Add new tests for In wrapped

theano/compile/tests/test_function.py

@@ -34,22 +34,35 @@ class TestFunctionIn(unittest.TestCase):
 
     def test_in_strict(self):
 
-            a = theano.tensor.dvector()
-            b = theano.shared(7)
-            out = a + b
-
-            f = theano.function([In(a, strict=False)], out)
-            # works, rand generates float64 by default
-            f(numpy.random.rand(8))
-            # works, casting is allowed
+        a = theano.tensor.dvector()
+        b = theano.shared(7)
+        out = a + b
+
+        f = theano.function([In(a, strict=False)], out)
+        # works, rand generates float64 by default
+        f(numpy.random.rand(8))
+        # works, casting is allowed
+        f(numpy.array([1, 2, 3, 4], dtype='int32'))
+
+        f = theano.function([In(a, strict=True)], out)
+        try:
+            # fails, f expects float64
             f(numpy.array([1, 2, 3, 4], dtype='int32'))
-
-            f = theano.function([In(a, strict=True)], out)
-            try:
-                # fails, f expects float64
-                f(numpy.array([1, 2, 3, 4], dtype='int32'))
-            except TypeError:
-                pass
+        except TypeError:
+            pass
+
+    def test_explicit_shared_input(self):
+        # This is not a test of the In class per se, but the In class relies
+        # on the fact that shared variables cannot be explicit inputs
+        a = theano.shared(1.0)
+        self.assertRaises(TypeError, theano.function, [a], a + 1)
+
+    def test_in_shared_variable(self):
+        # Ensure that an error is raised if the In wrapped is used to wrap
+        # a shared variable
+        a = theano.shared(1.0)
+        a_wrapped = In(a, update=a+1)
+        self.assertRaises(TypeError, theano.function, [a_wrapped])
 
     def test_in_mutable(self):
         a = theano.tensor.dvector()
@@ -71,7 +84,8 @@ class TestFunctionIn(unittest.TestCase):
 
     def test_in_update(self):
         a = theano.tensor.dscalar('a')
-        f = theano.function([In(a, value=0.0, update=a+1)], a, mode='FAST_RUN')
+        f = theano.function([In(a, value=0.0, update=a + 1)], a,
+                            mode='FAST_RUN')
 
         # Ensure that, through the executions of the function, the state of the
         # input is persistent and is updated as it should