warn that axis=None behavior will change and the default for axis will change…

theano/tensor/basic.py

@@ -6,6 +6,7 @@ import __builtin__
 import sys # for sys.maxint
 from theano.configparser import config, AddConfigVar, BoolParam
 import traceback #for overriding Op.__call__
+import warnings
 
 import numpy, theano
 #from copy import copy as python_copy
@@ -1396,10 +1397,14 @@ class MaxAndArgmax(Op):
     def __hash__(self):
         return hash(type(self))
 
-    def make_node(self, x, axis=None):
+    def make_node(self, x, axis='DEFAULT'):
         x = _as_tensor_variable(x)
-        if axis is None:
+        if axis=='DEFAULT':
+            axis=x.type.ndim - 1
+            warnings.warn("The default axis of MaxAndArgmax will change! Now we return the max and the armax over the last dimensions. It will change to be the same as numpy: the max and argmax over all dimensions. To hide this warning and be compatible with the future behavior, set axis to -1 to have the current behavior. MaxAndArgmax currently support axis over only 1 dimensions, so you must flatten the tensor to have the futur behavior.")
+        elif axis is None:
             axis = x.type.ndim - 1
+            warnings.warn("The behavior of MaxAndArgmax when axis==None will change! Now we return the max and argmax over the last dimensions. It will change to the max and argmax over all dimensions as numpy. To hide this warning and be compatible with the future behavior, set axis to -1 to have the current behavior. MaxAndArgmax currently support axis over only 1 dimensions, so you must flatten the tensor to have the futur behavior.")
         if isinstance(axis,int):
             axis = [axis]
         elif isinstance(axis,(tuple,list)):
@@ -1462,15 +1467,21 @@ def max_and_argmax(a):
 
 
 @constructor
-def max(x, axis=None):
+def max(x, axis='DEFAULT'):
     """
     Return maximum elements obtained by iterating over given axis
 
-    Default axis is the last one.
+    Default axis is the last one. This will change.
 
     :note: we return an error as numpy when we reduce a dim with a shape of 0
     :note2: see MaxAndArgmax note for a difference between numpy and theano when axis==None
     """
+    if axis=='DEFAULT':
+        axis = x.type.ndim - 1
+        warnings.warn("The default axis of max will change! Now we return the max over the last dimensions. It will change to be the same as numpy: the max over all dimensions. To hide this warning and be compatible with the future behavior, set axis to -1 to have the current behavior. To have the futur behavior set axis to range(nb dim), but this don't support the grad. To have the grad, you must flatten the tensor before calling max().")
+    elif axis is None:
+        axis = x.type.ndim - 1
+        warnings.warn("The behavior of max when axis==None will change! Now we return the max over the last dimensions. It will change to the max over all dimensions as numpy. To hide this warning and be compatible with the future behavior, set axis to -1 to have the current behavior. To have the futur behavior set axis to range(nb dim), but this don't support the grad. To have the grad, you must flatten the tensor before calling max().")
     if isinstance(axis,(list,tuple)) and len(axis)>1:
        return CAReduce(scal.maximum,axis)(x)
     try:
@@ -1480,19 +1491,31 @@ def max(x, axis=None):
       return max_and_argmax(x,axis)[0]
 
 @constructor
-def argmax(x, axis=None):
+def argmax(x, axis='DEFAULT'):
     """
     Return indexes of maximum elements obtained by iterating over given axis
 
-    Default axis is the last one.
+    Default axis is the last one. This will change.
     """
+    if axis=='DEFAULT':
+        axis = x.type.ndim - 1
+        warnings.warn("The default axis of argmax will change! Now we return the argmax over the last dimensions. It will change to be the same as numpy: the argmax over all dimensions. To hide this warning and be compatible with the future behavior, set axis to -1 to have the current behavior. To have the futur behavior, you must flatten the tensor before calling max().")
+    elif axis is None:
+        axis = x.type.ndim - 1
+        warnings.warn("The behavior of argmax when axis==None will change! Now we return the argmax over the last dimensions. It will change to the argmax over all dimensions as numpy. To hide this warning and be compatible with the future behavior, set axis to -1 to have the current behavior. To have the futur behavior, you must flatten the tensor before calling argmax().")
     # In python (using MaxAndArgmax.perform()) this leads to an wasteful
     # implementation that goes through the data twice instead of once
     # but when Argmax.c_impl() is in place, it should be fine.
     return max_and_argmax(x,axis)[1]
 
 @constructor
-def min(x, axis=None):
+def min(x, axis='DEFAULT'):
+    if axis=='DEFAULT':
+        axis = x.type.ndim - 1
+        warnings.warn("The default axis of min will change! Now we return the min over the last dimensions. It will change to be the same as numpy: the min over all dimensions. To hide this warning and be compatible with the future behavior, set axis to -1 to have the current behavior. To have the futur behavior set axis to range(nb dim), but this don't support the grad. To have the grad, you must flatten the tensor before calling min().")
+    elif axis is None:
+        axis = x.type.ndim - 1
+        warnings.warn("The behavior of min when axis==None will change! Now we return the min over the last dimensions. It will change to the min over all dimensions as numpy. To hide this warning and be compatible with the future behavior, set axis to -1 to have the current behavior. To have the futur behavior set axis to range(nb dim), but this don't support the grad. To have the grad, you must flatten the tensor before calling min().")
     str_x_type = str(x.dtype)
     if str_x_type.startswith('float') or str_x_type.startswith('int'):
         return -max(-x, axis=axis)
@@ -1501,7 +1524,13 @@ def min(x, axis=None):
         raise NotImplementedError()
 
 @constructor
-def argmin(x, axis=None):
+def argmin(x, axis='DEFAULT'):
+    if axis=='DEFAULT':
+        axis = x.type.ndim - 1
+        warnings.warn("The default axis of argmin will change! Now we return the argmin over the last dimensions. It will change to be the same as numpy: the argmin over all dimensions. To hide this warning and be compatible with the future behavior, set axis to -1 to have the current behavior. To have the futur behavior, you must flatten the axis before calling argmin.")
+    elif axis is None:
+        axis = x.type.ndim - 1
+        warnings.warn("The behavior of argmin when axis==None will change! Now we return the argmin over the last dimensions. It will change to the argmin over all dimensions as numpy. To hide this warning and be compatible with the future behavior, set axis to -1 to have the current behavior. To have the futur behavior, you must flatten the axis before calling argmin.")
     str_x_type = str(x.dtype)
     if str_x_type.startswith('float') or str_x_type.startswith('int'):
         return argmax(-x, axis=axis)
@@ -4222,6 +4251,9 @@ def verify_grad(fun, pt, n_tests=2, rng=None, eps=None, abs_tol=None, rel_tol=No
         g_cost = cast(g_cost, o_output.dtype)
 
     symbolic_grad = grad(cost, tensor_pt, g_cost)
+    #if o_output.dtype in ['float32','float64']:
+    #    assert all([x.dtype == o_output.dtype for x in symbolic_grad]),("Expected grad of type %s, got %s "%( symbolic_grad.dtype, o_output.dtyp))
+
     grad_fn = function(tensor_pt, symbolic_grad)
 
     for test_num in xrange(n_tests):