add tf.math.sigmoid

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

@@ -281,6 +281,9 @@ namespace Tensorflow
             return math_ops.reduce_sum(input, axis);
         }
 
+        public static Tensor sigmoid<T>(T x, string name = null)
+            => math_ops.sigmoid(x, name: name);
+
         public static Tensor sum(Tensor input, int axis, bool keep_dims = false, string name = null)
             => gen_math_ops._sum(input, axis, keep_dims: keep_dims, name: name);
 

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -108,6 +108,26 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
+        /// <summary>
+        ///    Computes sigmoid of <c>x</c> element-wise.
+        /// </summary>
+        /// <param name="x">
+        /// </param>
+        /// <param name="name">
+        /// If specified, the created operation in the graph will be this one, otherwise it will be named 'Sigmoid'.
+        /// </param>
+        /// <returns>
+        ///    The Operation can be fetched from the resulting Tensor, by fetching the Operation property from the result.
+        /// </returns>
+        /// <remarks>
+        ///    Specifically, <c>y = 1 / (1 + exp(-x))</c>.
+        /// </remarks>
+        public static Tensor sigmoid(Tensor x, string name = "Sigmoid")
+        {
+            var op = _op_def_lib._apply_op_helper("Sigmoid", name: name, new { x });
+
+            return op.output;
+        }
         public static Tensor sinh(Tensor x, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Sinh", name, args: new { x });

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -119,7 +119,13 @@ namespace Tensorflow
                 return _may_reduce_to_scalar(keepdims, axis, m);
             }
         }
-        
+
+        public static Tensor sigmoid<T>(T x, string name = null)
+        {
+            var x_tensor = ops.convert_to_tensor(x, name: "x");
+            return gen_math_ops.sigmoid(x_tensor, name: name);
+        }
+
         /// <summary>
         /// Returns (x - y)(x - y) element-wise.
         /// </summary>

test/TensorFlowNET.UnitTest/PythonTest.cs

@@ -143,7 +143,7 @@ namespace TensorFlowNET.UnitTest
             //    return self._eval_helper(tensors)
             //  else:
             {
-                with(ops.get_default_session(), s =>
+                with(tf.Session(), s =>
                 {
                     var ndarray=tensor.eval();
                     if (typeof(T) == typeof(double))