fix sign

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

@@ -364,8 +364,8 @@ namespace Tensorflow
         public Tensor divide<T>(Tensor x, T[] y, string name = null) where T : struct
             => x / ops.convert_to_tensor(y, dtype: x.dtype.as_base_dtype(), name: "y");
 
-        public Tensor pow<T1, T2>(T1 x, T2 y) 
-            => gen_math_ops.pow(x, y);
+        public Tensor pow<T1, T2>(T1 x, T2 y, string name = "pow")
+            => gen_math_ops.pow(x, y, name: name);
 
         /// <summary>
         /// Divides `x / y` elementwise, rounding toward the most negative integer.

src/TensorFlowNET.Core/Gradients/math_grad.cs

@@ -33,10 +33,14 @@ namespace Tensorflow.Gradients
             var x = op.inputs[0];
             var grad = grads[0];
 
-            return new Tensor[] { gen_ops.mul(grad, gen_math_ops.sign(x)) };
+            return new Tensor[] { grad * math_ops.sign(x) };
         }
 
         [RegisterGradient("AddV2")]
+        public static Tensor[] _AddV2Grad(Operation op, Tensor[] grads)
+            => _AddGrad(op, grads);
+
+        [RegisterGradient("Add")]
         public static Tensor[] _AddGrad(Operation op, Tensor[] grads)
         {
             var x = op.inputs[0];

src/TensorFlowNET.Core/Operations/OpDefLibrary.cs

@@ -65,10 +65,6 @@ namespace Tensorflow
                 var base_types = new List<TF_DataType>();
                 var types = new List<TF_DataType>();
 
-#if DEBUG
-                if (op_type_name == "FusedBatchNormGradV3")
-                    ;
-#endif
                 // Perform input type inference
                 foreach (var input_arg in op_def.InputArg)
                 {

src/TensorFlowNET.Core/Operations/Operation.Input.cs

@@ -40,13 +40,11 @@ namespace Tensorflow
             }
             return num;
         }
-#if SERIALIZABLE
-        [JsonIgnore]
-#endif
         public int NumInputs => c_api.TF_OperationNumInputs(_handle);
         private TF_DataType[] _input_types => _inputs_val._inputs.Select(x => x.dtype).ToArray();
 
         private InputList _inputs_val;
+
         public InputList inputs
         {
             get
@@ -69,8 +67,10 @@ namespace Tensorflow
             }
         }
 
-        public int NumControlInputs => c_api.TF_OperationNumControlInputs(_handle);
+        public int NumControlInputs
+            => _handle == IntPtr.Zero ? 0 : c_api.TF_OperationNumControlInputs(_handle);
 
+        Operation[] _control_inputs;
         /// <summary>
         /// The `Operation` objects on which this op has a control dependency.
         /// 
@@ -87,7 +87,9 @@ namespace Tensorflow
         {
             get
             {
-                return GetControlInputs();
+                if (_control_inputs == null || _control_inputs.Length == 0)
+                    _control_inputs = GetControlInputs();
+                return _control_inputs;
             }
         }
 

src/TensorFlowNET.Core/Operations/Operation.Output.cs

@@ -26,9 +26,6 @@ namespace Tensorflow
 {
     public partial class Operation
     {
-#if SERIALIZABLE
-        [JsonIgnore]
-#endif
         public int NumOutputs => c_api.TF_OperationNumOutputs(_handle);
         public TF_DataType OutputType(int index) => c_api.TF_OperationOutputType(_tf_output(index));
 

src/TensorFlowNET.Core/Operations/Operation.cs

@@ -154,11 +154,6 @@ namespace Tensorflow
         public Operation(NodeDef node_def, Graph g, Tensor[] inputs = null, TF_DataType[] output_types = null, ITensorOrOperation[] control_inputs = null, TF_DataType[] input_types = null, string original_op = "", OpDef op_def = null)
         {
             _graph = g;
-
-#if DEBUG
-            if (node_def.Name == "define_second_stage_train/gradients/define_loss/conv_lobj_branch/batch_normalization/cond/FusedBatchNormV3_1_grad/FusedBatchNormGradV3")
-                ;
-#endif
             // Build the list of control inputs.
             var control_input_ops = new List<Operation>();
             if (control_inputs != null)

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -140,6 +140,14 @@ namespace Tensorflow
         }
 
         public static Tensor add<Tx, Ty>(Tx x, Ty y, string name = null)
+        {
+            // forward_compatible(2019, 6, 25):
+            var _op = _op_def_lib._apply_op_helper("Add", name, args: new { x, y });
+
+            return _op.output;
+        }
+
+        public static Tensor add_v2<Tx, Ty>(Tx x, Ty y, string name = null)
         {
             // forward_compatible(2019, 6, 25):
             var _op = _op_def_lib._apply_op_helper("AddV2", name, args: new { x, y });
@@ -213,7 +221,7 @@ namespace Tensorflow
             return op.outputs[0];
         }
 
