Change RefVariable to IVariableV1.

src/TensorFlowNET.Core/APIs/tf.compat.v1.cs

@@ -15,6 +15,7 @@
 ******************************************************************************/
 
 using System;
+using System.Collections.Generic;
 using Tensorflow.Eager;
 using static Tensorflow.Binding;
 
@@ -26,5 +27,29 @@ namespace Tensorflow
         {
             tf.context.default_execution_mode = Context.GRAPH_MODE;
         }
+
+        public IVariableV1 get_variable(string name,
+            TensorShape shape = null,
+            TF_DataType dtype = TF_DataType.DtInvalid,
+            object initializer = null, // IInitializer or Tensor
+            bool? trainable = null,
+            List<string> collections = null,
+            bool? use_resource = null,
+            bool validate_shape = true,
+            VariableSynchronization synchronization = VariableSynchronization.Auto,
+            VariableAggregation aggregation = VariableAggregation.None)
+        {
+            var scope = Tensorflow.variable_scope.get_variable_scope();
+            var store = Tensorflow.variable_scope._get_default_variable_store();
+            return scope.get_variable(store,
+                name,
+                shape: shape,
+                dtype: dtype,
+                use_resource: use_resource,
+                validate_shape: validate_shape,
+                initializer: initializer,
+                trainable: trainable,
+                collections: collections);
+        }
     }
 }

src/TensorFlowNET.Core/APIs/tf.nn.cs

@@ -27,13 +27,13 @@ namespace Tensorflow
 
         public class nn_internal
         {
-            public Tensor conv2d(Tensor input, RefVariable filter, int[] strides, string padding, bool use_cudnn_on_gpu = true, 
+            public Tensor conv2d(Tensor input, IVariableV1 filter, int[] strides, string padding, bool use_cudnn_on_gpu = true, 
                 string data_format= "NHWC", int[] dilations= null, string name = null)
             {
                 var parameters = new Conv2dParams
                 {
                     Input = input,
-                    Filter = filter,
+                    Filter = filter.AsTensor(),
                     Strides = strides,
                     Padding = padding,
                     UseCudnnOnGpu = use_cudnn_on_gpu,
@@ -98,7 +98,7 @@ namespace Tensorflow
                     name: name, 
                     keep_dims: keep_dims);
 
-            public Tensor embedding_lookup(RefVariable @params,
+            public Tensor embedding_lookup(IVariableV1 @params,
                 Tensor ids,
                 string partition_strategy = "mod",
                 string name = null) => embedding_ops._embedding_lookup_and_transform(@params,
@@ -150,12 +150,12 @@ namespace Tensorflow
             public Tensor[] top_k(Tensor input, int k = 1, bool sorted = true, string name = null)
                 => gen_nn_ops.top_kv2(input, k: k, sorted: sorted, name: name);
 
-            public Tensor bias_add(Tensor value, RefVariable bias, string data_format = null, string name = null)
+            public Tensor bias_add(Tensor value, IVariableV1 bias, string data_format = null, string name = null)
             {
                 return tf_with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
                 {
                     name = scope;
-                    return gen_nn_ops.bias_add(value, bias, data_format: data_format, name: name);
+                    return gen_nn_ops.bias_add(value, bias.AsTensor(), data_format: data_format, name: name);
                 });
             }
 

src/TensorFlowNET.Core/APIs/tf.state.cs

@@ -18,7 +18,7 @@ namespace Tensorflow
 {
     public partial class tensorflow
     {
-        public Tensor assign_add<T>(IVariableV1 @ref, T value,
+        public ITensorOrOperation assign_add<T>(IVariableV1 @ref, T value,
             bool use_locking = false, string name = null)
             => state_ops.assign_add(@ref, value, use_locking: use_locking, name: name);
     }

src/TensorFlowNET.Core/APIs/tf.train.cs

@@ -26,10 +26,10 @@ namespace Tensorflow
 
         public class train_internal
         {
-            public RefVariable create_global_step(Graph graph)
+            public IVariableV1 create_global_step(Graph graph)
                 => TrainingUtil.create_global_step(graph);
 
