eq_approx_remove_inf_nan
+    ret.values_eq_approx = values_eq_approx_remove_inf_nan
 
     return [ret]
     """

theano/tensor/type.py

@@ -378,18 +378,6 @@ class TensorType(Type):
 
         return False
 
-    @staticmethod
-    def values_eq_approx_remove_inf(a, b):
-        return TensorType.values_eq_approx(a, b, True)
-
-    @staticmethod
-    def values_eq_approx_remove_nan(a, b):
-        return TensorType.values_eq_approx(a, b, False, True)
-
-    @staticmethod
-    def values_eq_approx_remove_inf_nan(a, b):
-        return TensorType.values_eq_approx(a, b, True, True)
-
     def __hash__(self):
         """Hash equal for same kinds of TensorType"""
         return hashtype(self) ^ hash(self.dtype) ^ hash(self.broadcastable)
@@ -629,6 +617,19 @@ class TensorType(Type):
             return numpy.dtype(self.dtype).itemsize
 theano.compile.ops.expandable_types += (TensorType,)
 
+
+def values_eq_approx_remove_inf(a, b):
+    return TensorType.values_eq_approx(a, b, True)
+
+
+def values_eq_approx_remove_nan(a, b):
+    return TensorType.values_eq_approx(a, b, False, True)
+
+
+def values_eq_approx_remove_inf_nan(a, b):
+    return TensorType.values_eq_approx(a, b, True, True)
+
+
 # Register TensorType C code for ViewOp.
 theano.compile.register_view_op_c_code(
         TensorType,