fix _apply_dense for Optimizer.

Author: Oceania2018

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

@@ -193,8 +193,7 @@ public Tensor dense(Tensor inputs,
                     Name = name
                 });
 
-                throw new NotImplementedException("");
-                //return layer.apply(inputs).Item1;
+                return layer.Apply(inputs);
             }
 
             /// <summary>

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

@@ -66,8 +66,8 @@ public Tensor dropout(Tensor x, Tensor keep_prob = null, Tensor noise_shape = nu
                 Tensor keep = null;
                 if (keep_prob != null)
                     keep = 1.0f - keep_prob;
-
-                return nn_ops.dropout_v2(x, rate: rate.Value, noise_shape: noise_shape, seed: seed, name: name);
+                var rate_tensor = rate.HasValue ? tf.constant(rate.Value) : keep;
+                return nn_ops.dropout_v2(x, rate: rate_tensor, noise_shape: noise_shape, seed: seed, name: name);
             }
 
             /// <summary>

src/TensorFlowNET.Core/Framework/meta_graph.cs

@@ -150,7 +150,7 @@ public static (Dictionary<string, IVariableV1>, ITensorOrOperation[]) import_sco
             var variables = graph.get_collection<IVariableV1>(tf.GraphKeys.GLOBAL_VARIABLES,
                                      scope: scope_to_prepend_to_names);
             var var_list = new Dictionary<string, IVariableV1>();
-            // variables.ForEach(v => var_list[ops.strip_name_scope(v.Name, scope_to_prepend_to_names)] = v);
+            variables.ForEach(v => var_list[ops.strip_name_scope(v.Name, scope_to_prepend_to_names)] = v);
 
             return (var_list, imported_return_elements);
         }
@@ -277,6 +277,11 @@ private static void add_collection_def(MetaGraphDef meta_graph_def,
                             var proto = x_ref_var.to_proto(export_scope);
                             col_def.BytesList.Value.Add(proto.ToByteString());
                         }
+                        else if(x is ResourceVariable x_res_var)
+                        {
+                            var proto = x_res_var.to_proto(export_scope);
+                            col_def.BytesList.Value.Add(proto.ToByteString());
+                        }
                     }
                     break;
                 case List<RefVariable> collection_list:

src/TensorFlowNET.Core/Functions/c_api.function.cs

@@ -31,8 +31,23 @@ public partial class c_api
         /// <param name="output_func_def"></param>
         /// <param name="status"></param>
         [DllImport(TensorFlowLibName)]
-        public static extern void TF_FunctionToFunctionDef(IntPtr func, IntPtr output_func_def, SafeStatusHandle status);
+        public static extern void TF_FunctionToFunctionDef(IntPtr func, SafeBufferHandle output_func_def, SafeStatusHandle status);
 
+        [DllImport(TensorFlowLibName)]
+        public static extern IntPtr TF_GraphToFunction(IntPtr fn_body, string fn_name,
+            bool append_hash_to_fn_name,
+            int num_opers, IntPtr[] opers,
+            int ninputs, TF_Output[] inputs,
+            int noutputs, TF_Output[] outputs,
+            IntPtr output_names,
+            IntPtr opts,
+            string description,
+            SafeStatusHandle status);
+
+        [DllImport(TensorFlowLibName)]
+        public static extern IntPtr TF_FunctionName(IntPtr func);
 
+        [DllImport(TensorFlowLibName)]
+        public static extern void TF_GraphCopyFunction(IntPtr g, IntPtr func, IntPtr grad, SafeStatusHandle status);
     }
 }

src/TensorFlowNET.Core/Gradients/math_grad.cs

@@ -327,8 +327,9 @@ public static Tensor[] _MeanGrad(Operation op, Tensor[] grads)
             var output_shape = op.outputs[0]._shape_tuple();
 
