Merge pull request #265 from delallea/fix_argmax_grad

theano/tensor/basic.py

@@ -1964,8 +1964,11 @@ class MaxAndArgmax(Op):
         # Raise the g_max and xmax to the same number of dim as the input.
         pattern = []
         out_dim = 0
-        for i in range(inp[0].ndim):
-            if i == axis.data:
+        if python_all(axis.data == range(x.ndim)):
+            # We are taking the max/argmax over all dimensions.
+            axis = None
+        for i in range(x.ndim):
+            if axis is None or i == axis.data:
                 pattern.append('x')
             else:
                 pattern.append(out_dim)

theano/tensor/tests/test_basic.py

@@ -1546,9 +1546,9 @@ class T_max_and_argmax(unittest.TestCase):
 
         def check_grad_max(data, max_grad_data, axis=None):
             """
-            Why this is needed? verify_grad is not enought?
+            Why this is needed? verify_grad is not enough?
             """
-            #This work only for axis in [0,None]
+            # This works only for axis in [0, None].
             assert axis in [0, None]
             z = numpy.zeros_like(data)
             z = z.flatten()
@@ -1563,29 +1563,22 @@ class T_max_and_argmax(unittest.TestCase):
             z = z.reshape(data.shape)
             assert numpy.all(max_grad_data == z)
 
-        #test grad of max
-        #axis is the last one
-        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])
-        check_grad_max(data, eval_outputs(grad(
-                    max_and_argmax(n, axis=0)[0].sum(), n)), axis=0)
-
-        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])
-        #check_grad_max(data,eval_outputs(grad(
-        #            max_and_argmax(n,axis=1)[0],n)),axis=1)
-
-        utt.verify_grad(lambda v: max_and_argmax(v.flatten())[0], [data])
-        utt.verify_grad(lambda v: max_and_argmax(v.flatten())[1], [data])
-        check_grad_max(data, eval_outputs(grad(
-                    max_and_argmax(n.flatten())[0], n)))
+        for axis in (-1, 0, 1, None):
+            for j in xrange(2):
+                utt.verify_grad(lambda v: max_and_argmax(v, axis=axis)[j],
+                                [data])
+                if axis != 1:
+                    utt.verify_grad(lambda v: max_and_argmax(v.flatten(),
+                                                             axis=axis)[j],
+                                    [data])
+            if axis in (0, None):
+                check_grad_max(data, eval_outputs(grad(
+                    max_and_argmax(n, axis=axis)[0].sum(), n)), axis=axis)
+            check_grad_max(data, eval_outputs(grad(
+                max_and_argmax(n.flatten())[0], n)))
 
         # Test 4d inner dimensions
         data = numpy.random.rand(2, 3, 4, 5)
-        n = as_tensor_variable(data)
         for i in [0, 1, 2, 3]:
             utt.verify_grad(lambda v: max_and_argmax(v, axis=[i])[0], [data])
             utt.verify_grad(lambda v: max_and_argmax(v, axis=[i])[1], [data])