-            public RefVariable get_global_step(Graph graph)
+            public IVariableV1 get_global_step(Graph graph)
                 => TrainingUtil.get_global_step(graph);
 
             public Optimizer GradientDescentOptimizer(float learning_rate) 

src/TensorFlowNET.Core/APIs/tf.variable.cs

@@ -50,30 +50,6 @@ namespace Tensorflow
         public IVariableV1[] trainable_variables(string scope = null)
             => (variables.trainable_variables() as List<IVariableV1>).ToArray();
 
-        public RefVariable get_variable(string name,
-            TensorShape shape = null,
-            TF_DataType dtype = TF_DataType.DtInvalid,
-            object initializer = null, // IInitializer or Tensor
-            bool? trainable = null,
-            List<string> collections = null,
-            bool? use_resource = null,
-            bool validate_shape = true,
-            VariableSynchronization synchronization = VariableSynchronization.Auto,
-            VariableAggregation aggregation = VariableAggregation.None)
-        {
-            var scope = Tensorflow.variable_scope.get_variable_scope();
-            var store = Tensorflow.variable_scope._get_default_variable_store();
-            return scope.get_variable(store,
-                name,
-                shape: shape,
-                dtype: dtype,
-                use_resource: use_resource,
-                validate_shape: validate_shape,
-                initializer: initializer,
-                trainable: trainable,
-                collections: collections);
-        }
-
         public VariableScope get_variable_scope()
             => Tensorflow.variable_scope.get_variable_scope();
     }

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -535,7 +535,7 @@ namespace Tensorflow
         string debugString = string.Empty;
         public override string ToString()
         {
-            return $"{graph_key}, ({_handle})";
+            return $"{graph_key}, 0x{_handle.ToString("x16")}";
             /*if (string.IsNullOrEmpty(debugString))
             {
                 int len = 0;

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

@@ -34,7 +34,7 @@ namespace Tensorflow.Keras.Optimizers
             this.name = name;
         }
 
-        public Tensor __call__(RefVariable step)
+        public Tensor __call__(IVariableV1 step)
         {
             return tf_with(ops.name_scope(name ?? "PolynomialDecay"), scope =>
             {

src/TensorFlowNET.Core/Layers/Layer.cs

@@ -161,7 +161,7 @@ namespace Tensorflow.Layers
                             initializer: initializer,
                             trainable: trainable,
                             getter: (name1, shape1, dtype1, initializer1, trainable1) =>
-                                tf.get_variable(name1,
+                                tf.compat.v1.get_variable(name1,
                                     shape: new TensorShape(shape1),
                                     dtype: dtype1,
                                     initializer: initializer1,

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -68,11 +68,11 @@ namespace Tensorflow
             return gen_math_ops.add_n(inputs, name: name);
         }
 
-        public static Tensor cast(RefVariable x, TF_DataType dtype = TF_DataType.DtInvalid, string name = null)
+        public static Tensor cast(IVariableV1 x, TF_DataType dtype = TF_DataType.DtInvalid, string name = null)
         {
             var base_type = dtype.as_base_dtype();
             if (base_type == x.dtype)
-                return x;
+                return x.AsTensor();
 
             return tf_with(ops.name_scope(name, "Cast", new { x }), scope =>
             {
@@ -81,7 +81,7 @@ namespace Tensorflow
                 if (t_x.dtype.as_base_dtype() != base_type)
                     t_x = gen_math_ops.cast(t_x, base_type, name: name);
 
-                return x;
+                return x.AsTensor();
             });
         }
 

src/TensorFlowNET.Core/Training/AdamOptimizer.cs

@@ -79,7 +79,7 @@ namespace Tensorflow.Train
                 use_locking: _use_locking).op;
         }
 
