Add api set_weights and get_weights

src/TensorFlowNET.Core/Keras/Layers/ILayer.cs

@@ -1,5 +1,6 @@
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Saving;
+using Tensorflow.NumPy;
 using Tensorflow.Training;
 
 namespace Tensorflow.Keras
@@ -18,6 +19,8 @@ namespace Tensorflow.Keras
         List<IVariableV1> TrainableWeights { get; }
         List<IVariableV1> NonTrainableWeights { get; }
         List<IVariableV1> Weights { get; set; }
+        void set_weights(List<NDArray> weights);
+        List<NDArray> get_weights();
         Shape OutputShape { get; }
         Shape BatchInputShape { get; }
         TensorShapeConfig BuildInputShape { get; }

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

@@ -21,6 +21,7 @@ using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.ArgsDefinition.Rnn;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Saving;
+using Tensorflow.NumPy;
 using Tensorflow.Operations;
 using Tensorflow.Train;
 using Tensorflow.Util;
@@ -71,7 +72,10 @@ namespace Tensorflow
 
         public List<IVariableV1> TrainableVariables => throw new NotImplementedException();
         public List<IVariableV1> TrainableWeights => throw new NotImplementedException();
-        public List<IVariableV1> Weights => throw new NotImplementedException();
+        public List<IVariableV1> Weights { get => throw new NotImplementedException(); set => throw new NotImplementedException(); }
+
+        public List<NDArray> get_weights() => throw new NotImplementedException();
+        public void set_weights(List<NDArray> weights) => throw new NotImplementedException();
         public List<IVariableV1> NonTrainableWeights => throw new NotImplementedException();
 
         public Shape OutputShape => throw new NotImplementedException();
@@ -84,8 +88,6 @@ namespace Tensorflow
         protected bool built = false;
         public bool Built => built;
 
-        List<IVariableV1> ILayer.Weights { get => throw new NotImplementedException(); set => throw new NotImplementedException(); }
-
         public RnnCell(bool trainable = true,
             string name = null,
             TF_DataType dtype = TF_DataType.DtInvalid,

src/TensorFlowNET.Keras/Engine/Layer.cs

@@ -120,6 +120,30 @@ namespace Tensorflow.Keras.Engine
             }
         }
 
+        public virtual void set_weights(List<NDArray> weights)
+        {
+            if (Weights.Count() != weights.Count()) throw new ValueError(
+                                            $"You called `set_weights` on layer \"{this.name}\"" +
+                                            $"with a weight list of length {len(weights)}, but the layer was " +
+                                            $"expecting {len(Weights)} weights.");
+            for (int i = 0; i < weights.Count(); i++)
+            {
+                if (weights[i].shape != Weights[i].shape)
+                {
+                    throw new ValueError($"Layer weight shape {weights[i].shape} not compatible with provided weight shape {Weights[i].shape}");
+                }
+            }
+            foreach (var (this_w, v_w) in zip(Weights, weights))
+                this_w.assign(v_w, read_value: true);
+        }
+
+        public List<NDArray> get_weights()
+        {
+            List<NDArray > weights = new List<NDArray>();
+            weights.AddRange(Weights.ConvertAll(x => x.numpy())); 
+            return weights;
+        }
+
         protected int id;
         public int Id => id;
         protected string name;