Explicitly calls min, max, etc. with axis=-1 to silence warning.

theano/tensor/nnet/nnet.py

@@ -890,7 +890,7 @@ def crossentropy_softmax_max_and_argmax_1hot_with_bias(x, b, y_idx, **kwargs):
     the appropriate information (i.e. the max probability)?
     """
     (xent, softmax) = crossentropy_softmax_1hot_with_bias(x, b, y_idx, **kwargs)
-    (max_pr, argmax) = tensor.max_and_argmax(softmax)
+    (max_pr, argmax) = tensor.max_and_argmax(softmax, axis=-1)
     return (xent, softmax, max_pr, argmax)
 def crossentropy_softmax_max_and_argmax_1hot(x, y_idx, **kwargs):
     b = tensor.zeros_like(x[0,:])

theano/tensor/tests/test_basic.py

@@ -875,10 +875,10 @@ class T_max_and_argmax(unittest.TestCase):
     def test2(self):
         data = numpy.random.rand(2,3)
         n = as_tensor_variable(data)
-        v,i = eval_outputs(max_and_argmax(n))
+        v,i = eval_outputs(max_and_argmax(n,-1))
         self.failUnless(numpy.all(v == numpy.max(data,-1)))
         self.failUnless(numpy.all(i == numpy.argmax(data,-1)))
-        v = eval_outputs(max_and_argmax(n)[0].shape)
+        v = eval_outputs(max_and_argmax(n,-1)[0].shape)
         assert v==(2)
 
     def test2b(self):
@@ -977,8 +977,8 @@ class T_max_and_argmax(unittest.TestCase):
 
         #test grad of max
         #axis is the last one
-        utt.verify_grad(lambda v: max_and_argmax(v)[0], [data])
-        utt.verify_grad(lambda v: max_and_argmax(v)[1], [data])
+        utt.verify_grad(lambda v: max_and_argmax(v,axis=-1)[0], [data])
+        utt.verify_grad(lambda v: max_and_argmax(v,axis=-1)[1], [data])
 
         utt.verify_grad(lambda v: max_and_argmax(v,axis=[0])[0], [data])
         utt.verify_grad(lambda v: max_and_argmax(v,axis=[0])[1], [data])
@@ -1022,9 +1022,9 @@ class T_argmin_argmax(unittest.TestCase):
         for fct,nfct in [(argmax,numpy.argmax),(argmin,numpy.argmin)]:
             data = numpy.random.rand(2,3)
             n = as_tensor_variable(data)
-            i = eval_outputs(fct(n))
+            i = eval_outputs(fct(n,-1))
             self.failUnless(numpy.all(i == nfct(data,-1)))
-            v = eval_outputs(fct(n).shape)
+            v = eval_outputs(fct(n,-1).shape)
             assert v==(2)
 
     def test2b(self):
@@ -1111,7 +1111,7 @@ class T_argmin_argmax(unittest.TestCase):
         n = as_tensor_variable(data)
 
         #test grad of argmin
-        utt.verify_grad(lambda v: argmin(v), [data])
+        utt.verify_grad(lambda v: argmin(v,axis=-1), [data])
 
         utt.verify_grad(lambda v: argmin(v,axis=[0]), [data])
 
@@ -1120,7 +1120,7 @@ class T_argmin_argmax(unittest.TestCase):
         utt.verify_grad(lambda v: argmin(v.flatten()), [data])
 
         try:
-            grad(argmin(n),n)
+            grad(argmin(n,axis=-1),n)
             raise Exception('Expected an error')
         except TypeError:
             pass
@@ -1130,7 +1130,7 @@ class T_argmin_argmax(unittest.TestCase):
         n = as_tensor_variable(data)
 
         #test grad of argmax
-        utt.verify_grad(lambda v: argmax(v), [data])
+        utt.verify_grad(lambda v: argmax(v, axis=-1), [data])
 
         utt.verify_grad(lambda v: argmax(v,axis=[0]), [data])
 
@@ -1139,7 +1139,7 @@ class T_argmin_argmax(unittest.TestCase):
         utt.verify_grad(lambda v: argmax(v.flatten()), [data])
 
         try:
-            grad(argmax(n),n)
+            grad(argmax(n, axis=-1),n)
             raise Exception('Expected an error')
         except TypeError:
             pass
@@ -1174,7 +1174,7 @@ class T_min_max(unittest.TestCase):
             v = eval_outputs(fct(n,-1))
             self.failUnless(numpy.all(v == nfct(data,-1)))
 
-            v = eval_outputs(fct(n).shape)
+            v = eval_outputs(fct(n,-1).shape)
             assert v==(2)
 
     def test2b(self):
@@ -1294,7 +1294,7 @@ class T_min_max(unittest.TestCase):
 
         #test grad of max
         #axis is the last one
-        utt.verify_grad(lambda v: max(v), [data])
+        utt.verify_grad(lambda v: max(v,axis=-1), [data])
 
         utt.verify_grad(lambda v: max(v,axis=[0]), [data])
         check_grad_max(data,eval_outputs(grad(max(n,axis=0).sum(),n)),axis=0)
@@ -1326,7 +1326,7 @@ class T_min_max(unittest.TestCase):
 
         #test grad of min
         #axis is the last one
-        utt.verify_grad(lambda v: min(v), [data])
+        utt.verify_grad(lambda v: min(v,axis=-1), [data])
 
         utt.verify_grad(lambda v: min(v,axis=[0]), [data])
         check_grad_min(data,eval_outputs(grad(min(n,axis=0).sum(),n)),axis=0)