-        private Operation _apply_sparse_shared(Tensor grad, RefVariable var, Tensor indices, Func<RefVariable, Tensor, Tensor, Tensor> scatter_add)
+        private Operation _apply_sparse_shared(Tensor grad, IVariableV1 var, Tensor indices, Func<IVariableV1, Tensor, Tensor, Tensor> scatter_add)
         {
             var (beta1_power_v, beta2_power_v) = _get_beta_accumulators();
             Tensor beta1_power = math_ops.cast(beta1_power_v, var.dtype.as_base_dtype());
@@ -91,7 +91,7 @@ namespace Tensorflow.Train
             var lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power));
             var m = get_slot(var, "m");
             var m_scaled_g_values = grad * (1 - beta1_t);
-            var m_t = state_ops.assign(m, m * beta1_t, use_locking: _use_locking);
+            var m_t = state_ops.assign(m.AsTensor(), m.AsTensor() * beta1_t, use_locking: _use_locking);
             tf_with(ops.control_dependencies(new[] { m_t }), delegate
             {
                 m_t = scatter_add(m, indices, m_scaled_g_values);
@@ -99,7 +99,7 @@ namespace Tensorflow.Train
 
             var v = get_slot(var, "v");
             var v_scaled_g_values = (grad * grad) * (1 - beta2_t);
-            var v_t = state_ops.assign(v, v * beta2_t, use_locking: _use_locking);
+            var v_t = state_ops.assign(v.AsTensor(), v.AsTensor() * beta2_t, use_locking: _use_locking);
             tf_with(ops.control_dependencies(new[] { v_t }), delegate
             {
                 v_t = scatter_add(v, indices, v_scaled_g_values);
@@ -132,8 +132,8 @@ namespace Tensorflow.Train
             {
                 var (beta1_power, beta2_power) = _get_beta_accumulators();
                 ops.colocate_with(beta1_power);
-                var update_beta1 = beta1_power.assign(beta1_power * _beta1_t, use_locking: _use_locking);
-                var update_beta2 = beta2_power.assign(beta2_power * _beta2_t, use_locking: _use_locking);
+                var update_beta1 = beta1_power.assign(beta1_power.AsTensor() * _beta1_t, use_locking: _use_locking);
+                var update_beta2 = beta2_power.assign(beta2_power.AsTensor() * _beta2_t, use_locking: _use_locking);
 
                 operations.Add(update_beta1);
                 operations.Add(update_beta2);
@@ -142,12 +142,12 @@ namespace Tensorflow.Train
             return control_flow_ops.group(operations.ToArray(), name: name_scope);
         }
 
-        private (RefVariable, RefVariable) _get_beta_accumulators()
+        private (IVariableV1, IVariableV1) _get_beta_accumulators()
         {
             ops.init_scope();
             var graph = ops.get_default_graph();
-            return (_get_non_slot_variable("beta1_power", graph: graph) as RefVariable,
-                _get_non_slot_variable("beta2_power", graph: graph) as RefVariable);
+            return (_get_non_slot_variable("beta1_power", graph: graph),
+                _get_non_slot_variable("beta2_power", graph: graph));
         }
 
         public override void _prepare()

src/TensorFlowNET.Core/Training/ExponentialMovingAverage.cs

@@ -13,7 +13,7 @@ namespace Tensorflow.Train
         bool _zero_debias;
         string _name;
         public string name => _name;
-        Dictionary<RefVariable, RefVariable> _averages;
+        Dictionary<IVariableV1, IVariableV1> _averages;
 
         public ExponentialMovingAverage(float decay, int? num_updates = null, bool zero_debias = false,
             string name = "ExponentialMovingAverage")
@@ -22,7 +22,7 @@ namespace Tensorflow.Train
             _num_updates = num_updates;
             _zero_debias = zero_debias;
             _name = name;
-            _averages = new Dictionary<RefVariable, RefVariable>();
+            _averages = new Dictionary<IVariableV1, IVariableV1>();
         }
 
