skip layer wihtout trainable weights when save_weights.

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

@@ -318,16 +318,16 @@ namespace Tensorflow.Operations
             return _op.outputs;
         }
 
-        public static Tensor[] fused_batch_norm_v3(Tensor x,
-        Tensor scale,
-        Tensor offset,
-        IVariableV1 mean,
-        IVariableV1 variance,
-        float epsilon = 0.0001f,
-        float exponential_avg_factor = 1.0f,
-        string data_format = "NHWC",
-        bool is_training = true,
-        string name = null)
+        public static Tensors fused_batch_norm_v3(Tensor x,
+            IVariableV1 scale,
+            IVariableV1 offset,
+            IVariableV1 mean,
+            IVariableV1 variance,
+            float epsilon = 0.0001f,
+            float exponential_avg_factor = 1.0f,
+            string data_format = "NHWC",
+            bool is_training = true,
+            string name = null)
         {
             if (tf.executing_eagerly())
             {
@@ -337,8 +337,8 @@ namespace Tensorflow.Operations
                     x,
                     scale,
                     offset,
-                    mean.AsTensor(),
-                    variance.AsTensor(),
+                    mean,
+                    variance,
                     "epsilon", epsilon,
                     "exponential_avg_factor", exponential_avg_factor,
                     "data_format", data_format,

src/TensorFlowNET.Core/Operations/nn_impl.py.cs

@@ -107,9 +107,6 @@ namespace Tensorflow
             string name = null,
             float exponential_avg_factor = 1.0f)
         {
-            x = ops.convert_to_tensor(x, name: "input");
-            var scale_tensor = ops.convert_to_tensor(scale, name: "scale");
-            var offset_tensor = ops.convert_to_tensor(offset, name: "offset");
             /*if (mean == null)
                 mean = constant_op.constant(new float[0]);
             if (variance == null)
@@ -118,11 +115,11 @@ namespace Tensorflow
             epsilon = epsilon > min_epsilon ? epsilon : min_epsilon;
 
             var results = gen_nn_ops.fused_batch_norm_v3(x,
-                scale_tensor,
-                offset_tensor,
+                scale,
+                offset,
                 mean,
                 variance,
-                epsilon,
+                epsilon: epsilon,
                 exponential_avg_factor: exponential_avg_factor,
                 data_format: data_format,
                 is_training: is_training,

src/TensorFlowNET.Core/Variables/BaseResourceVariable.cs

@@ -163,11 +163,14 @@ namespace Tensorflow
         /// </summary>
         /// <returns></returns>
         protected Tensor read_value()
-            => tf_with(ops.name_scope("Read"), delegate
-            {
-                var value = _read_variable_op();
-                return array_ops.identity(value);
+        {
+            var value = tf_with(ops.name_scope("Read"), delegate
+            { 
+                return _read_variable_op(); 
             });
+            return array_ops.identity(value);
+        }
+            
 
         public Tensor assign_add<T>(T delta, bool use_locking = false, string name = null, bool read_value = true)
         {

src/TensorFlowNET.Keras/Saving/hdf5_format.cs

@@ -101,26 +101,28 @@ namespace Tensorflow.Keras.Saving
                 if (success)
                     original_backend = attr.First();
             }
-            List<ILayer> filtered_layers = new List<ILayer>();
-            List<IVariableV1> weights;
+
+            var filtered_layers = new List<ILayer>();
             foreach (var layer in layers)
             {
-                weights = _legacy_weights(layer);
+                var weights = _legacy_weights(layer);
                 if (weights.Count > 0)
-                {
                     filtered_layers.append(layer);
-                }
             }
+
             string[] layer_names = load_attributes_from_hdf5_group(f, "layer_names");
             var filtered_layer_names = new List<string>();
             foreach(var name in layer_names)
             {
+                if (!filtered_layers.Select(x => x.Name).Contains(name))
+                    continue;
                 long g = H5G.open(f, name);
                 var weight_names = load_attributes_from_hdf5_group(g, "weight_names");
                 if (weight_names.Count() > 0)
                     filtered_layer_names.Add(name);
                 H5G.close(g);
             }
+
             layer_names = filtered_layer_names.ToArray();
             if (layer_names.Length != filtered_layers.Count())
                 throw new ValueError("You are trying to load a weight file " +
@@ -133,7 +135,6 @@ namespace Tensorflow.Keras.Saving
                 var weight_values = new List<NDArray>();
                 long g = H5G.open(f, name);
                 var weight_names = load_attributes_from_hdf5_group(g, "weight_names");
-                var get_Name = "";
                 foreach (var i_ in weight_names)
                 {
                     (bool success, Array result) = Hdf5.ReadDataset<float>(g, i_);
@@ -153,6 +154,7 @@ namespace Tensorflow.Keras.Saving
                         $"{weight_values.Count()} elements.");
                 weight_value_tuples.AddRange(zip(symbolic_weights, weight_values));
             }
+
             keras.backend.batch_set_value(weight_value_tuples);
         }
         public static void toarrayf4(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
@@ -175,43 +177,37 @@ namespace Tensorflow.Keras.Saving
             Hdf5.WriteAttribute(f, "keras_version", "2.5.0");
 
             long g = 0, crDataGroup=0;
-            List<IVariableV1> weights = new List<IVariableV1>();
-            //List<IVariableV1> weight_values = new List<IVariableV1>();
-            List<string> weight_names = new List<string>();
-            foreach (var layer in layers) {
-                weight_names = new List<string>();
-                g = Hdf5.CreateOrOpenGroup(f, Hdf5Utils.NormalizedName(layer.Name));
-                weights = _legacy_weights(layer);
-                //weight_values= keras.backend.batch_get_value(weights);
+            foreach (var layer in layers)
+            {
+                var weights = _legacy_weights(layer);
+                if (weights.Count == 0)
+                    continue;
+
+                var weight_names = new List<string>();
+                // weight_values= keras.backend.batch_get_value(weights);
                 foreach (var weight in weights)
-                {
                     weight_names.Add(weight.Name);
-                }
+                
+                g = Hdf5.CreateOrOpenGroup(f, Hdf5Utils.NormalizedName(layer.Name));
                 save_attributes_to_hdf5_group(g, "weight_names", weight_names.ToArray());
-                Tensor tensor = null;
-                foreach (var (name, val) in zip(weight_names, weights)) {
-               
-                     tensor = val.AsTensor();
+                foreach (var (name, val) in zip(weight_names, weights))
+                {
+                    var tensor = val.AsTensor();
                     if (name.IndexOf("/") > 1)
                     {
                         crDataGroup = Hdf5.CreateOrOpenGroup(g, Hdf5Utils.NormalizedName(name.Split('/')[0]));
                         WriteDataset(crDataGroup, name.Split('/')[1], tensor);
                         Hdf5.CloseGroup(crDataGroup);
                     }
-                    else {
+                    else
+                    {
                         WriteDataset(crDataGroup, name, tensor);
                     }
-                   
-                    tensor = null;
-               }
+                }
                 Hdf5.CloseGroup(g);
-                weight_names = null;
             }
-            weights = null;
-           // weight_values = null;
-
-  
         }
+
         private static void save_attributes_to_hdf5_group(long f,string name ,Array data)
         {
             int num_chunks = 1;