Merge branch 'master' of https://github.com/SciSharp/TensorFlow.NET

# Conflicts:
#	README.md
#	src/TensorFlowNET.Core/Sessions/_FetchMapper.cs

README.md

@@ -1,6 +1,7 @@
 # TensorFlow.NET
 TensorFlow.NET provides a .NET Standard binding for [TensorFlow](https://www.tensorflow.org/). It aims to implement the complete Tensorflow API in CSharp which allows .NET developers to develop, train and deploy Machine Learning models with the cross-platform .NET Standard framework. 
-Here is a simple test
+TensorFlow.NET (TF.NET) provides a .NET Standard binding for [TensorFlow](https://www.tensorflow.org/). It aims to implement the complete Tensorflow API in CSharp which allows .NET developers to develop, train and deploy Machine Learning models with the cross-platform .NET Standard framework. 
+
 [![Join the chat at https://gitter.im/publiclab/publiclab](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/sci-sharp/community)
 [![Tensorflow.NET](https://ci.appveyor.com/api/projects/status/wx4td43v2d3f2xj6?svg=true)](https://ci.appveyor.com/project/Haiping-Chen/tensorflow-net)
 [![codecov](https://codecov.io/gh/SciSharp/NumSharp/branch/master/graph/badge.svg)](https://codecov.io/gh/SciSharp/NumSharp)
@@ -8,7 +9,7 @@ Here is a simple test
 [![Documentation Status](https://readthedocs.org/projects/tensorflownet/badge/?version=latest)](https://tensorflownet.readthedocs.io/en/latest/?badge=latest)
 [![Badge](https://img.shields.io/badge/link-996.icu-red.svg)](https://996.icu/#/en_US)
 
-TensorFlow.NET is a member project of [SciSharp STACK](https://github.com/SciSharp).
+TF.NET is a member project of [SciSharp STACK](https://github.com/SciSharp).
 
 ![tensors_flowing](docs/assets/tensors_flowing.gif)
 
@@ -24,14 +25,14 @@ In comparison to other projects, like for instance TensorFlowSharp which only pr
 
 ### How to use
 
-Install TensorFlow.NET through NuGet.
+Install TF.NET through NuGet.
 ```sh
 PM> Install-Package TensorFlow.NET
 ```
 
 If you are using Linux or Mac OS, please download the pre-compiled dll [here](tensorflowlib) and place it in the working folder. This is only need for Linux and Mac OS, and already packed into NuGet for Windows.
 
