fixed Repeated Abs name #442

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

@@ -441,7 +441,7 @@ namespace Tensorflow
             return math_ops.reduce_sum(input, keepdims: keepdims, name: name);
         }
 
-        public Tensor reduce_sum(Tensor input, int[] axis, int? reduction_indices = null, 
+        public Tensor reduce_sum(Tensor input, TensorShape axis, int? reduction_indices = null, 
             bool keepdims = false, string name = null)
             => math_ops.reduce_sum(input, axis, keepdims: keepdims, name: name);
 
@@ -456,6 +456,9 @@ namespace Tensorflow
         public Tensor reduce_max(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
             => math_ops.reduce_max(input_tensor, axis, keepdims, name);
 
+        public Tensor reduce_max(Tensor input_tensor, int axis, bool keepdims = false, string name = null)
+            => math_ops.reduce_max(input_tensor, axis, keepdims, name);
+
         public Tensor reduce_min(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
             => math_ops.reduce_min(input_tensor, axis, keepdims, name);
 

src/TensorFlowNET.Core/Operations/Operation.cs

@@ -186,6 +186,11 @@ namespace Tensorflow
             if (op_def == null)
                 op_def = g.GetOpDef(node_def.Op);
 
+            if (node_def.Name.Equals("learn_rate/cond/pred_id"))
+            {
+
+            }
+
             var grouped_inputs = _reconstruct_sequence_inputs(op_def, inputs, node_def.Attr);
             _handle = ops._create_c_op(g, node_def, grouped_inputs, control_input_ops.ToArray());
             _is_stateful = op_def.IsStateful;

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -629,9 +629,9 @@ namespace Tensorflow
 
         public static Tensor _abs(Tensor x, string name = null)
         {
-            var _op = _op_def_lib._apply_op_helper("Abs", name, new { x });
+            var _op = _op_def_lib._apply_op_helper("Abs", name, args: new { x });
 
-            return _op.outputs[0];
+            return _op.output;
         }
 
         public static Tensor _any<Tx, Ty>(Tx input, Ty axis, bool keep_dims = false, string name = null)
@@ -662,14 +662,7 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
-        public static Tensor _sum(Tensor input, Tensor axis = null, bool keep_dims = false, string name = null)
-        {
-            var _op = _op_def_lib._apply_op_helper("Sum", name, args: new { input, reduction_indices = axis, keep_dims });
-
-            return _op.outputs[0];
-        }
-
-        public static Tensor _sum(Tensor input, int axis, bool keep_dims = false, string name = null)
+        public static Tensor _sum<Tx, Ty>(Tx input, Ty axis = default, bool keep_dims = false, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Sum", name, args: new { input, reduction_indices = axis, keep_dims });
 

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -31,6 +31,7 @@ namespace Tensorflow
         {
             return tf_with(ops.name_scope(name, "Abs", new { x }), scope =>
             {
+                name = scope;
                 x = ops.convert_to_tensor(x, name: "x");
                 if (x.dtype.is_complex())
                     throw new NotImplementedException("math_ops.abs for dtype.is_complex");
@@ -379,6 +380,13 @@ namespace Tensorflow
             return _may_reduce_to_scalar(keepdims, axis, max);
         }
 
+        public static Tensor reduce_max(Tensor input_tensor, int axis, bool keepdims = false, string name = null)
+        {
+            var r = _ReductionDims(input_tensor, axis);
+            var max = gen_math_ops._max(input_tensor, r, keepdims, name);
+            return _may_reduce_to_scalar(keepdims, axis, max);
+        }
+
         public static Tensor reduce_min(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
         {
             var r = _ReductionDims(input_tensor, axis);
@@ -434,15 +442,14 @@ namespace Tensorflow
 
         public static Tensor reduce_sum(Tensor input_tensor, int[] axis, bool keepdims = false, string name = null)
         {
-            var r = _ReductionDims(input_tensor, axis);
-            var m = gen_math_ops._sum(input_tensor, r, keep_dims: keepdims, name: name);
+            var m = gen_math_ops._sum(input_tensor, axis, keep_dims: keepdims, name: name);
             return _may_reduce_to_scalar(keepdims, axis, m);
         }
 
         public static Tensor reduce_sum(Tensor input_tensor, int axis, bool keepdims = false, string name = null)
         {
             var m = gen_math_ops._sum(input_tensor, axis, keep_dims: keepdims, name: name);
-            return _may_reduce_to_scalar(keepdims, new int[] { axis }, m);
+            return _may_reduce_to_scalar(keepdims, axis, m);
         }
 
         private static Tensor _may_reduce_to_scalar(bool keepdims, Tensor axis, Tensor output)
@@ -464,6 +471,11 @@ namespace Tensorflow
             return output;
         }
 
+        private static Tensor _may_reduce_to_scalar(bool keepdims, int axis, Tensor output)
+        {
+            return output;
+        }
+
         private static Tensor _ReductionDims(Tensor x, Tensor axis)
         {
             if (axis != null)
@@ -477,6 +489,11 @@ namespace Tensorflow
             }
         }
 
+        private static int _ReductionDims(Tensor x, int axis)
+        {
+            return axis;
+        }
+
         private static Tensor _ReductionDims(Tensor x, int[] axis)
         {
             if (axis != null)

src/TensorFlowNET.Core/Tensors/Tensor.Operators.cs

@@ -328,18 +328,16 @@ namespace Tensorflow
                 switch (name.ToLowerInvariant())
                 {
                     case "add":
-                        result = gen_math_ops.add(x1, y1, name: scope);
+                        result = math_ops.add(x1, y1, name: scope);
                         break;
                     case "div":
-                        result = _intTfDataTypes.Contains(x1.dtype) || _intTfDataTypes.Contains(y1.dtype)
-                            ? gen_math_ops.floor_div(x1, y1, name: scope)
-                            : gen_math_ops.real_div(x1, y1, name: scope);
+                        result = math_ops.div(x1, y1, name: scope);
                         break;
                     case "floordiv":
                         result = gen_math_ops.floor_div(x1, y1, name: scope);
                         break;
                     case "truediv":
-                        result = gen_math_ops.real_div(x1, y1, name: scope);
+                        result = math_ops.truediv(x1, y1, name: scope);
                         break;
                     case "mul":
                         result = gen_math_ops.mul(x1, y1, name: scope);