removed the use of TT and replaced with tensor

theano/scan_module/tests/test_scan.py

@@ -9,7 +9,6 @@ from theano import tensor
 from theano.tests  import unittest_tools as utt
 from theano.compile.pfunc import rebuild_collect_shared
 
-import theano.tensor as TT
 
 
 '''
@@ -2201,12 +2200,12 @@ class T_Scan(unittest.TestCase):
 
 
     def test_pushout(self):
-        W1 = TT.matrix('W1')
-        W2 = TT.matrix('W2')
-        h0 = TT.vector('h0')
+        W1 = tensor.matrix('W1')
+        W2 = tensor.matrix('W2')
+        h0 = tensor.vector('h0')
 
         def lambda_fn(h, W1, W2):
-            return TT.dot(h, W1 + W2)
+            return tensor.dot(h, W1 + W2)
 
         o, _ = theano.scan(lambda_fn, outputs_info= h0,
                            non_sequences =[W1,W2],
@@ -2223,14 +2222,14 @@ class T_Scan(unittest.TestCase):
 
 
     def test_alloc_inputs1(self):
-        W1 = TT.matrix('W1')
-        W2 = TT.matrix('W2')
-        h0 = TT.vector('h0')
+        W1 = tensor.matrix('W1')
+        W2 = tensor.matrix('W2')
+        h0 = tensor.vector('h0')
 
         def lambda_fn(h, W1, W2):
-            return TT.dot(h, W1 * W2)
+            return tensor.dot(h, W1 * W2)
         o, _ = theano.scan(lambda_fn, outputs_info= h0,
-                           non_sequences =[W1,TT.zeros_like(W2)],
+                           non_sequences =[W1,tensor.zeros_like(W2)],
                            n_steps = 5)
 
         f = theano.function([h0,W1,W2], o)
@@ -2242,17 +2241,17 @@ class T_Scan(unittest.TestCase):
 
 
     def test_alloc_inputs2(self):
-        W1 = TT.matrix()
-        W2 = TT.matrix()
-        h0 = TT.vector()
+        W1 = tensor.matrix()
+        W2 = tensor.matrix()
+        h0 = tensor.vector()
 
         def lambda_fn(W1,h, W2):
-            return W1 * TT.dot(h, W2)
+            return W1 * tensor.dot(h, W2)
 
         o, _ = theano.scan(lambda_fn,
-                           sequences = TT.zeros_like(W1),
+                           sequences = tensor.zeros_like(W1),
                            outputs_info= h0,
-                           non_sequences =[TT.zeros_like(W2)],
+                           non_sequences =[tensor.zeros_like(W2)],
                            n_steps = 5)
 
         f = theano.function([h0,W1,W2], o)
@@ -2266,21 +2265,21 @@ class T_Scan(unittest.TestCase):
 
 
     def test_alloc_inputs3(self):
-        _W1 = TT.matrix()
-        _W2 = TT.matrix()
-        _h0 = TT.vector()
+        _W1 = tensor.matrix()
+        _W2 = tensor.matrix()
+        _h0 = tensor.vector()
 
-        W1 = TT.specify_shape(_W1, (3,3))
-        W2 = TT.specify_shape(_W2, (3,3))
-        h0 = TT.specify_shape(_h0, (3,))
+        W1 = tensor.specify_shape(_W1, (3,3))
+        W2 = tensor.specify_shape(_W2, (3,3))
+        h0 = tensor.specify_shape(_h0, (3,))
 
         def lambda_fn(W1,h, W2):
-            return W1 * TT.dot(h, W2)
+            return W1 * tensor.dot(h, W2)
 
         o, _ = theano.scan(lambda_fn,
-                           sequences = TT.zeros_like(W1),
+                           sequences = tensor.zeros_like(W1),
                            outputs_info= h0,
-                           non_sequences =[TT.zeros_like(W2)],
+                           non_sequences =[tensor.zeros_like(W2)],
                            n_steps = 5)
 
         f = theano.function([_h0,_W1,_W2], o)
@@ -2292,7 +2291,7 @@ class T_Scan(unittest.TestCase):
 
 
     def test_while0(self):
-        x = TT.vector('x')
+        x = tensor.vector('x')
         def lambda_fn(x_t):
             return x_t+1, theano.until( x_t > 3)
         o, _ = theano.scan(lambda_fn, x)
@@ -2303,7 +2302,7 @@ class T_Scan(unittest.TestCase):
         assert numpy.sum(out[24:]) == 0
 
     def test_while1(self):
-        x = TT.vector('x')
+        x = tensor.vector('x')
         def lambda_fn(x_t):
             return x_t+1, theano.until( x_t > 3)
         o, _  = theano.scan(lambda_fn, x)
@@ -2322,7 +2321,7 @@ class T_Scan(unittest.TestCase):
 
 
     def test_while2(self):
-        x = TT.vector('x')
+        x = tensor.vector('x')
         def lambda_fn(x_t):
             return x_t+1, theano.until( x_t > 3)
         o, _  = theano.scan(lambda_fn, x)