Add set_weights and get_weights APIs

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

@@ -19,7 +19,7 @@ namespace Tensorflow.Keras
         List<IVariableV1> TrainableWeights { get; }
         List<IVariableV1> NonTrainableWeights { get; }
         List<IVariableV1> Weights { get; set; }
-        void set_weights(List<NDArray> weights);
+        void set_weights(IEnumerable<NDArray> weights);
         List<NDArray> get_weights();
         Shape OutputShape { get; }
         Shape BatchInputShape { get; }

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

@@ -75,7 +75,7 @@ namespace Tensorflow
         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 void set_weights(IEnumerable<NDArray> weights) => throw new NotImplementedException();
         public List<IVariableV1> NonTrainableWeights => throw new NotImplementedException();
 
         public Shape OutputShape => throw new NotImplementedException();

src/TensorFlowNET.Keras/Engine/Layer.cs

@@ -30,6 +30,9 @@ using Tensorflow.Training;
 using Tensorflow.Training.Saving.SavedModel;
 using Tensorflow.Util;
 using static Tensorflow.Binding;
+using Tensorflow.Framework;
+using Tensorflow.Sessions;
+
 
 namespace Tensorflow.Keras.Engine
 {
@@ -134,21 +137,53 @@ namespace Tensorflow.Keras.Engine
             }
         }
 
-        public virtual void set_weights(List<NDArray> 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 " +
                                             $"expecting {len(Weights)} weights.");
-            for (int i = 0; i < weights.Count(); i++)
+
+
+
+            // check if the shapes are compatible
+            var weight_index = 0;
+            foreach(var w in weights)
             {
-                if (weights[i].shape != Weights[i].shape)
+                if (!Weights[weight_index].AsTensor().is_compatible_with(w))
                 {
-                    throw new ValueError($"Layer weight shape {weights[i].shape} not compatible with provided weight shape {Weights[i].shape}");
+                    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();
             }
-            foreach (var (this_w, v_w) in zip(Weights, weights))
-                this_w.assign(v_w, read_value: true);
         }
 
         public List<NDArray> get_weights()