         /// <summary>

src/TensorFlowNET.Core/Training/Optimizer.cs

@@ -43,7 +43,7 @@ namespace Tensorflow
         protected Tensor _lr_t;
         public Tensor LearningRateTensor => _lr_t;
         public bool _use_locking;
-        public Dictionary<string, Dictionary<string, RefVariable>> _slots;
+        public Dictionary<string, Dictionary<string, IVariableV1>> _slots;
         public Dictionary<string, IVariableV1> _non_slot_dict;
         public Dictionary<string, object> _deferred_slot_restorations;
         SlotCreator slot_creator = new SlotCreator();
@@ -57,7 +57,7 @@ namespace Tensorflow
             _use_locking = use_locking;
             _lr = learning_rate;
             // Dictionary of slots.
-            _slots = new Dictionary<string, Dictionary<string, RefVariable>>();
+            _slots = new Dictionary<string, Dictionary<string, IVariableV1>>();
             _non_slot_dict = new Dictionary<string, IVariableV1>();
             _deferred_slot_restorations = new Dictionary<string, object>();
         }
@@ -71,7 +71,7 @@ namespace Tensorflow
             _use_locking = use_locking;
             _lr_t = learning_rate;
             // Dictionary of slots.
-            _slots = new Dictionary<string, Dictionary<string, RefVariable>>();
+            _slots = new Dictionary<string, Dictionary<string, IVariableV1>>();
             _non_slot_dict = new Dictionary<string, IVariableV1>();
             _deferred_slot_restorations = new Dictionary<string, object>();
         }
@@ -207,7 +207,7 @@ namespace Tensorflow
                         {
                             apply_updates = state_ops.assign_add(global_step,
                                 ops.convert_to_tensor(1, dtype: global_step.dtype),
-                                name: name);
+                                name: name) as Operation;
                         }
                     });
                 }
@@ -241,7 +241,7 @@ namespace Tensorflow
         /// <param name="initial_value"></param>
         /// <param name="name"></param>
         /// <param name="colocate_with"></param>
-        protected IVariableV1 _create_non_slot_variable(float initial_value, string name, RefVariable colocate_with)
+        protected IVariableV1 _create_non_slot_variable(float initial_value, string name, IVariableV1 colocate_with)
         {
             // Recommendation: Use OptimizerV2 if your optimizer uses non-slot variables.
             var graph = colocate_with.Graph;
@@ -338,7 +338,7 @@ namespace Tensorflow
         /// <param name="var"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        protected RefVariable get_slot(RefVariable var, string name)
+        protected IVariableV1 get_slot(IVariableV1 var, string name)
         {
             var named_slots = _slots.ContainsKey(name) ? _slots[name] : null;
             if (named_slots == null)
@@ -347,7 +347,7 @@ namespace Tensorflow
             return named_slots.ContainsKey(_var_key(var)) ? named_slots[_var_key(var)] : null;
         }
 
-        private string _var_key(RefVariable var)
+        private string _var_key(IVariableV1 var)
         {
             return $"{var.Op.graph.graph_key}.{var.Op.name}";
         }
@@ -438,7 +438,7 @@ namespace Tensorflow
         /// <param name="slot_name"></param>
         /// <param name="op_name"></param>
         /// <returns></returns>
-        protected RefVariable _zeros_slot(RefVariable var, string slot_name, string op_name)
+        protected IVariableV1 _zeros_slot(IVariableV1 var, string slot_name, string op_name)
         {
             var named_slots = _slot_dict(slot_name);
             if (!named_slots.ContainsKey(_var_key(var)))
@@ -453,18 +453,18 @@ namespace Tensorflow
         /// <summary>
         /// Restore a newly created slot variable's value.
         /// </summary>
-        protected void _restore_slot_variable(string slot_name, RefVariable variable, RefVariable slot_variable)
+        protected void _restore_slot_variable(string slot_name, IVariableV1 variable, IVariableV1 slot_variable)
         {
             var variable_key = _var_key(variable);
             // TODO
         }
 
