Merge pull request #2624 from carriepl/scan_crash

theano/scan_module/scan_op.py

@@ -1931,9 +1931,10 @@ class Scan(PureOp):
                 type_outs.append(vl.type.why_null)
                 # Replace the inner output with a zero tensor of
                 # the right shape
-                inner_out_sitsot[_p] = tensor.zeros(
+                inner_out_nitsot[_p] = tensor.zeros(
                     diff_inputs[_p].shape,
                     dtype=theano.config.floatX)
+
             if through_shared:
                 type_outs.append('through_shared')
             elif disconnected_dC_dinps_t[_p]:

theano/scan_module/tests/test_scan.py

@@ -836,6 +836,36 @@ class T_Scan(unittest.TestCase):
                         n_steps=2)
         tensor.grad(a[-1], a0)
 
+    def test_grad_two_scans(self):
+
+        # data input & output
+        x = tensor.tensor3('x')
+        t = tensor.imatrix('t')
+
+        # forward pass
+        W = theano.shared(
+            numpy.random.randn(2, 2).astype('float32'),
+            name="W", borrow=True)
+
+        def forward_scanner(x_t):
+            a2_t = tensor.dot(x_t, W)
+            y_t = tensor.nnet.softmax(a2_t)
+            return y_t
+
+        y, _ = theano.scan(fn=forward_scanner, sequences=x,
+                           outputs_info=[None])
+
+        # loss function
+        def error_scanner(y_t, t_t):
+            return tensor.mean(tensor.nnet.categorical_crossentropy(y_t, t_t))
+
+        L, _ = theano.scan(fn=error_scanner, sequences=[y, t],
+                           outputs_info=[None])
+        L = tensor.mean(L)
+
+        # backward pass
+        gW = tensor.grad(L, [W])
+
     # simple rnn, one input, one state, weights for each; input/state are
     # vectors, weights are scalars; using shared variables and past
     # taps (sequences and outputs)