don't accept the DEFAULT value for axis and update the docstring for…

theano/tensor/basic.py

@@ -1874,19 +1874,6 @@ class MaxAndArgmax(Op):
 
     def make_node(self, x, axis=None):
         x = _as_tensor_variable(x)
-        if x.type.ndim <= 1 and axis in ('DEFAULT', None):
-            # The old and new behavior are not different.
-            axis = 0
-        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."),
-            stacklevel=3)
         if isinstance(axis,int):
             axis = [axis]
         elif isinstance(axis,(tuple,list)):
@@ -1982,24 +1969,10 @@ def max(x, axis=None):
     """
     Return maximum elements obtained by iterating over given axis
 
-    Default axis is the last one. This will change.
+    Default axis is None: sum over all dimensions.
 
     :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 x.type.ndim <= 1 and axis in ('DEFAULT', None):
-        # The old and new behavior are not different.
-        axis = 0
-    elif 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()."),
-        stacklevel=2)
     if isinstance(axis,(list,tuple)) and len(axis)>1:
         return CAReduce(scal.maximum,axis)(x)
     try:
@@ -2013,20 +1986,8 @@ def argmax(x, axis=None):
     """
     Return indexes of maximum elements obtained by iterating over given axis
 
-    Default axis is the last one. This will change.
+    Default axis is None: sum over all dimensions.
     """
-    if x.type.ndim <= 1 and axis in ('DEFAULT', None):
-        # The old and new behavior are not different.
-        axis = 0
-    elif 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()."),
-        stacklevel=2)
     # 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.
@@ -2034,19 +1995,6 @@ def argmax(x, axis=None):
 
 @constructor
 def min(x, axis=None):
-    if x.type.ndim <= 1 and axis in ('DEFAULT', None):
-        # The old and new behavior are not different.
-        axis = 0
-    elif 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 future behavior, set axis to range(x.ndim), but "
-        "this does not support the grad. To be able to get the grad, you must "
-        "flatten the tensor before calling min()."),
-        stacklevel=2)
     str_x_type = str(x.dtype)
     if str_x_type.startswith('float') or str_x_type in int_dtypes:
         return -max(-x, axis=axis)
@@ -2056,18 +2004,6 @@ def min(x, axis=None):
 
 @constructor
 def argmin(x, axis=None):
-    if x.type.ndim <= 1 and axis in ('DEFAULT', None):
-        # The old and new behavior are not different.
-        axis = 0
-    elif 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."),
-        stacklevel=2)
     str_x_type = str(x.dtype)
     if str_x_type.startswith('float') or str_x_type in int_dtypes:
         return argmax(-x, axis=axis)