define gamma and beta as VariableV1

src/TensorFlowNET.Core/APIs/tf.nn.cs

@@ -115,8 +115,8 @@ namespace Tensorflow
             public Tensor relu(Tensor features, string name = null) => gen_nn_ops.relu(features, name);
 
             public Tensor[] fused_batch_norm(Tensor x,
-                RefVariable scale,
-                RefVariable offset,
+                VariableV1 scale,
+                VariableV1 offset,
                 Tensor mean = null,
                 Tensor variance = null,
                 float epsilon = 0.001f,

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

@@ -37,8 +37,8 @@ namespace Tensorflow.Keras.Layers
         private IInitializer gamma_initializer;
         private IInitializer moving_mean_initializer;
         private IInitializer moving_variance_initializer;
-        private RefVariable gamma;
-        private RefVariable beta;
+        private VariableV1 gamma;
+        private VariableV1 beta;
         private RefVariable moving_mean;
         private RefVariable moving_variance;
 
@@ -95,7 +95,7 @@ namespace Tensorflow.Keras.Layers
             var param_shape = new int[] { input_shape.dims[axis[0]] };
 
             if (scale)
-                gamma = (RefVariable)add_weight("gamma",
+                gamma = add_weight("gamma",
                     param_shape,
                     dtype: param_dtype,
                     initializer: gamma_initializer,
@@ -104,7 +104,7 @@ namespace Tensorflow.Keras.Layers
                 throw new NotImplementedException("add_weight gamma");
 
             if (center)
-                beta = (RefVariable)add_weight("beta",
+                beta = add_weight("beta",
                     param_shape,
                     dtype: param_dtype,
                     initializer: beta_initializer,

src/TensorFlowNET.Core/Keras/Utils/base_layer_utils.cs

@@ -32,36 +32,21 @@ namespace Tensorflow.Keras.Utils
         /// <param name="initializer"></param>
         /// <param name="trainable"></param>
         /// <returns></returns>
-        public static RefVariable make_variable(string name,
+        public static VariableV1 make_variable(string name,
             int[] shape,
             TF_DataType dtype = TF_DataType.TF_FLOAT,
             IInitializer initializer = null,
-            bool trainable = true) => make_variable(name, shape, dtype, initializer, trainable, true);
-
-        /// <summary>
-        /// Adds a new variable to the layer.
-        /// </summary>
-        /// <param name="name"></param>
-        /// <param name="shape"></param>
-        /// <param name="dtype"></param>
-        /// <param name="initializer"></param>
-        /// <param name="trainable"></param>
-        /// <returns></returns>
-        public static RefVariable make_variable(string name,
-            int[] shape,
-            TF_DataType dtype = TF_DataType.TF_FLOAT,
-            IInitializer initializer = null,
-            bool trainable = true,
-            bool use_resource = true)
+            bool trainable = true)
         {
             var initializing_from_value = false;
+            bool use_resource = true;
 
             ops.init_scope();
 
             Func<Tensor> init_val = () => initializer.call(new TensorShape(shape), dtype: dtype);
 
             var variable_dtype = dtype.as_base_dtype();
-            var v = tf.Variable(init_val);
+            var v = tf.VariableV1(init_val);
 
             return v;
         }

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

@@ -97,9 +97,9 @@ namespace Tensorflow
         /// <param name="is_training"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor[] fused_batch_norm(Tensor x, 
-            RefVariable scale,
-            RefVariable offset,
+        public static Tensor[] fused_batch_norm(Tensor x,
+            VariableV1 scale,
+            VariableV1 offset,
             Tensor mean,
             Tensor variance,
             float epsilon = 0.001f,

src/TensorFlowNET.Core/tensorflow.cs

@@ -58,6 +58,21 @@ namespace Tensorflow
                 dtype: dtype);
         }
 
+        public VariableV1 VariableV1<T>(T data,
+            bool trainable = true,
+            bool validate_shape = true,
+            string name = null,
+            TF_DataType dtype = TF_DataType.DtInvalid,
+            bool use_resource = false)
+        {
+            return Tensorflow.variable_scope.default_variable_creator(data,
+                trainable: trainable,
+                validate_shape: validate_shape,
+                name: name,
+                dtype: dtype,
+                use_resource: use_resource);
+        }
+
         public unsafe Tensor placeholder(TF_DataType dtype, TensorShape shape = null, string name = null)
         {
             return gen_array_ops.placeholder(dtype, shape, name);