-        protected Dictionary<string, RefVariable> _slot_dict(string slot_name)
+        protected Dictionary<string, IVariableV1> _slot_dict(string slot_name)
         {
             var named_slots = _slots.ContainsKey(slot_name) ? _slots[slot_name] : null;
             if(named_slots == null)
             {
-                named_slots = new Dictionary<string, RefVariable>();
+                named_slots = new Dictionary<string, IVariableV1>();
                 _slots[slot_name] = named_slots;
             }
 

src/TensorFlowNET.Core/Training/SlotCreator.cs

@@ -30,7 +30,7 @@ namespace Tensorflow.Train
         /// <param name="name"></param>
         /// <param name="colocate_with_primary"></param>
         /// <returns></returns>
-        public RefVariable create_slot(RefVariable primary, Tensor val, string name, bool colocate_with_primary = true)
+        public IVariableV1 create_slot(RefVariable primary, Tensor val, string name, bool colocate_with_primary = true)
         {
             var validate_shape = val.TensorShape.is_fully_defined();
             var prefix = primary.Op.name;
@@ -48,7 +48,7 @@ namespace Tensorflow.Train
         /// <param name="dtype"></param>
         /// <param name="colocate_with_primary"></param>
         /// <returns></returns>
-        public RefVariable create_zeros_slot(RefVariable primary, string name, TF_DataType dtype = TF_DataType.DtInvalid, bool colocate_with_primary = true)
+        public IVariableV1 create_zeros_slot(IVariableV1 primary, string name, TF_DataType dtype = TF_DataType.DtInvalid, bool colocate_with_primary = true)
         {
             if (dtype == TF_DataType.DtInvalid)
                 dtype = primary.dtype;
@@ -70,7 +70,7 @@ namespace Tensorflow.Train
         /// Creates a slot initialized using an `Initializer`.
         /// </summary>
         /// <returns></returns>
-        public RefVariable create_slot_with_initializer(RefVariable primary, IInitializer initializer, TensorShape shape, 
+        public IVariableV1 create_slot_with_initializer(IVariableV1 primary, IInitializer initializer, TensorShape shape, 
             TF_DataType dtype, string name, bool colocate_with_primary = true)
         {
             var validate_shape = shape.is_fully_defined();
@@ -91,14 +91,14 @@ namespace Tensorflow.Train
         /// <param name="shape"></param>
         /// <param name="dtype"></param>
         /// <returns></returns>
-        private RefVariable _create_slot_var(IVariableV1 primary, object val, string scope, bool validate_shape, 
+        private IVariableV1 _create_slot_var(IVariableV1 primary, object val, string scope, bool validate_shape, 
             TensorShape shape, TF_DataType dtype)
         {
             bool use_resource = primary is ResourceVariable;
             if (resource_variable_ops.is_resource_variable(primary))
                 use_resource = true;
 
-            var slot = tf.get_variable(
+            var slot = tf.compat.v1.get_variable(
               scope,
               initializer: val,
               trainable: false,

src/TensorFlowNET.Core/Training/TrainingUtil.cs

@@ -7,7 +7,7 @@ namespace Tensorflow.Train
 {
     public class TrainingUtil
     {
-        public static RefVariable create_global_step(Graph graph = null)
+        public static IVariableV1 create_global_step(Graph graph = null)
         {
             graph = graph ?? ops.get_default_graph();
             if (get_global_step(graph) != null)
@@ -16,7 +16,7 @@ namespace Tensorflow.Train
             // Create in proper graph and base name_scope.
             var g = graph.as_default();
             g.name_scope(null);
-            var v = tf.get_variable(tf.GraphKeys.GLOBAL_STEP, new int[0], dtype: dtypes.int64,
+            var v = tf.compat.v1.get_variable(tf.GraphKeys.GLOBAL_STEP, new int[0], dtype: dtypes.int64,
                 initializer: tf.zeros_initializer,
                 trainable: false,
                 aggregation: VariableAggregation.OnlyFirstReplica,

src/TensorFlowNET.Core/Training/gen_training_ops.cs

@@ -23,7 +23,7 @@ namespace Tensorflow
 {
     public class gen_training_ops
     {
-        public static Tensor apply_adam(RefVariable var, RefVariable m, RefVariable v, Tensor beta1_power, Tensor beta2_power, 
+        public static Tensor apply_adam(IVariableV1 var, IVariableV1 m, IVariableV1 v, Tensor beta1_power, Tensor beta2_power, 
             Tensor lr, Tensor beta1, Tensor beta2, Tensor epsilon, Tensor grad, 
             bool use_locking = false, bool use_nesterov = false, string name = null)
         {

