fix _apply_sparse for ResourceVariable.

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

@@ -30,10 +30,7 @@ namespace Tensorflow
         public Tensor assign(Tensor @ref, object value, bool validate_shape = true, bool use_locking = true, string name = null) 
             => state_ops.assign(@ref, value, validate_shape, use_locking, name);
 
-        public Tensor assign(RefVariable @ref, object value, bool validate_shape = true, bool use_locking = true, string name = null)
-            => state_ops.assign(@ref, value, validate_shape, use_locking, name);
-
-        public Tensor assign(ResourceVariable @ref, object value, bool validate_shape = true, bool use_locking = true, string name = null)
+        public Tensor assign(IVariableV1 @ref, object value, bool validate_shape = true, bool use_locking = true, string name = null)
             => state_ops.assign(@ref, value, validate_shape, use_locking, name);
 
         public void device(string device_name)

src/TensorFlowNET.Core/Keras/Engine/Layer.cs

@@ -121,7 +121,7 @@ namespace Tensorflow.Keras.Engine
         /// <param name="input"></param>
         /// <param name="is_training"></param>
         /// <returns></returns>
-        public Tensor Apply(Tensor inputs, bool is_training = false)
+        public Tensor Apply(Tensor inputs, bool is_training = false, Tensor state = null)
         {
             Tensor outputs = null;
 
@@ -135,9 +135,9 @@ namespace Tensorflow.Keras.Engine
 
             string nameScope = "";
             if (eager)
-            {
                 nameScope = name;
-            }
+            else
+                nameScope = _name_scope();
 
             // using var graph = tf.keras.backend.get_graph().as_default();
             if (!inputs.IsEagerTensor)
@@ -148,7 +148,7 @@ namespace Tensorflow.Keras.Engine
                 if (!built)
                     MaybeBuild(inputs);
 
-                outputs = call(inputs, is_training: is_training);
+                outputs = call(inputs, is_training: is_training, state: state);
 
                 outputs = _set_connectivity_metadata_(inputs, outputs);
                 _handle_activity_regularization(inputs, outputs);

src/TensorFlowNET.Core/Layers/Layer.cs

@@ -88,7 +88,9 @@ namespace Tensorflow.Layers
             {
                 _current_scope = scope2;
                 // Actually call layer
-                outputs = base.Apply(inputs);
+                outputs = base.Apply(inputs, 
+                    is_training: training == null ? false : false,
+                    state: state);
             });
 
 

src/TensorFlowNET.Core/Operations/NnOps/BasicRNNCell.cs

@@ -71,8 +71,8 @@ namespace Tensorflow
         {
             // Most basic RNN: output = new_state = act(W * input + U * state + B).
             var concat = array_ops.concat(new[] { inputs, state }, 1);
-            var gate_inputs = math_ops.matmul(concat, _kernel as RefVariable);
-            gate_inputs = nn_ops.bias_add(gate_inputs, _bias as RefVariable);
+            var gate_inputs = math_ops.matmul(concat, _kernel.AsTensor());
+            gate_inputs = nn_ops.bias_add(gate_inputs, _bias.AsTensor());
             var output = _activation(gate_inputs, null);
             return output;
         }

src/TensorFlowNET.Core/Operations/Operation.cs

@@ -326,7 +326,7 @@ namespace Tensorflow
             // the updated inputs are reloaded from the c_api
             lock (Locks.ProcessWide)
             {
-                c_api.UpdateEdge(_graph, output, input, tf.Status.Handle);
+                // c_api.UpdateEdge(_graph, output, input, tf.Status.Handle);
                 //var updated_inputs = inputs;
                 tf.Status.Check();
             }

src/TensorFlowNET.Core/Operations/embedding_ops.cs

@@ -74,7 +74,7 @@ namespace Tensorflow
                 ids = ops.convert_to_tensor(ids, name: "ids");
                 if (np == 1)
                 {
-                    var gather = array_ops.gather(@params, ids, name: name);
+                    var gather = array_ops.gather(@params.AsTensor(), ids, name: name);
                     var result = _clip(gather, ids, max_norm);
 
                     return array_ops.identity(result);

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -706,11 +706,12 @@ namespace Tensorflow
             => tf_with(ops.name_scope(name, "Pow", new { x, y }), scope =>
             {
                 name = scope;
-                var x_tensor = ops.convert_to_tensor(x, name: "x");
-                var y_tensor = ops.convert_to_tensor(y, name: "y", dtype: x_tensor.dtype.as_base_dtype());
 
                 if (tf.executing_eagerly())
                 {
+                    var x_tensor = ops.convert_to_tensor(x, name: "x");
+                    var y_tensor = ops.convert_to_tensor(y, name: "y", dtype: x_tensor.dtype.as_base_dtype());
+
                     var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
                         "Pow", name,
                         null,
@@ -719,7 +720,7 @@ namespace Tensorflow
                     return results[0];
                 }
 
