gradients not finished yet

src/TensorFlowNET.Core/Gradients/c_api.gradient.cs

@@ -24,6 +24,6 @@ namespace Tensorflow
         /// <param name="dy">TF_Output*</param>
         [DllImport(TensorFlowLibName)]
         public static extern void TF_AddGradientsWithPrefix(IntPtr g, string prefix, TF_Output[] y, int ny,
-            TF_Output[] x, int nx, TF_Output[] dx, IntPtr status, ref IntPtr dy);
+            TF_Output[] x, int nx, TF_Output[] dx, IntPtr status, IntPtr[] dy);
     }
 }

src/TensorFlowNET.Core/Gradients/gradients_impl.py.cs

@@ -37,6 +37,8 @@ namespace Tensorflow
             // ancestor graphs. This is necessary for correctly handling captured values.
             var curr_graph = src_graph;
 
+            if (stop_gradients == null)
+                stop_gradients = new Tensor[0];
             if (grad_ys == null)
                 grad_ys = new Tensor[ys.Length];
 
@@ -45,7 +47,7 @@ namespace Tensorflow
             all.AddRange(xs);
             all.AddRange(stop_gradients);
             all.AddRange(grad_ys);
-
+            
             // Iterate over the collected ops.
             /**
              * grads: op => list of gradients received on each output endpoint of the

src/TensorFlowNET.Core/Gradients/math_grad.py.cs

@@ -16,5 +16,17 @@ namespace Tensorflow
 
             return (grad, grad);
         }
+
+        public static (Tensor, Tensor) _RealDivGrad(Operation op, Tensor grad)
+        {
+            var x = op.inputs[0];
+            var y = op.inputs[1];
+
+            var sx = array_ops.shape(x);
+            var sy = array_ops.shape(y);
+           // rx, ry = gen_array_ops.broadcast_gradient_args(sx, sy)
+
+            return (grad, grad);
+        }
     }
 }

src/TensorFlowNET.Core/Operations/gen_array_ops.cs

@@ -59,5 +59,10 @@ namespace Tensorflow
 
             return _op.outputs[0];
         }
+
+        public static (Tensor, Tensor) broadcast_gradient_args(Tensor s0, Tensor s1, string name = "")
+        {
+            return (null, null);
+        }
     }
 }

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -42,7 +42,7 @@ namespace Tensorflow
         {
             get
             {
-                var dims = new long[rank];
+                var dims = new long[rank < 0 ? 0 : rank];
 
                 if (_handle == IntPtr.Zero)
                 {

src/TensorFlowNET.Core/Train/Optimizer.cs

@@ -14,6 +14,11 @@ namespace Tensorflow
     /// </summary>
     public abstract class Optimizer
     {
+        // Values for gate_gradients.
+        public static int GATE_NONE = 0;
+        public static int GATE_OP = 1;
+        public static int GATE_GRAPH = 2;
+
         public string Name { get; set; }
         public double LearningRate { get; set; }
         public Tensor LearningRateTensor { get; set; }
@@ -87,11 +92,15 @@ namespace Tensorflow
             var processors = var_list.Select(v => optimizer._get_processor(v)).ToList();
             var var_refs = processors.Select(x => x.target()).ToArray();
 
-            gradients_impl.gradients(new Tensor[] { loss }, var_refs, grad_ys: grad_loss,
+            var grads = gradients_impl.gradients(new Tensor[] { loss }, var_refs, grad_ys: grad_loss,
                 gate_gradients: (gate_gradients == GateGradientType.GATE_OP),
                 aggregation_method: aggregation_method,
                 colocate_gradients_with_ops: colocate_gradients_with_ops);
 
+            //if ((int)gate_gradients == Optimizer.GATE_GRAPH)
+                //grads = control_flow_ops.tuple(grads);
+
+
             return null;
         }
     }

src/TensorFlowNET.Core/ops.py.cs

@@ -8,6 +8,7 @@ using node_def_pb2 = Tensorflow;
 using Google.Protobuf;
 using System.Linq;
 using NumSharp.Core;
+using System.ComponentModel;
 
 namespace Tensorflow
 {
@@ -285,7 +286,20 @@ namespace Tensorflow
 
             return (oper, out_grads) =>
             {
-                return math_grad._AddGrad(op, out_grads);
+                switch (oper.type)
+                {
+                    case "Add":
+                        return math_grad._AddGrad(op, out_grads);
+                    case "RealDiv":
+                        return math_grad._RealDivGrad(op, out_grads);
+                    default:
+                        throw new NotImplementedException("get_gradient_function");
+                }
+                /*var result = typeof(math_grad).GetMethod($"_{op.type}Grad").Invoke(null, new object[] { op, out_grads });
+                var p1 = result.GetType().GetProperty("Item1");
+                var p2 = result.GetType().GetProperty("Item2");
+
+                return (p1.GetValue(result, null) as Tensor, p2.GetValue(result, null) as Tensor);*/
             };
         }
     }

test/TensorFlowNET.Examples/LinearRegression.cs

@@ -36,8 +36,8 @@ namespace TensorFlowNET.Examples
             var W = tf.Variable(rng.randn<double>(), name: "weight");
             var b = tf.Variable(rng.randn<double>(), name: "bias");
 
-            var part1 = tf.multiply(X, W);
-            var pred = tf.add(part1, b);
+            var mul = tf.multiply(X, W);
+            var pred = tf.add(mul, b);
 
             // Mean squared error
             var sub = pred - Y;

test/TensorFlowNET.UnitTest/CApiGradientsTest.cs

@@ -111,12 +111,11 @@ namespace TensorFlowNET.UnitTest
                 var grad_inputs_op = FloatConst2x2(graph_, s_, grad_inputs_val, "GradInputs");
                 grad_inputs[0] = new TF_Output(grad_inputs_op, 0);
 
-                IntPtr handle = IntPtr.Zero;
+                IntPtr[] handles = new IntPtr[2] { IntPtr.Zero, IntPtr.Zero };
                 c_api.TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
-                                      ninputs, grad_inputs, s_, ref handle);
-
-                grad_outputs[0] = Marshal.PtrToStructure<TF_Output>(handle);
-                var op = new Operation(handle);
+                                      ninputs, grad_inputs, s_, handles);
+                
+                var op = new Operation(handles[0]);
             }
             else
             {