-        public static Tensor sign(Tensor x, string name = "Sign")
+        public static Tensor sign<T>(T x, string name = "Sign")
         {
             var op = _op_def_lib._apply_op_helper("Sign", name: name, args: new {x});
 
@@ -448,7 +456,7 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
-        public static Tensor cast(Tensor x, TF_DataType DstT, bool Truncate= false, string name= "")
+        public static Tensor cast(Tensor x, TF_DataType DstT, bool Truncate= false, string name= null)
         {
             var _op = _op_def_lib._apply_op_helper("Cast", name, args: new { x, DstT, Truncate });
 

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -35,14 +35,17 @@ namespace Tensorflow
                 x = ops.convert_to_tensor(x, name: "x");
                 if (x.dtype.is_complex())
                     throw new NotImplementedException("math_ops.abs for dtype.is_complex");
-                    //return gen_math_ops.complex_abs(x, Tout: x.dtype.real_dtype, name: name);
+                //return gen_math_ops.complex_abs(x, Tout: x.dtype.real_dtype, name: name);
                 return gen_math_ops._abs(x, name: name);
             });
         }
 
-        public static Tensor add<Tx, Ty>(Tx x, Ty y, string name = null) 
+        public static Tensor add<Tx, Ty>(Tx x, Ty y, string name = null)
             => gen_math_ops.add(x, y, name);
 
+        public static Tensor add_v2<Tx, Ty>(Tx x, Ty y, string name = null)
+            => gen_math_ops.add_v2(x, y, name);
+
         /// <summary>
         /// Adds all input tensors element-wise.
         /// </summary>
@@ -53,21 +56,38 @@ namespace Tensorflow
         {
             inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs);
 
-            if(inputs.Length == 1)
+            if (inputs.Length == 1)
             {
                 var values = inputs[0];
                 if (name != null)
                     return array_ops.identity(values, name: name);
                 return values;
             }
-            
+
             return gen_math_ops.add_n(inputs, name: name);
         }
 
+        public static Tensor cast(RefVariable 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 tf_with(ops.name_scope(name, "Cast", new { x }), scope =>
+            {
+                name = scope;
+                var t_x = ops.convert_to_tensor(x, name: "x");
+                if (t_x.dtype.as_base_dtype() != base_type)
+                    t_x = gen_math_ops.cast(t_x, base_type, name: name);
+
+                return x;
+            });
+        }
+
         public static Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, string name = null)
         {
             var base_type = dtype.as_base_dtype();
-            if(base_type == x.dtype)
+            if (base_type == x.dtype)
                 return x;
 
             return tf_with(ops.name_scope(name, "Cast", new { x }), scope =>
@@ -98,13 +118,13 @@ namespace Tensorflow
 
         public static Tensor cumsum<T>(Tensor x, T axis = default, bool exclusive = false, bool reverse = false, string name = null)
         {
-            return tf_with(ops.name_scope(name, "Cumsum", new {x}), scope =>
-            {
-                name = scope;
-                x = ops.convert_to_tensor(x, name: "x");
+            return tf_with(ops.name_scope(name, "Cumsum", new { x }), scope =>
+              {
+                  name = scope;
+                  x = ops.convert_to_tensor(x, name: "x");
 
-                return gen_math_ops.cumsum(x, axis: axis, exclusive: exclusive, reverse: reverse, name: name);
-            });
+                  return gen_math_ops.cumsum(x, axis: axis, exclusive: exclusive, reverse: reverse, name: name);
+              });
         }
 
         /// <summary>
@@ -221,7 +241,7 @@ namespace Tensorflow
 
         public static Tensor reduce_mean(Tensor[] input_tensors, int? axis = null, bool keepdims = false, string name = null)
         {
-            if(axis == null)
+            if (axis == null)
             {
                 var r = _ReductionDims(input_tensors, axis);
                 var m = gen_math_ops.mean(input_tensors, r, keepdims, name);
@@ -263,14 +283,8 @@ namespace Tensorflow
             return gen_math_ops.sigmoid(x_tensor, name: name);
         }
 
-        public static Tensor sign(Tensor x, string name = null)
-        {
-            return tf_with(ops.name_scope(name, "Sign", new {x}), scope =>
-            {
-                x = ops.convert_to_tensor(x, name: "x");
-                return gen_math_ops.sign(x);
-            });
-        }
+        public static Tensor sign<T>(T x, string name = null)
+            => gen_math_ops.sign(x, name: name);
 
         /// <summary>
         /// Returns (x - y)(x - y) element-wise.

src/TensorFlowNET.Core/Tensors/Tensor.Operators.cs

@@ -328,7 +328,7 @@ namespace Tensorflow
                 switch (name.ToLowerInvariant())
                 {
                     case "add":
-                        result = math_ops.add(x1, y1, name: scope);
+                        result = math_ops.add_v2(x1, y1, name: scope);
                         break;
                     case "div":
                         result = math_ops.div(x1, y1, name: scope);