src/TensorFlowNET.Core/Training/moving_averages.cs

@@ -16,7 +16,7 @@ namespace Tensorflow.Train
         /// <param name="zero_debias"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor assign_moving_average(RefVariable variable, RefVariable value, Tensor decay,
+        public static Tensor assign_moving_average(IVariableV1 variable, IVariableV1 value, Tensor decay,
             bool zero_debias = true, string name = null)
         {
             return tf_with(ops.name_scope(name, "AssignMovingAvg", new { variable, value, decay }), scope =>
@@ -25,7 +25,7 @@ namespace Tensorflow.Train
                 if (decay.dtype != variable.dtype.as_base_dtype())
                     decay = math_ops.cast(decay, variable.dtype.as_base_dtype());
 
-                return state_ops.assign_sub(variable, (variable - value) * decay, name: scope);
+                return state_ops.assign_sub(variable, (variable.AsTensor() - value.AsTensor()) * decay, name: scope);
             });
         }
     }

src/TensorFlowNET.Core/Variables/BaseResourceVariable.cs

@@ -70,14 +70,15 @@ namespace Tensorflow
             // handle_deleter
         }
 
-        public BaseResourceVariable assign(object value, bool use_locking = false, string name = null, bool read_value = true)
+        public ITensorOrOperation assign<T>(T value, bool use_locking = false, string name = null, bool read_value = true)
         {
             var value_tensor = ops.convert_to_tensor(value, dtype: dtype);
             var assign_op = gen_resource_variable_ops.assign_variable_op(
                 handle, value_tensor, name: name);
             if (read_value)
-                return _lazy_read(assign_op, value_tensor);
-            return null;
+                return gen_resource_variable_ops.read_variable_op(handle, dtype);
+                // return _lazy_read(assign_op, value_tensor);
+            return assign_op;
         }
 
         public Tensor value() => _read_variable_op();
@@ -122,13 +123,14 @@ namespace Tensorflow
                 return array_ops.identity(value);
             });
 
-        public Operation assign_add<T>(T delta, bool use_locking = false, string name = null, bool read_value = true)
+        public ITensorOrOperation assign_add<T>(T delta, bool use_locking = false, string name = null, bool read_value = true)
         {
             var assign_add_op = gen_resource_variable_ops.assign_add_variable_op(Handle,
                 ops.convert_to_tensor(delta, dtype: dtype), name: name);
             
-            /*if (read_value)
-                return _lazy_read(assign_add_op);*/
+            if (read_value)
+                return gen_resource_variable_ops.read_variable_op(handle, dtype);
+                // return _lazy_read(assign_add_op);
             return assign_add_op;
         }
 
@@ -145,5 +147,7 @@ namespace Tensorflow
         protected override void DisposeUnmanagedResources(IntPtr handle)
         {
         }
+
+        public Tensor AsTensor() => _graph_element;
     }
 }

src/TensorFlowNET.Core/Variables/IVariableV1.cs

@@ -38,6 +38,9 @@ namespace Tensorflow
         public Tensor GraphElement { get; }
         public Graph Graph { get; }
         public TF_DataType dtype { get; }
-        public Operation assign_add<T>(T delta, bool use_locking = false, string name = null, bool read_value = true);
+        public TensorShape shape { get; }
+        ITensorOrOperation assign_add<T>(T delta, bool use_locking = false, string name = null, bool read_value = true);
+        ITensorOrOperation assign<T>(T value, bool use_locking = false, string name = null, bool read_value = true);
+        Tensor AsTensor();
     }
 }

src/TensorFlowNET.Core/Variables/RefVariable.Implicit.cs

@@ -14,7 +14,7 @@
 
         public static implicit operator Tensor(RefVariable var)
         {
-            return var._AsTensor();
+            return var.AsTensor();
         }
 
         public static implicit operator RefVariable(Tensor var)

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -220,7 +220,7 @@ namespace Tensorflow
 
         public Tensor value() => _snapshot;
 
-        public Tensor _AsTensor() => _snapshot;
+        public Tensor AsTensor() => _snapshot;
 
         public Tensor _as_graph_element() => _variable;
 
