Added gradient case to test_get_rid_of_advanced_indexing_version_of_xent

theano/tensor/tests/test_nnet.py

@@ -319,13 +319,19 @@ def test_asymptotic_32():
 
 def test_get_rid_of_advanced_indexing_version_of_xent():
 
-    rng = numpy.random.RandomState(234343)
+    rng = numpy.random.RandomState(utt.fetch_seed())
 
     x_val = rng.randn(3,5)
     y_val = numpy.asarray([2,4,1])
 
-    y = T.lvector('y')
     x = T.dmatrix('x')
+    y = T.lvector('y')
+
+    expressions_to_test = [
+            T.sum(-T.log(softmax(x)[T.arange(y.shape[0]), y])),
+            -T.sum(T.log(softmax(x)[T.arange(y.shape[0]), y])),
+            -T.sum(T.log(softmax(x))[T.arange(y.shape[0]), y]),
+            T.sum(-T.log(softmax(x))[T.arange(y.shape[0]), y])]
 
     def assert_optimizer_worked(expr):
         f = theano.function([x,y], expr)
@@ -333,21 +339,22 @@ def test_get_rid_of_advanced_indexing_version_of_xent():
             print i, node
         f(x_val, y_val)
         assert len(f.maker.env.toposort()) == 4
-    for expr in [
-            T.sum(-T.log(softmax(x)[T.arange(y.shape[0]), y])),
-            -T.sum(T.log(softmax(x)[T.arange(y.shape[0]), y])),
-            -T.sum(T.log(softmax(x))[T.arange(y.shape[0]), y]),
-            T.sum(-T.log(softmax(x))[T.arange(y.shape[0]), y])]:
+    for expr in expressions_to_test:
         assert_optimizer_worked(expr)
 
+    ## Gradient wrt x
+    for expr in expressions_to_test:
+        grad_x = T.grad(expr, x)
+        g = theano.function([x, y], grad_x)
+        for i, node in enumerate(g.maker.env.toposort()):
+            print i, node
+        g(x_val, y_val)
+        assert len(g.maker.env.toposort()) == 4
 
-    #TODO: Case 
 
     #TODO: Case with bias
     #   hint - call local_softmax_with_bias from within the other optimization
     #   hint - call the argmax push-down optimization first too
 
-    #TODO: Case with derivative
-
 if __name__ == '__main__':
     unittest.main()