fixed #632

src/TensorFlowNET.Core/Keras/Engine/Model.Train.cs

@@ -16,7 +16,7 @@ namespace Tensorflow.Keras.Engine
             var data = iterator.next();
             var outputs = train_step(data[0], data[1]);
             tf_with(ops.control_dependencies(new object[0]), ctl => _train_counter.assign_add(1));
-            throw new NotImplementedException("");
+            return null;
         }
 
         /// <summary>
@@ -38,7 +38,6 @@ namespace Tensorflow.Keras.Engine
             // The _minimize call does a few extra steps unnecessary in most cases,
             // such as loss scaling and gradient clipping.
             _minimize(tape, optimizer, loss, trainable_variables);
-
             compiled_metrics.update_state(y, y_pred);
             return new[] { ("loss", loss) };
         }

src/TensorFlowNET.Core/Keras/Optimizers/OptimizerV2.cs

@@ -71,7 +71,7 @@ namespace Tensorflow.Keras.Optimizers
                     return control_flow_ops.no_op();
 
                 var apply_state = _prepare(var_list);
-                if(experimental_aggregate_gradients)
+                // if(experimental_aggregate_gradients)
                 {
                     // var reduced_grads = _aggregate_gradients(grads_and_vars);
                     _distributed_apply(grads_and_vars, name, apply_state);
@@ -84,6 +84,9 @@ namespace Tensorflow.Keras.Optimizers
         void apply_grad_to_update_var(ResourceVariable var, Tensor grad, Dictionary<DeviceDType, Dictionary<string, Tensor>> apply_state)
         {
             _resource_apply_dense(var, grad, apply_state);
+            // if var.constraint is not None:
+            //     with ops.control_dependencies([update_op]):
+            //         return var.assign(var.constraint(var))
         }
 
         protected virtual Operation _resource_apply_dense(IVariableV1 var, 

src/TensorFlowNET.Core/Variables/state_ops.cs

@@ -67,6 +67,21 @@ namespace Tensorflow
                 name: name);
         }
 
+        public static Tensor assign(IVariableV1 @ref, object value,
+            bool validate_shape = true,
+            bool use_locking = true,
+            string name = null)
+        {
+            if (@ref.dtype.is_ref_dtype())
+                return gen_state_ops.assign(@ref,
+                    value,
+                    validate_shape: validate_shape,
+                    use_locking: use_locking,
+                    name: name);
+            else
+                return @ref.assign(value, name: name);
+        }
+
         public static Tensor assign_sub(IVariableV1 @ref,
             Tensor value,
             bool use_locking = false,