-                var _op = tf.OpDefLib._apply_op_helper("Pow", name, args: new { x_tensor, y_tensor });
+                var _op = tf.OpDefLib._apply_op_helper("Pow", name, args: new { x, y });
 
                 return _op.output;
             }); 

src/TensorFlowNET.Core/Tensorflow.Binding.csproj

@@ -10,7 +10,7 @@
     <Authors>Haiping Chen, Meinrad Recheis, Eli Belash</Authors>
     <Company>SciSharp STACK</Company>
     <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
-    <Copyright>Apache 2.0</Copyright>
+    <Copyright>Apache 2.0, Haiping Chen $([System.DateTime]::UtcNow.ToString(yyyy))</Copyright>
     <RepositoryUrl>https://github.com/SciSharp/TensorFlow.NET</RepositoryUrl>
     <RepositoryType>git</RepositoryType>
     <PackageProjectUrl>http://scisharpstack.org</PackageProjectUrl>

src/TensorFlowNET.Core/Training/AdamOptimizer.cs

@@ -52,6 +52,14 @@ namespace Tensorflow.Train
             _dtype = dtype;
         }
 
+        public override Operation _apply_sparse(IndexedSlices grad, ResourceVariable var)
+        {
+            return _apply_sparse_shared(grad.values, var, grad.indices, (x, i, v) =>
+            {
+                return state_ops.scatter_add(x, i, v, use_locking: _use_locking);
+            });
+        }
+
         public override Operation _apply_sparse(IndexedSlices grad, RefVariable var)
         {
             return _apply_sparse_shared(grad.values, var, grad.indices, (x, i, v) =>
@@ -91,7 +99,7 @@ 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.AsTensor(), m.AsTensor() * beta1_t, use_locking: _use_locking);
+            var m_t = state_ops.assign(m, 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 +107,7 @@ 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.AsTensor(), v.AsTensor() * beta2_t, use_locking: _use_locking);
+            var v_t = state_ops.assign(v, 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);

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

@@ -56,7 +56,7 @@ namespace Tensorflow
         /// <param name="validate_shape"></param>
         /// <param name="use_locking"></param>
         /// <param name="name"></param>
-        public static Tensor assign(Tensor @ref, object value, 
+        public static Tensor assign<T>(T @ref, object value, 
             bool validate_shape = true, 
             bool use_locking = true,
             string name = null)
@@ -74,40 +74,10 @@ namespace Tensorflow
             return _result[0];
         }
 
-        public static Tensor assign(RefVariable @ref, object value,
-            bool validate_shape = true,
-            bool use_locking = true,
-            string name = null)
-        {
-            var _op = tf.OpDefLib._apply_op_helper("Assign", name: name, args: new { @ref, value, validate_shape, use_locking });
-
-            var _result = _op.outputs;
-            var _inputs_flat = _op.inputs;
-
-            var _attrs = new Dictionary<string, object>();
-            _attrs["T"] = _op.get_attr("T");
-            _attrs["validate_shape"] = _op.get_attr("validate_shape");
-            _attrs["use_locking"] = _op.get_attr("use_locking");
-
-            return _result[0];
-        }
-
-        public static Tensor assign(ResourceVariable @ref, object value,
-            bool validate_shape = true,
-            bool use_locking = true,
-            string name = null)
+        public static Tensor assign_add<T>(IVariableV1 @ref, T value, bool use_locking = false, string name = null)
         {
-            var _op = tf.OpDefLib._apply_op_helper("Assign", name: name, args: new { @ref, value, validate_shape, use_locking });
-
-            var _result = _op.outputs;
-            var _inputs_flat = _op.inputs;
-
-            var _attrs = new Dictionary<string, object>();
-            _attrs["T"] = _op.get_attr("T");
-            _attrs["validate_shape"] = _op.get_attr("validate_shape");
-            _attrs["use_locking"] = _op.get_attr("use_locking");
-
-            return _result[0];
+            var _op = tf.OpDefLib._apply_op_helper("AssignAdd", name: name, args: new { @ref, value, use_locking });
+            return _op.outputs[0];
         }
 
         public static Tensor assign_sub(IVariableV1 @ref,

src/TensorFlowNET.Core/Variables/state_ops.cs

@@ -15,6 +15,7 @@
 ******************************************************************************/
 
 using System;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {
@@ -54,19 +55,7 @@ namespace Tensorflow
             return @ref.assign((Tensor)value, name: name);
         }
 
-        public static Tensor assign(RefVariable @ref, object value,
-            bool validate_shape = true,
-            bool use_locking = true,
-            string name = null)
-        {
-            return gen_state_ops.assign(@ref,
-                value,
-                validate_shape: validate_shape,
-                use_locking: use_locking,
-                name: name);
-        }
-
-        public static Tensor assign(ResourceVariable @ref, object value,
+        public static Tensor assign<T>(T @ref, object value,
             bool validate_shape = true,
             bool use_locking = true,
             string name = null)
@@ -110,7 +99,12 @@ namespace Tensorflow
             T value,
             bool use_locking = false,
             string name = null)
-            => @ref.assign_add(value, use_locking: use_locking, name: name);
+        {
+            if(tf.executing_eagerly())
+                return @ref.assign_add(value, use_locking: use_locking, name: name);
+            else
+                return gen_state_ops.assign_add(@ref, value, use_locking: use_locking, name: name);
+        }
 
         public static Tensor scatter_add(IVariableV1 @ref, Tensor indices, Tensor updates, bool use_locking = false, string name = null)
         {