Add set_weights and get_weighst APIs

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 @@ public interface ILayer: IWithTrackable, IKerasConfigable
         List<IVariableV1> TrainableWeights { get; }
         List<IVariableV1> NonTrainableWeights { get; }
         List<IVariableV1> Weights { get; set; }
+        void set_weights(IEnumerable<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 @@ limitations under the License.
 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 @@ public abstract class RnnCell : ILayer, RNNArgs.IRnnArgCell
 
         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(IEnumerable<NDArray> weights) => throw new NotImplementedException();
         public List<IVariableV1> NonTrainableWeights => throw new NotImplementedException();
 
         public Shape OutputShape => throw new NotImplementedException();
@@ -84,8 +88,6 @@ public abstract class RnnCell : ILayer, RNNArgs.IRnnArgCell
         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

@@ -30,6 +30,9 @@ limitations under the License.
 using Tensorflow.Training.Saving.SavedModel;
 using Tensorflow.Util;
 using static Tensorflow.Binding;
+using Tensorflow.Framework;
+using Tensorflow.Sessions;
+
 
 namespace Tensorflow.Keras.Engine
 {
@@ -134,6 +137,62 @@ public virtual List<IVariableV1> Weights
             }
         }
 
+        public virtual void set_weights(IEnumerable<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 " +
+                                            $"expecting {len(Weights)} weights.");
+
+
+
+            // check if the shapes are compatible
+            var weight_index = 0;
+            foreach(var w in weights)
+            {
+                if (!Weights[weight_index].AsTensor().is_compatible_with(w))
+                {
+                    throw new ValueError($"Layer weight shape {w.shape} not compatible with provided weight shape {Weights[weight_index].shape}");
+                }
+                weight_index++;
+            }
+
+            if (tf.executing_eagerly())
+            {
+                foreach (var (this_w, v_w) in zip(Weights, weights))
+                    this_w.assign(v_w, read_value: true);
+            }
+            else
+            {
+                // TODO(Wanglongzhi2001):seems like there exist some bug in graph mode when define model, so uncomment the following when it fixed.
+
+                //Tensors assign_ops = new Tensors();
+                //var feed_dict = new FeedDict();
+
+                //Graph g = tf.Graph().as_default();
+                //foreach (var (this_w, v_w) in zip(Weights, weights))
+                //{
+                //    var tf_dtype = this_w.dtype;
+                //    var placeholder_shape = v_w.shape;
+                //    var assign_placeholder = tf.placeholder(tf_dtype, placeholder_shape);
+                //    var assign_op = this_w.assign(assign_placeholder);
+                //    assign_ops.Add(assign_op);
+                //    feed_dict.Add(assign_placeholder, v_w);
+                //}
+                //var sess = tf.Session().as_default();
+                //sess.run(assign_ops, feed_dict);
+
+                //g.Exit();
+            }
+        }
+
+        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;