-Import tensorflow.net.
+Import TF.NET.
 
 ```cs
 using Tensorflow;

src/TensorFlowNET.Core/Sessions/_FetchMapper.cs

@@ -17,10 +17,10 @@ namespace Tensorflow
             if (fetch.GetType().IsArray)
                 return new _ListFetchMapper(fetches);
             else
-                return new _ElementFetchMapper(fetches, (List<object> fetched_vals) => fetched_vals[0]);
+                return new _ElementFetchMapper(fetches, (List<NDArray> fetched_vals) => fetched_vals[0]);
         }
 
-        public virtual NDArray build_results(List<object> values)
+        public virtual NDArray build_results(List<NDArray> values)
         {
             var type = values[0].GetType();
             var nd = new NDArray(type, values.Count);
@@ -31,16 +31,12 @@ namespace Tensorflow
                     nd.SetData(values.Select(x => (float)x).ToArray());
                     break;
                 case "NDArray":
-                    //    nd.SetData<NDArray>(values.ToArray());
-                    //NDArray[] arr = new NDArray[values.Count];
-                    //for (int i=0; i<values.Count; i++)
                     NDArray[] arr = values.Select(x => (NDArray)x).ToArray();
                     nd = new NDArray(arr);
                     break;
                 default:
                     break;
             }
-
             return nd;
         }
 

test/TensorFlowNET.Examples/KMeansClustering.cs

@@ -33,19 +33,54 @@ namespace TensorFlowNET.Examples
 
         public bool Run()
         {
+            PrepareData();
+
+            var graph = tf.Graph().as_default();
+
             tf.train.import_meta_graph("kmeans.meta");
 
             // Input images
-            var X = tf.placeholder(tf.float32, shape: new TensorShape(-1, num_features));
+            var X = graph.get_operation_by_name("Placeholder").output; // tf.placeholder(tf.float32, shape: new TensorShape(-1, num_features));
             //  Labels (for assigning a label to a centroid and testing)
-            var Y = tf.placeholder(tf.float32, shape: new TensorShape(-1, num_classes));
+            var Y = graph.get_operation_by_name("Placeholder_1").output; // tf.placeholder(tf.float32, shape: new TensorShape(-1, num_classes));
 
             // K-Means Parameters
-            var kmeans = new KMeans(X, k, distance_metric: KMeans.COSINE_DISTANCE, use_mini_batch: true);
+            //var kmeans = new KMeans(X, k, distance_metric: KMeans.COSINE_DISTANCE, use_mini_batch: true);
 
             // Build KMeans graph
-            var training_graph = kmeans.training_graph();
-            
+            //var training_graph = kmeans.training_graph();
+
+            var init_vars = tf.global_variables_initializer();
+            Tensor init_op = graph.get_operation_by_name("cond/Merge");
+            var train_op = graph.get_operation_by_name("group_deps");
+            Tensor avg_distance = graph.get_operation_by_name("Mean");
+            Tensor cluster_idx = graph.get_operation_by_name("Squeeze_1");
+
+            with(tf.Session(graph), sess =>
+            {
+                sess.run(init_vars, new FeedItem(X, full_data_x));
+                sess.run(init_op, new FeedItem(X, full_data_x));
+
+                // Training
+                NDArray result = null;
+                foreach(var i in range(1, num_steps + 1))
+                {
+                    result = sess.run(new ITensorOrOperation[] { train_op, avg_distance, cluster_idx }, new FeedItem(X, full_data_x));
+                    if (i % 2 == 0 || i == 1)
+                        print($"Step {i}, Avg Distance: {result[1]}");
+                }
+
+                var idx = result[2];
+
+                // Assign a label to each centroid
+                // Count total number of labels per centroid, using the label of each training
+                // sample to their closest centroid (given by 'idx')
+                var counts = np.zeros(k, num_classes);
+                foreach (var i in range(idx.len))
+                    counts[idx[i]] += mnist.train.labels[i];
+
+            });
+
             return false;
         }
 

test/TensorFlowNET.Examples/ObjectDetection.cs

@@ -50,13 +50,9 @@ namespace TensorFlowNET.Examples
             with(tf.Session(graph), sess =>
             {
                 var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr));
-
-                //NDArray scores = results.Array.GetValue(2) as NDArray;
-
-                //floatscores.Data<float>();
+                
                 NDArray[] resultArr = results.Data<NDArray>();
-
-                //float[] scores = resultArr[2].Data<float>();
+                
                 buildOutputImage(resultArr);
             });
 

test/TensorFlowNET.UnitTest/OperationsTest.cs

@@ -1,6 +1,7 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using System.Text;
 using Tensorflow;
 using Buffer = Tensorflow.Buffer;
@@ -20,6 +21,13 @@ namespace TensorFlowNET.UnitTest
             var handle = c_api.TF_GetAllOpList();
             var buffer = new Buffer(handle);
             var op_list = OpList.Parser.ParseFrom(buffer);
+
+            var _registered_ops = new Dictionary<string, OpDef>();
+            foreach (var op_def in op_list.Op)
+                _registered_ops[op_def.Name] = op_def;
+
+            // r1.14 added NN op
+            var op = _registered_ops.FirstOrDefault(x => x.Key == "NearestNeighbors");
             Assert.IsTrue(op_list.Op.Count > 1000);
         }