diff --git a/src/validate_encoder_decoder.cu b/src/validate_encoder_decoder.cu
index c7be225a..d8eea261 100644
--- a/src/validate_encoder_decoder.cu
+++ b/src/validate_encoder_decoder.cu
@@ -2,11 +2,78 @@
 #include "marian.h"
 #include "mnist.h"
 
-#if 0
-ExpressionGraph build_graph() {
-  std::cerr << "Loading model params...";
+using namespace marian;
+using namespace keywords;
+
+const int input_size = 10;
+const int output_size = 15;
+const int batch_size = 25;
+const int hidden_size = 5;
+const int num_inputs = 8;
+const int num_outputs = 6;
+
+ExpressionGraph build_graph(int cuda_device) {
+  std::cerr << "Building computation graph..." << std::endl;
+
+  ExpressionGraph g(cuda_device);
+  std::vector<Expr> X, Y, H, S;
+
+  // For the stop symbol.
+  for (int t = 0; t <= num_inputs; ++t) {
+    std::stringstream ss;
+    ss << "X" << t;
+    X.emplace_back(named(g.input(shape={batch_size, input_size}), ss.str()));
+  }
+
+  // For the stop symbol.
+  for (int t = 0; t <= num_outputs; ++t) {
+    std::stringstream ss;
+    ss << "Y" << t;
+    Y.emplace_back(named(g.input(shape={batch_size, output_size}), ss.str()));
+  }
+
+  Expr Wxh = g.param(shape={input_size, hidden_size}, init=uniform(), name="Wxh");
+  Expr Whh = g.param(shape={hidden_size, hidden_size}, init=uniform(), name="Whh");
+  Expr bh = g.param(shape={1, hidden_size}, init=uniform(), name="bh");
+  Expr h0 = g.param(shape={1, hidden_size}, init=uniform(), name="h0");
+
+  std::cerr << "Building encoder RNN..." << std::endl;
+  H.emplace_back(tanh(dot(X[0], Wxh) + dot(h0, Whh) + bh));
+  for (int t = 1; t <= num_inputs; ++t) {
+    H.emplace_back(tanh(dot(X[t], Wxh) + dot(H[t-1], Whh) + bh));
+  }
+
+  Expr Wxh_d = g.param(shape={output_size, hidden_size}, init=uniform(), name="Wxh_d");
+  Expr Whh_d = g.param(shape={hidden_size, hidden_size}, init=uniform(), name="Whh_d");
+  Expr bh_d = g.param(shape={1, hidden_size}, init=uniform(), name="bh_d");
+
+  std::cerr << "Building decoder RNN..." << std::endl;
+  auto h0_d = H[num_inputs];
+  S.emplace_back(tanh(dot(Y[0], Wxh_d) + dot(h0_d, Whh_d) + bh_d));
+  for (int t = 1; t < num_outputs; ++t) {
+    S.emplace_back(tanh(dot(Y[t], Wxh_d) + dot(S[t-1], Whh_d) + bh_d));
+  }
+
+  Expr Why = g.param(shape={hidden_size, output_size}, init=uniform(), name="Why");
+  Expr by = g.param(shape={1, output_size}, init=uniform(), name="by");
+
+  std::cerr << "Building output layer..." << std::endl;
+  std::vector<Expr> Yp;
+
+  Yp.emplace_back(named(softmax_fast(dot(h0_d, Why) + by), "pred"));
+  Expr cross_entropy = sum(Y[0] * log(Yp[0]), axis=1);
+  for (int t = 1; t <= num_outputs; ++t) {
+    Yp.emplace_back(named(softmax_fast(dot(S[t-1], Why) + by), "pred"));
+    cross_entropy = cross_entropy + sum(Y[t] * log(Yp[t]), axis=1);
+  }
+  auto graph = -mean(cross_entropy, axis=0, name="cost");
+
+  std::cerr << "Done." << std::endl;
+
+  return g;
 }
 
+#if 0
   // read parallel corpus from file
   std::fstream sourceFile("../examples/mt/dev/newstest2013.de");
   std::fstream targetFile("../examples/mt/dev/newstest2013.en");
@@ -21,73 +88,8 @@ ExpressionGraph build_graph() {
 
 
 int main(int argc, char** argv) {
-  cudaSetDevice(0);
 
