Make mlp_test test a more sensible case.

Previous test was testing settings where we removed either the "local_track_shape_i" or "local_shape_to_shape_i" optimizations. These are unlikely settings, what we test now is the complete removal of the "ShapeOpt" optimization.

Make mlp_test test a more sensible case.
652db699 · Pascal Lamblin · 074a40d2 · 652db699
--- a/theano/tensor/tests/mlp_test.py
+++ b/theano/tensor/tests/mlp_test.py
@@ -306,27 +306,15 @@ def test_mlp():
    theano.printing.debugprint(train_model, print_type=True)
    assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.env.toposort()])

-    # Now, this case works, too!
-    train_model =theano.function( inputs = [index],
-            updates = updates2,
-            mode=mode.excluding('local_track_shape_i'),
-            givens={
-                x:train_set_x[index*batch_size:(index+1)*batch_size],
-                y:train_set_y[index*batch_size:(index+1)*batch_size]})
-    print
-    print 'MODEL 2'
-    theano.printing.debugprint(train_model, print_type=True)
-    assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.env.toposort()])
-
    # Even without FeatureShape
    train_model =theano.function( inputs = [index],
            updates = updates2,
-            mode=mode.excluding('local_shape_to_shape_i'),
+            mode=mode.excluding('ShapeOpt'),
            givens={
                x:train_set_x[index*batch_size:(index+1)*batch_size],
                y:train_set_y[index*batch_size:(index+1)*batch_size]})
    print
-    print 'MODEL 3'
+    print 'MODEL 2'
    theano.printing.debugprint(train_model, print_type=True)
    assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.env.toposort()])