@@ -333,7 +333,7 @@ namespace Tensorflow
         /// A `Tensor` that will hold the new value of this variable after
         /// the assignment has completed.
         /// </returns>
-        public ITensorOrOperation assign(object value, bool use_locking = false, string name = null, bool read_value = true)
+        public ITensorOrOperation assign<T>(T value, bool use_locking = false, string name = null, bool read_value = true)
         {
             var assign = gen_state_ops.assign(_variable, value, use_locking: use_locking, name: name);
             if (read_value)
@@ -416,7 +416,7 @@ namespace Tensorflow
         //      name: A name for the operation(optional).
         //  Returns:
         //    A mutable `Tensor`. Has the same type as `ref`.
-        public Operation assign_add<T>(T value, bool use_locking = false, string name = null, bool read_value = true)
+        public ITensorOrOperation assign_add<T>(T value, bool use_locking = false, string name = null, bool read_value = true)
         {
             var variable = this;
             var _op = tf._op_def_lib._apply_op_helper("AssignAdd", name: name, args: new { variable, value, use_locking });

src/TensorFlowNET.Core/Variables/VariableScope.cs

@@ -47,7 +47,7 @@ namespace Tensorflow
             _dtype = dtype;
         }
 
-        public RefVariable get_variable(_VariableStore var_store, 
+        public IVariableV1 get_variable(_VariableStore var_store, 
             string name, 
             TensorShape shape = null, 
             TF_DataType dtype = TF_DataType.DtInvalid,
@@ -73,7 +73,7 @@ namespace Tensorflow
                     trainable: trainable,
                     collections: collections,
                     synchronization: synchronization,
-                    aggregation: aggregation) as RefVariable;
+                    aggregation: aggregation);
             });
         }
 

src/TensorFlowNET.Core/Variables/_VariableStore.cs

@@ -92,7 +92,7 @@ namespace Tensorflow
                 return _get_single_variable(name: name,
                     shape: shape,
                     dtype: dtype,
-                    initializer: tensor,
+                    init_value: tensor,
                     trainable: trainable,
                     validate_shape: validate_shape,
                     synchronization: synchronization,
@@ -116,6 +116,7 @@ namespace Tensorflow
             TensorShape shape = null,
             TF_DataType dtype = TF_DataType.DtInvalid,
             IInitializer initializer = null,
+            Tensor init_value = null,
             bool reuse = false,
             bool? trainable = null,
             List<string> collections = null,
@@ -124,9 +125,9 @@ namespace Tensorflow
             VariableSynchronization synchronization = VariableSynchronization.Auto,
             VariableAggregation aggregation = VariableAggregation.None)
         {
-            bool initializing_from_value = false;
+            bool initializing_from_value = init_value != null;
             if (use_resource == null)
-                use_resource = false;
+                use_resource = variable_scope._DEFAULT_USE_RESOURCE;
 
             if (_vars.ContainsKey(name))
             {
@@ -140,7 +141,7 @@ namespace Tensorflow
 
             IVariableV1 v = null;
             // Create the tensor to initialize the variable with default value.
-            if (initializer == null)
+            if (initializer == null && init_value == null)
             {
                 if (dtype.is_floating())
                 {
@@ -154,7 +155,10 @@ namespace Tensorflow
             {
                 if (initializing_from_value)
                 {
-
+                    v = new ResourceVariable(init_value,
+                        name: name,
+                        validate_shape: validate_shape,
+                        trainable: trainable.Value);
                 }
                 else
                 {
@@ -166,6 +170,7 @@ namespace Tensorflow
                         trainable: trainable,
                         collections: collections,
                         dtype: variable_dtype,
+                        use_resource: use_resource,
                         validate_shape: validate_shape,
                         synchronization: synchronization,
                         aggregation: aggregation);
@@ -176,45 +181,5 @@ namespace Tensorflow
 