             Tensor result, factor_tensor;
-            if(input_shape != null &&
-                output_shape != null)
+            if(tf.executing_eagerly() 
+                && input_shape != null 
+                && output_shape != null)
             {
                 var input_size = np.prod(input_shape);
                 var output_size = np.prod(output_shape);
@@ -339,11 +340,7 @@ public static Tensor[] _MeanGrad(Operation op, Tensor[] grads)
             {
                 var input_shape_tensor = array_ops.shape(op.inputs[0]);
                 var output_shape_tensor = array_ops.shape(op.outputs[0]);
-                var factor = _safe_shape_div(math_ops.reduce_prod(input_shape_tensor), math_ops.reduce_prod(output_shape_tensor));
-                throw new NotImplementedException("");
-#pragma warning disable CS0162 // Unreachable code detected
-                factor_tensor = null;
-#pragma warning restore CS0162 // Unreachable code detected
+                factor_tensor = _safe_shape_div(math_ops.reduce_prod(input_shape_tensor), math_ops.reduce_prod(output_shape_tensor));
             }
 
             result = math_ops.truediv(sum_grad, math_ops.cast(factor_tensor, sum_grad.dtype));

src/TensorFlowNET.Core/Gradients/nn_grad.cs

@@ -128,10 +128,10 @@ public static Tensor[] _SparseSoftmaxCrossEntropyWithLogitsGrad(Operation op, Te
         [RegisterGradient("Conv2D")]
         public static Tensor[] _Conv2DGrad(Operation op, Tensor[] grads)
         {
-            var dilations = op.get_attr<int[]>("dilations");
-            var strides = op.get_attr<int[]>("strides");
+            var dilations = op.get_attr_list<int>("dilations");
+            var strides = op.get_attr_list<int>("strides");
             var padding = op.get_attr<string>("padding");
-            var explicit_paddings = op.get_attr<int[]>("explicit_paddings");
+            var explicit_paddings = op.get_attr_list<int>("explicit_paddings");
             var use_cudnn_on_gpu = op.get_attr<bool>("use_cudnn_on_gpu");
             var data_format = op.get_attr<string>("data_format");
             var shape = gen_array_ops.shape_n(new Tensor[] { op.inputs[0], op.inputs[1] });
@@ -287,8 +287,8 @@ public static Tensor[] _MaxPoolGrad(Operation op, Tensor[] grads)
                   op.inputs[0],
                   op.outputs[0],
                   grad,
-                  op.get_attr("ksize") as int[],
-                  op.get_attr("strides") as int[],
+                  op.get_attr_list<int>("ksize"),
+                  op.get_attr_list<int>("strides"),
                   padding: op.get_attr("padding").ToString(),
                   data_format: op.get_attr("data_format").ToString())
             };

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -293,12 +293,6 @@ public Operation create_op(string op_type, Tensor[] inputs, TF_DataType[] dtypes
 
             _create_op_helper(op, compute_device);
 
-            /*Console.Write($"create_op: {op_type} '{node_def.Name}'");
-            Console.Write($", inputs: {(inputs.Length == 0 ? "empty" : String.Join(", ", inputs.Select(x => x.name)))}");
-            Console.Write($", control_inputs: {(control_inputs.Length == 0 ? "empty" : String.Join(", ", control_inputs.Select(x => x.name)))}");
-            Console.Write($", outputs: {(op.outputs.Length == 0 ? "empty" : String.Join(", ", op.outputs.Select(x => x.name)))}");
-            Console.WriteLine();*/
-
             return op;
         }
 

src/TensorFlowNET.Core/Graphs/c_api.graph.cs

@@ -139,7 +139,7 @@ public partial class c_api
         /// <param name="status">TF_Status*</param>
         [DllImport(TensorFlowLibName)]
         public static extern void TF_GraphToGraphDef(IntPtr graph, SafeBufferHandle output_graph_def, SafeStatusHandle status);
-        
+
         /// <summary>
         /// Returns the number of dimensions of the Tensor referenced by `output`
         /// in `graph`.

src/TensorFlowNET.Core/Keras/ArgsDefinition/TensorLikeDataAdapterArgs.cs

@@ -0,0 +1,16 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class TensorLikeDataAdapterArgs
+    {
+        public Tensor X { get; set; }
+        public Tensor Y { get; set; }
+        public int BatchSize { get; set; }
+        public int Steps { get; set; }
+        public int Epochs { get; set; }
+        public bool Shuffle { get; set; }
+    }
+}

src/TensorFlowNET.Core/Keras/Engine/DataAdapters/DataHandler.cs

@@ -27,7 +27,9 @@ public class DataHandler
 
         public DataHandler(DataHandlerArgs args)
         {
+            this.args = args;
 
+            var adapter_cls = new TensorLikeDataAdapter(new TensorLikeDataAdapterArgs { });
         }
     }
 }