At olivier's advice, uncommented MaxAndArgmax.grad

tensor.py

@@ -519,10 +519,16 @@ class MaxAndArgmax(Op):
     def perform(self, node, (x, axis), (max, max_idx)):
         max[0] = numpy.max(x, axis)
         max_idx[0] = numpy.argmax(x, axis)
-#     def grad(self, (x, axis), (g_max, g_max_idx)):
-#         # This only works if axis is 0, else the max is broadcasted wrong in the call to eq
-#         g_x = eq(max(x, axis), x) * g_max
-#         return g_x, None
+    def grad(self, (x, axis), (g_max, g_max_idx)):
+#        (x, y), (gz, gw)
+#        gz*dz/dx + gw*dw/dx, gz*dz/dy + gw*dw/dy
+#        gMax * dMax/dx + gArgMax * dArgMax/dx, gMax * dMax/daxis + gArgMax * dArgMax/daxis
+#       g_max has one less dimension than x, so you need to complete g_max to x's shape
+#        when axis=0 the broadcasting mechanism does it automatically
+        # This only works if axis is 0, else the max is broadcasted wrong in the call to eq
+        assert axis.data == 0
+        g_x = eq(max(x, axis), x) * g_max
+        return g_x, None
 max_and_argmax = MaxAndArgmax()