diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index e58473cf..0694509c 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -36,11 +36,16 @@ cuda_add_executable(
   validate_mnist_batch
   validate_mnist_batch.cu
 )
+cuda_add_executable(
+  validate_encoder_decoder
+  validate_encoder_decoder.cu
+)
 
 target_link_libraries(validate_mnist marian_lib)
 target_link_libraries(validate_mnist_batch marian_lib)
+target_link_libraries(validate_encoder_decoder marian_lib)
 
-foreach(exec marian train_mnist validate_mnist validate_mnist_batch )
+foreach(exec marian train_mnist validate_mnist validate_mnist_batch validate_encoder_decoder)
   target_link_libraries(${exec} ${EXT_LIBS} cuda cudnn)
   cuda_add_cublas_to_target(${exec})
   set_target_properties(${exec} PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
diff --git a/src/chainable.h b/src/chainable.h
index 9fe6d208..885efdbd 100644
--- a/src/chainable.h
+++ b/src/chainable.h
@@ -17,6 +17,8 @@ struct Chainable {
     virtual void set_zero_adjoint() { }
 
     virtual void allocate(size_t) = 0;
+    virtual std::string graphviz() = 0;
+
     
     virtual const Shape& shape() = 0;
     virtual DataType &val() = 0;
diff --git a/src/expression_graph.h b/src/expression_graph.h
index 8b7d74f4..a092fcb1 100644
--- a/src/expression_graph.h
+++ b/src/expression_graph.h
@@ -48,6 +48,18 @@ class ExpressionGraph {
         v->forward();    
     }
     
+    std::string graphviz() {
+      std::stringstream ss;
+      ss << "digraph ExpressionGraph {" << std::endl;
+      ss << "rankdir=BT" << std::endl;
+      
+      typedef typename ChainableStack::reverse_iterator It;
+      for(It it = stack_->rbegin(); it != stack_->rend(); ++it)
+        ss << (*it)->graphviz();
+      ss << "}" << std::endl;
+      return ss.str();
+    }
+    
     void backward() {
       for(auto&& v : *stack_)
         v->set_zero_adjoint();
diff --git a/src/node_operators.h b/src/node_operators.h
index e5cf2110..8620a645 100644
--- a/src/node_operators.h
+++ b/src/node_operators.h
@@ -22,6 +22,13 @@ struct InputNode : public Node {
 
   void forward() {}
   void backward() {}
+  
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"parallelogram\", label=\"input\", style=\"filled\", fillcolor=\"lawngreen\"]" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 struct ConstantNode : public Node {
@@ -35,6 +42,13 @@ struct ConstantNode : public Node {
 
   void forward() {}
   void backward() {}
+
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"diamond\", label=\"const\"]" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 struct ParamNode : public Node {
@@ -60,6 +74,13 @@ struct ParamNode : public Node {
     }
   }
 
