added a unit test for strict mode

theano/scan_module/tests/test_scan.py

@@ -46,11 +46,11 @@ else:
 mode_with_gpu = mode_with_opt.including('gpu', 'scan')
 
 
+type_eps = {'float64': 1e-7,
+            'float32': 3e-3}
+
 class multiple_outputs_numeric_grad:
     """WRITEME"""
-    type_eps = {'float64': 1e-7,
-                'float32': 3e-3}
-
     def __init__(self, f, pt, ndarray_mask=None, eps=None):
         """Return the gradient of f at pt.
 
@@ -3897,6 +3897,42 @@ class T_Scan(unittest.TestCase):
         f = theano.function([W, n_steps], H)
         f(numpy.ones((8,), dtype='float32'), 1)
 
+    def test_strict_mode(self):
+        n = 10
+
+        w = numpy.array([[-1,2],[3,-4]]).astype(theano.config.floatX)
+        w_ = theano.shared(w)
+        x0 = numpy.array([1,2]).astype(theano.config.floatX)
+        x0_ = tensor.vector(name='x0', dtype=theano.config.floatX)
+
+        def _scan_loose(x):
+            return tensor.dot(x, w_)
+
+        def _scan_strict(x, w_ns):
+            return tensor.dot(x, w_ns)
+
+        ret_loose = theano.scan(_scan_loose, 
+                              sequences=[],
+                              outputs_info=[x0_],
+                              n_steps=n,
+                              strict=False)
+        f_loose = theano.function([x0_], ret_loose[0][-1])
+
+        ret_strict = theano.scan(_scan_strict, 
+                               sequences=[],
+                               outputs_info=[x0_],
+                               non_sequences=[w_],
+                               n_steps=n,
+                               strict=True)
+        f_strict = theano.function([x0_], ret_strict[0][-1])
+
+        result_loose = f_loose(x0)
+        result_strict = f_strict(x0)
+
+        diff = (abs(result_loose - result_strict)).mean()
+
+        assert diff <= type_eps[theano.config.floatX]
+
 
 def test_speed():
     #