overload tf.reduce_mean

Author: Oceania2018

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

@@ -474,7 +474,7 @@ public Tensor sum(Tensor input, int axis, bool keep_dims = false, string name =
         public Tensor reduce_mean(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null, int? reduction_indices = null)
             => math_ops.reduce_mean(input_tensor, axis: axis, keepdims: keepdims, name: name, reduction_indices: reduction_indices);
 
-        public Tensor reduce_mean(Tensor[] input_tensors, int axis, bool keepdims = false, string name = null)
+        public Tensor reduce_mean(Tensor[] input_tensors, int? axis = null, bool keepdims = false, string name = null)
             => math_ops.reduce_mean(input_tensors, axis: axis, keepdims: keepdims, name: name);
 
         public Tensor round(Tensor x, string name = null)

src/TensorFlowNET.Core/Operations/array_ops.cs

@@ -228,6 +228,16 @@ private static Tensor expand_dims_v2(Tensor input, int axis, string name = null)
         public static Tensor rank(Tensor input, string name = null)
             => rank_internal(input, name, optimize: true);
 
+        public static Tensor rank(Tensor[] inputs, string name = null)
+        {
+            return tf_with(ops.name_scope(name, "Rank", new { inputs }), scope =>
+            {
+                name = scope;
+                var input_tensor = ops.convert_to_tensor(inputs);
+                return constant_op.constant(input_tensor.NDims, dtype: tf.int32, name: name);
+            });
+        }
+
         public static Tensor rank_internal(Tensor input, string name = null, bool optimize = true)
         {
             return tf_with(ops.name_scope(name, "Rank", new List<Tensor> { input }), scope =>

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -219,10 +219,19 @@ public static Tensor reduce_mean(Tensor input_tensor, int[] axis = null, bool ke
             }
         }
 
-        public static Tensor reduce_mean(Tensor[] input_tensors, int axis, bool keepdims = false, string name = null)
+        public static Tensor reduce_mean(Tensor[] input_tensors, int? axis = null, bool keepdims = false, string name = null)
         {
-            var m = gen_math_ops.mean(input_tensors, axis, keepdims, name);
-            return _may_reduce_to_scalar(keepdims, axis, m);
+            if(axis == null)
+            {
+                var r = _ReductionDims(input_tensors, axis);
+                var m = gen_math_ops.mean(input_tensors, r, keepdims, name);
+                return _may_reduce_to_scalar(keepdims, axis, m);
+            }
+            else
+            {
+                var m = gen_math_ops.mean(input_tensors, axis, keepdims, name);
+                return _may_reduce_to_scalar(keepdims, axis, m);
+            }
         }
 
         /// <summary>
@@ -492,7 +501,7 @@ private static Tensor _may_reduce_to_scalar(bool keepdims, int[] axis, Tensor ou
             return output;
         }
 
-        private static Tensor _may_reduce_to_scalar(bool keepdims, int axis, Tensor output)
+        private static Tensor _may_reduce_to_scalar(bool keepdims, int? axis, Tensor output)
         {
             return output;
         }
@@ -515,6 +524,11 @@ private static int _ReductionDims(Tensor x, int axis)
             return axis;
         }
 
+        private static Tensor _ReductionDims(Tensor[] x, int? axis = null, string name = null)
+        {
+            return range(0, array_ops.rank(x));
+        }
+
         private static Tensor _ReductionDims(Tensor x, int[] axis)
         {
             if (axis != null)