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"hexagon\", label=\"param\", style=\"filled\", fillcolor=\"orangered\"]" << std::endl << std::endl;
+    return ss.str();
+  };
+
+  
   private:
     std::function<void(Tensor)> init_;
     bool initialized_;
@@ -89,6 +110,14 @@ struct LogitNodeOp : public UnaryNodeOp {
     Element(_1 += _2 * _3 * (1 - _3),
             a_->grad(), adj_, val_);
   }
+
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"logit\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
+    ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 struct TanhNodeOp : public UnaryNodeOp {
@@ -105,6 +134,14 @@ struct TanhNodeOp : public UnaryNodeOp {
     Element(_1 += _2 * (1 - _3 * _3),
             a_->grad(), adj_, val_);
   }
+
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"tanh\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
+    ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 // @TODO, make this numerically safe(r):
@@ -131,6 +168,14 @@ struct SoftmaxNodeOp : public UnaryNodeOp {
     SubtractMean(&result, val_);
     Element(_1 += _2 *  _3, a_->grad(), val_, result);
   }
+
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"softmax\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
+    ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 struct LogNodeOp : public UnaryNodeOp {
@@ -146,6 +191,14 @@ struct LogNodeOp : public UnaryNodeOp {
     Element(_1 += _2 * 1.f / _3,
             a_->grad(), adj_, a_->val());
   }
+  
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"log\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
+    ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 struct ExpNodeOp : public UnaryNodeOp {
@@ -161,6 +214,14 @@ struct ExpNodeOp : public UnaryNodeOp {
     Element(_1 += _2 * Exp(_3),
             a_->grad(), adj_, a_->val());
   }
+  
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"exp\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
+    ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 struct NegNodeOp : public UnaryNodeOp {
@@ -175,6 +236,14 @@ struct NegNodeOp : public UnaryNodeOp {
   void backward() {
     Element(_1 += -_2, a_->grad(), adj_);
   }
+  
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"-\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
+    ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 /******************************************************/
@@ -220,6 +289,15 @@ struct DotNodeOp : public BinaryNodeOp {
     Prod(a_->grad(), adj_, b_->val(), false, true, 1.0);
     Prod(b_->grad(), a_->val(), adj_, true, false, 1.0);
   }
+  
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"×\", style=\"filled\", fillcolor=\"orange\"]" << std::endl;
+    ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl;
+    ss << "\"" << &*b_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 struct PlusNodeOp : public BinaryNodeOp {
@@ -238,6 +316,15 @@ struct PlusNodeOp : public BinaryNodeOp {
     Element(_1 += _2,
             b_->grad(), adj_);
   }
+
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"+\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
+    ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl;
+    ss << "\"" << &*b_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 struct MinusNodeOp : public BinaryNodeOp {
@@ -256,6 +343,15 @@ struct MinusNodeOp : public BinaryNodeOp {
     Element(_1 -= _2,
             b_->grad(), adj_);
   }
+  
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"-\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
+    ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl;
+    ss << "\"" << &*b_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 struct MultNodeOp : public BinaryNodeOp {
@@ -274,6 +370,15 @@ struct MultNodeOp : public BinaryNodeOp {
     Element(_1 += _2 * _3,
             b_->grad(), adj_, a_->val());
   }
+  
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"•\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
+    ss << "\"" << &*a_ << "\" -> \"" << this << "\"" << std::endl;
+    ss << "\"" << &*b_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 struct DivNodeOp : public BinaryNodeOp {
@@ -291,7 +396,16 @@ struct DivNodeOp : public BinaryNodeOp {
             a_->grad(), adj_, b_->val());
     Element(_1 -= _2 * _3 / (_4 * _4),
             b_->grad(), adj_, a_->val(), b_->val());
-  }  
+  }
+  
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"÷\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
+    ss << "\"" << a_ << "\" -> \"" << this << "\"" << std::endl;
+    ss << "\"" << b_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
 };
 
 }
diff --git a/src/test.cu b/src/test.cu
index ba798715..8d7073f4 100644
--- a/src/test.cu
+++ b/src/test.cu
@@ -90,6 +90,8 @@ int main(int argc, char** argv) {
     Y[t] = Yt;
   }
 
+  std::cout << g.graphviz() << std::endl;
+  
   g.forward(batch_size);
   g.backward();
 