-  using namespace marian;
-  using namespace keywords;
-
-  int input_size = 10;
-  int output_size = 15;
-  int batch_size = 25;
-  int hidden_size = 5;
-  int num_inputs = 8;
-  int num_outputs = 6;
-
-  ExpressionGraph g;
-  std::vector<Expr*> X(num_inputs+1); // For the stop symbol.
-  std::vector<Expr*> Y(num_outputs);
-  std::vector<Expr*> H(num_inputs+1); // For the stop symbol.
-  std::vector<Expr*> S(num_outputs);
-
-  // For the stop symbol.
-  for (int t = 0; t <= num_inputs; ++t) {
-    X[t] = new Expr(g.input(shape={batch_size, input_size}));
-  }
-
-  // For the stop symbol.
-  for (int t = 0; t <= num_outputs; ++t) {
-    Y[t] = new Expr(g.input(shape={batch_size, output_size}));
-  }
-
-  Expr Wxh = g.param(shape={input_size, hidden_size}, init=uniform(), name="Wxh");
-  Expr Whh = g.param(shape={hidden_size, hidden_size}, init=uniform(), name="Whh");
-  Expr bh = g.param(shape={1, hidden_size}, init=uniform(), name="bh");
-  Expr h0 = g.param(shape={1, hidden_size}, init=uniform(), name="h0");
-
-  std::cerr << "Building encoder RNN..." << std::endl;
-  H[0] = new Expr(tanh(dot(*X[0], Wxh) + dot(h0, Whh) + bh));
-  for (int t = 1; t <= num_inputs; ++t) {
-    H[t] = new Expr(tanh(dot(*X[t], Wxh) + dot(*H[t-1], Whh) + bh));
-  }
-
-  Expr Wxh_d = g.param(shape={output_size, hidden_size}, init=uniform(), name="Wxh_d");
-  Expr Whh_d = g.param(shape={hidden_size, hidden_size}, init=uniform(), name="Whh_d");
-  Expr bh_d = g.param(shape={1, hidden_size}, init=uniform(), name="bh_d");
-
-  std::cerr << "Building decoder RNN..." << std::endl;
-  auto h0_d = *H[num_inputs];
-  S[0] = new Expr(tanh(dot(*Y[0], Wxh_d) + dot(h0_d, Whh_d) + bh_d));
-  for (int t = 1; t < num_outputs; ++t) {
-    S[t] = new Expr(tanh(dot(*Y[t], Wxh_d) + dot(*S[t-1], Whh_d) + bh_d));
-  }
-
-  Expr Why = g.param(shape={hidden_size, output_size}, init=uniform(), name="Why");
-  Expr by = g.param(shape={1, output_size}, init=uniform(), name="by");
-
-  std::cerr << "Building output layer..." << std::endl;
-  std::vector<Expr*> Yp(num_outputs+1); // For the stop symbol.
-
-  Expr* cross_entropy = NULL;
-  for (int t = 0; t <= num_outputs; ++t) {
-    if (t == 0) {
-      Yp[t] = new Expr(named(softmax_fast(dot(h0_d, Why) + by), "pred"));
-      cross_entropy = new Expr(sum(*Y[t] * log(*Yp[t]), axis=1));
-    } else {
-      Yp[t] = new Expr(named(softmax_fast(dot(*S[t-1], Why) + by), "pred"));
-      *cross_entropy = *cross_entropy + sum(*Y[t] * log(*Yp[t]), axis=1);
-    }
-  }
-  auto graph = -mean(*cross_entropy, axis=0, name="cost");
+  ExpressionGraph g = build_graph(0);
 
   // For the stop symbol.
   for (int t = 0; t <= num_inputs; ++t) {
@@ -105,10 +107,13 @@ int main(int argc, char** argv) {
 
     thrust::copy(values.begin(), values.end(), Xt.begin());
 
-    *X[t] = Xt;
+    std::stringstream ss;
+    ss << "X" << t;
+    g[ss.str()] = Xt;
+
   }
 
-  for (int t = 0; t < num_outputs; ++t) {
+  for (int t = 0; t <= num_outputs; ++t) {
     Tensor Yt({batch_size, output_size});
 
     std::vector<float> classes(batch_size * output_size, 0.0);
@@ -121,23 +126,33 @@ int main(int argc, char** argv) {
 
     thrust::copy(classes.begin(), classes.end(), Yt.begin());
 
-    *Y[t] = Yt;
+    std::stringstream ss;
+    ss << "Y" << t;
+    g[ss.str()] = Yt;
   }
 
+  std::cerr << "Graphviz step" << std::endl;
+  std::cout << g.graphviz() << std::endl;
+
+  std::cerr << "Forward step" << std::endl;
   g.forward(batch_size);
+  std::cerr << "Backward step" << std::endl;
   g.backward();
+  std::cerr << "Done" << std::endl;
 
-  std::cerr << graph.val().Debug() << std::endl;
+  std::cerr << g["graph"].val().Debug() << std::endl;
 
-  std::cerr << X[0]->val().Debug() << std::endl;
-  std::cerr << Y[0]->val().Debug() << std::endl;
+  std::cerr << g["X0"].val().Debug() << std::endl;
+  std::cerr << g["Y0"].val().Debug() << std::endl;
 
+#if 0
   std::cerr << Whh.grad().Debug() << std::endl;
   std::cerr << bh.grad().Debug() << std::endl;
   std::cerr << Why.grad().Debug() << std::endl;
   std::cerr << by.grad().Debug() << std::endl;
   std::cerr << Wxh.grad().Debug() << std::endl;
   std::cerr << h0.grad().Debug() << std::endl;
+#endif
 
   return 0;
 }