coding style improved for the new test following Pascal's suggestions

theano/tests/test_scan.py

@@ -1067,14 +1067,9 @@ class T_Scan(unittest.TestCase):
 
     def caching_nsteps_by_scan_op(self):
 
-        import theano
-        import theano.tensor as T
-        import scipy
-
-
-        W = T.matrix('weights')
-        initial = T.vector('initial')
-        inpt = T.matrix('inpt')
+        W       = theano.tensor.matrix('weights')
+        initial = theano.tensor.vector('initial')
+        inpt    = theano.tensor.matrix('inpt')
 
         def one_step(x_t, h_tm1, W):
           expr = T.dot(h_tm1, W) + x_t
@@ -1086,30 +1081,29 @@ class T_Scan(unittest.TestCase):
           outputs_info=[initial],
           non_sequences=[W])
 
+        floatX = theano.config.floatX
         sh = expr.shape[0]
-
+        init_val = theano.shared( numpy.ones(5, dtype=floatX))
+        inpt_val = theano.shared( numpy.ones((5,5), dtype=floatX))
         shapef = theano.function([W], expr,
-                                 givens={initial: theano.shared(
-                                     scipy.ones(5,
-                                                dtype=theano.config.floatX)),
-                                         inpt: theano.shared(
-                                             scipy.ones((5, 5),
-                                                       dtype=theano.config.floatX))})
+                                 givens={initial: init_val,
+                                         inpt: inpt_val })
         # First execution to cache n_steps
-        shapef(scipy.ones((5, 5), dtype=theano.config.floatX))
+        val0 = numpy.ones((5,5), dtype = floatX)
+        shapef(val0)
 
 
         cost = expr.sum()
         d_cost_wrt_W = T.grad(cost, [W])
+        init_val = theano.shared( numpy.zeros(5, dtype =floatX))
         f = theano.function([W, inpt], d_cost_wrt_W,
-                             givens={initial: theano.shared(scipy.zeros(5))})
-
-        rval = numpy.asarray([[5187989]*5]*5, dtype = theano.config.floatX)
-        assert numpy.allclose( f(scipy.ones((5, 5),
-                                            dtype=theano.config.floatX)
-                                 , scipy.ones((10, 5),
-                                              dtype=theano.config.floatX))
-                              ,rval)
+                             givens={initial: init_val})
+
+        rval = numpy.asarray([[5187989]*5]*5, dtype = floatX)
+        x = numpy.ones((5,5), dtype = floatX)
+        y = numpy.ones((10,5), dtype = floatX)
+        t_rval = f( x,y)
+        assert numpy.allclose( t_rval, rval)