             return v;
         }
-
-        private RefVariable _get_single_variable(string name,
-            TensorShape shape = null,
-            TF_DataType dtype = TF_DataType.DtInvalid,
-            Tensor initializer = null,
-            bool reuse = false,
-            bool? trainable = null,
-            bool validate_shape = false,
-            bool? use_resource = null,
-            VariableSynchronization synchronization = VariableSynchronization.Auto,
-            VariableAggregation aggregation = VariableAggregation.None)
-        {
-            if (use_resource == null)
-                use_resource = false;
-
-            if (_vars.ContainsKey(name))
-            {
-                if (!reuse)
-                {
-                    var var = _vars[name];
-
-                }
-                throw new NotImplementedException("_get_single_variable");
-            }
-
-            RefVariable v = null;
-            // Create the variable.
-            ops.init_scope();
-            {
-                var init_val = initializer;
-                v = new RefVariable(init_val,
-                    name: name,
-                    validate_shape: validate_shape,
-                    trainable: trainable.Value);
-            }
-
-            _vars[name] = v;
-
-            return v;
-        }
     }
 }

src/TensorFlowNET.Core/Variables/gen_state_ops.py.cs

@@ -110,7 +110,7 @@ namespace Tensorflow
             return _result[0];
         }
 
-        public static Tensor assign_sub(RefVariable @ref,
+        public static Tensor assign_sub(IVariableV1 @ref,
             Tensor value,
             bool use_locking = false,
             string name = null)
@@ -129,7 +129,7 @@ namespace Tensorflow
         /// <param name="use_locking"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor scatter_add(RefVariable @ref, Tensor indices, Tensor updates, bool use_locking = false, string name = null)
+        public static Tensor scatter_add(IVariableV1 @ref, Tensor indices, Tensor updates, bool use_locking = false, string name = null)
         {
             var _op = tf._op_def_lib._apply_op_helper("ScatterAdd", name: name, args: new { @ref, indices, updates, use_locking });
             return _op.outputs[0];

src/TensorFlowNET.Core/Variables/state_ops.cs

@@ -78,7 +78,7 @@ namespace Tensorflow
                 name: name);
         }
 
-        public static Tensor assign_sub(RefVariable @ref,
+        public static Tensor assign_sub(IVariableV1 @ref,
             Tensor value,
             bool use_locking = false,
             string name = null) => gen_state_ops.assign_sub(@ref,
@@ -106,13 +106,13 @@ namespace Tensorflow
         //  Returns:
         //    Same as "ref".  Returned as a convenience for operations that want
         //    to use the new value after the variable has been updated.
-        public static Operation assign_add<T>(IVariableV1 @ref,
+        public static ITensorOrOperation assign_add<T>(IVariableV1 @ref,
             T value,
             bool use_locking = false,
             string name = null)
             => @ref.assign_add(value, use_locking: use_locking, name: name);
 
-        public static Tensor scatter_add(RefVariable @ref, Tensor indices, Tensor updates, bool use_locking = false, string name = null)
+        public static Tensor scatter_add(IVariableV1 @ref, Tensor indices, Tensor updates, bool use_locking = false, string name = null)
         {
             if (@ref.dtype.is_ref_dtype())
                 return gen_state_ops.scatter_add(@ref, indices, updates, use_locking: use_locking, name: name);

src/TensorFlowNET.Core/Variables/variable_scope.py.cs

@@ -27,7 +27,7 @@ namespace Tensorflow
     {
         public static string _VARSTORE_KEY = "__variable_store";
         public static string _VARSCOPESTORE_KEY = "__varscope";
-        public static bool _DEFAULT_USE_RESOURCE = false;
+        public static bool _DEFAULT_USE_RESOURCE = true;
 
         private bool _use_resource;
         public bool UseResource => _use_resource;

src/TensorFlowNET.Core/ops.cs

@@ -304,6 +304,11 @@ namespace Tensorflow
             _colocate_with_for_gradient(tensor.op, null, ignore_existing);
         }
 
+        public static void colocate_with(IVariableV1 variable, bool ignore_existing = false)
+        {
+            _colocate_with_for_gradient(variable.AsTensor(), null, ignore_existing);
+        }
+
         public static void _colocate_with_for_gradient(Operation op, string gradient_uid, bool ignore_existing = false)
         {
             var default_graph = get_default_graph();