nn_ops.in_top_kv2

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

@@ -134,7 +134,7 @@ namespace Tensorflow
                 => nn_ops.max_pool(value, ksize, strides, padding, data_format: data_format, name: name);
 
             public Tensor in_top_k(Tensor predictions, Tensor targets, int k, string name = "InTopK")
-                => gen_ops.in_top_k(predictions, targets, k, name);
+                => nn_ops.in_top_k(predictions, targets, k, name);
 
             public Tensor[] top_k(Tensor input, int k = 1, bool sorted = true, string name = null)
                 => gen_nn_ops.top_kv2(input, k: k, sorted: sorted, name: name);

src/TensorFlowNET.Core/Operations/NnOps/gen_nn_ops.cs

@@ -244,7 +244,27 @@ namespace Tensorflow.Operations
                 logits
             });
 
-            return _op.outputs[0];
+            return _op.output;
+        }
+
+        /// <summary>
+        /// Says whether the targets are in the top `K` predictions.
+        /// </summary>
+        /// <param name="predictions"></param>
+        /// <param name="targets"></param>
+        /// <param name="k"></param>
+        /// <param name="name"></param>
+        /// <returns>A `Tensor` of type `bool`.</returns>
+        public static Tensor in_top_kv2(Tensor predictions, Tensor targets, int k, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("InTopKV2", name: name, args: new
+            {
+                predictions,
+                targets,
+                k
+            });
+
+            return _op.output;
         }
 
         public static Tensor leaky_relu(Tensor features, float alpha = 0.2f, string name = null)

src/TensorFlowNET.Core/Operations/nn_ops.cs

@@ -111,6 +111,14 @@ namespace Tensorflow
                 return noise_shape;
         }
 
+        public static Tensor in_top_k(Tensor predictions, Tensor targets, int k, string name = null)
+        {
+            return tf_with(ops.name_scope(name, "in_top_k"), delegate
+            {
+                return gen_nn_ops.in_top_kv2(predictions, targets, k, name: name);
+            });
+        }
+
         public static Tensor log_softmax(Tensor logits, int axis = -1, string name = null)
         {
             return _softmax(logits, gen_nn_ops.log_softmax, axis, name);