diff --git a/src/validate_encoder_decoder.cu b/src/validate_encoder_decoder.cu
new file mode 100644
index 00000000..b053c0ea
--- /dev/null
+++ b/src/validate_encoder_decoder.cu
@@ -0,0 +1,142 @@
+
+#include "marian.h"
+#include "mnist.h"
+
+#if 0
+ExpressionGraph build_graph() {
+  std::cerr << "Loading model params...";
+}
+
+  // read parallel corpus from file
+  std::fstream sourceFile("../examples/mt/dev/newstest2013.de");
+  std::fstream targetFile("../examples/mt/dev/newstest2013.en");
+
+  std::string sourceLine, targetLine;
+  while (getline(sourceFile, sourceLine)) {
+    getline(targetFile, targetLine);
+    std::vector<size_t> sourceIds = sourceVocab.ProcessSentence(sourceLine);
+    std::vector<size_t> targetIds = sourceVocab.ProcessSentence(targetLine);
+  }
+#endif
+
+
+int main(int argc, char** argv) {
+
+  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(0);
+  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");
+
+  // For the stop symbol.
+  for (int t = 0; t <= num_inputs; ++t) {
+    Tensor Xt({batch_size, input_size});
+
+    float max = 1.;
+    std::vector<float> values(batch_size * input_size);
+    std::vector<float> classes(batch_size * output_size, 0.0);
+    int k = 0;
+    for (int i = 0; i < batch_size; ++i) {
+      for (int j = 0; j < input_size; ++j, ++k) {
+         values[k] = max * (2.0*static_cast<float>(rand()) / RAND_MAX - 1.0);
+      }
+    }
+
+    thrust::copy(values.begin(), values.end(), Xt.begin());
+
+    *X[t] = Xt;
+  }
+
+  for (int t = 0; t < num_outputs; ++t) {
+    Tensor Yt({batch_size, output_size});
+
+    std::vector<float> classes(batch_size * output_size, 0.0);
+    int l = 0;
+    for (int i = 0; i < batch_size; ++i) {
+      int gold = output_size * static_cast<float>(rand()) / RAND_MAX;
+      classes[l + gold] = 1.0;
+      l += output_size;
+    }
+
+    thrust::copy(classes.begin(), classes.end(), Yt.begin());
+
+    *Y[t] = Yt;
+  }
+
+  g.forward(batch_size);
+  g.backward();
+
+  std::cerr << graph.val().Debug() << std::endl;
+
+  std::cerr << X[0]->val().Debug() << std::endl;
+  std::cerr << Y[0]->val().Debug() << std::endl;
+
+  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;
+
+  return 0;
+}
diff --git a/src/validate_mnist.cu b/src/validate_mnist.cu
index 36f0135a..449fc193 100644
--- a/src/validate_mnist.cu
+++ b/src/validate_mnist.cu
@@ -10,7 +10,7 @@ const size_t IMAGE_SIZE = 784;
 const size_t LABEL_SIZE = 10;
 int BATCH_SIZE = 10000;
 
-ExpressionGraph build_graph(int cudaDevice) {
+ExpressionGraph build_graph() {
   std::cerr << "Loading model params...";
   NpzConverter converter("../scripts/test_model_single/model.npz");
 
@@ -22,7 +22,7 @@ ExpressionGraph build_graph(int cudaDevice) {
 
   std::cerr << "Building model...";
   
-  ExpressionGraph g(cudaDevice);
+  ExpressionGraph g(0);
   auto x = named(g.input(shape={whatevs, IMAGE_SIZE}), "x");
   auto y = named(g.input(shape={whatevs, LABEL_SIZE}), "y");
   
@@ -46,12 +46,15 @@ ExpressionGraph build_graph(int cudaDevice) {
 }
 
 int main(int argc, char** argv) {
+  
+  cudaSetDevice(0);
+    
   std::cerr << "Loading test set...";
   std::vector<float> testImages = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", BATCH_SIZE, IMAGE_SIZE);
   std::vector<float> testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", BATCH_SIZE, LABEL_SIZE);
   std::cerr << "Done." << std::endl;
 
-  ExpressionGraph g = build_graph(1);
+  ExpressionGraph g = build_graph();
   
   Tensor xt({BATCH_SIZE, IMAGE_SIZE});
   Tensor yt({BATCH_SIZE, LABEL_SIZE});
@@ -59,6 +62,8 @@ int main(int argc, char** argv) {
   g["x"] = (xt << testImages);
   g["y"] = (yt << testLabels);
   
+  std::cout << g.graphviz() << std::endl;
+  
   g.forward(BATCH_SIZE);
  
   std::vector<float> results;