tf.GraphKeys #359

README.md

@@ -1,6 +1,6 @@
 ![logo](docs/assets/tf.net.logo.png)
 
-**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. 
+**TensorFlow.NET** (TF.NET) provides a .NET Standard binding for [TensorFlow](https://www.tensorflow.org/). It aims to implement the complete Tensorflow API in C# 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)
@@ -34,40 +34,15 @@ PM> Install-Package TensorFlow.NET
 ### Install tensorflow binary
 ### For CPU version
 PM> Install-Package SciSharp.TensorFlow.Redist
+
 ### For GPU version (CUDA and cuDNN are required)
 PM> Install-Package SciSharp.TensorFlow.Redist-Windows-GPU
 ```
 
-Import TF.NET.
-
-```cs
-using Tensorflow;
-```
-
-Add two constants:
-```cs
-// Create a Constant op
-var a = tf.constant(4.0f);
-var b = tf.constant(5.0f);
-var c = tf.add(a, b);
-
-using (var sess = tf.Session())
-{
-    var o = sess.run(c);
-}
-```
+Import TF.NET in your project.
 
-Feed placeholder:
 ```cs
-// Create a placeholder op
-var a = tf.placeholder(tf.float32);
-var b = tf.placeholder(tf.float32);
-var c = tf.add(a, b);
-
-using(var sess = tf.Session())
-{
-    var o = sess.run(c, new FeedItem(a, 3.0f), new FeedItem(b, 2.0f));
-}
+using static Tensorflow.Binding;
 ```
 
 Linear Regression:
@@ -91,39 +66,40 @@ var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);
 var init = tf.global_variables_initializer();
 
 // Start training
-with(tf.Session(), sess => 
+using(tf.Session()) 
 {
     // Run the initializer
     sess.run(init);
-    
+
     // Fit all training data
     for (int epoch = 0; epoch < training_epochs; epoch++)
     {
         foreach (var (x, y) in zip<float>(train_X, train_Y))
-            sess.run(optimizer, new FeedItem(X, x), new FeedItem(Y, y));
-        
+            sess.run(optimizer, (X, x), (Y, y));
+
         // Display logs per epoch step
         if ((epoch + 1) % display_step == 0)
         {
-            var c = sess.run(cost, new FeedItem(X, train_X), new FeedItem(Y, train_Y));
+            var c = sess.run(cost, (X, train_X), (Y, train_Y));
             Console.WriteLine($"Epoch: {epoch + 1} cost={c} " + $"W={sess.run(W)} b={sess.run(b)}");
         }
-        
-        Console.WriteLine("Optimization Finished!");
-        var training_cost = sess.run(cost, new FeedItem(X, train_X), new FeedItem(Y, train_Y));
-        Console.WriteLine($"Training cost={training_cost} W={sess.run(W)} b={sess.run(b)}");
-        
-        // Testing example
-        var test_X = np.array(6.83f, 4.668f, 8.9f, 7.91f, 5.7f, 8.7f, 3.1f, 2.1f);
-        var test_Y = np.array(1.84f, 2.273f, 3.2f, 2.831f, 2.92f, 3.24f, 1.35f, 1.03f);
-        Console.WriteLine("Testing... (Mean square loss Comparison)");
-        
-        var testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * test_X.shape[0]), new FeedItem(X, test_X), new FeedItem(Y, test_Y));
-        Console.WriteLine($"Testing cost={testing_cost}");
-        
-        var diff = Math.Abs((float)training_cost - (float)testing_cost);
-        Console.WriteLine($"Absolute mean square loss difference: {diff}");
     }
+
+    Console.WriteLine("Optimization Finished!");
+    var training_cost = sess.run(cost, (X, train_X), (Y, train_Y));
+    Console.WriteLine($"Training cost={training_cost} W={sess.run(W)} b={sess.run(b)}");
+
+    // Testing example
+    var test_X = np.array(6.83f, 4.668f, 8.9f, 7.91f, 5.7f, 8.7f, 3.1f, 2.1f);
+    var test_Y = np.array(1.84f, 2.273f, 3.2f, 2.831f, 2.92f, 3.24f, 1.35f, 1.03f);
+    Console.WriteLine("Testing... (Mean square loss Comparison)");
+    var testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * test_X.shape[0]),
+                                (X, test_X), (Y, test_Y));
+    Console.WriteLine($"Testing cost={testing_cost}");
+    var diff = Math.Abs((float)training_cost - (float)testing_cost);
+    Console.WriteLine($"Absolute mean square loss difference: {diff}");
+
+    return diff < 0.01;
 });
 ```
 

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

@@ -14,11 +14,16 @@
    limitations under the License.
 ******************************************************************************/
 
+using static Tensorflow.ops;
+
 namespace Tensorflow
 {
     public partial class tensorflow
     {
         public graph_util_impl graph_util => new graph_util_impl();
+
+        public GraphKeys GraphKeys { get; } = new GraphKeys();
+
         public Graph get_default_graph()
         {
             return ops.get_default_graph();

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

@@ -15,6 +15,7 @@
 ******************************************************************************/
 
 using System.Collections.Generic;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {
@@ -22,7 +23,7 @@ namespace Tensorflow
     {
         public VariableV1[] global_variables(string scope = null)
         {
-            return (ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, scope) as List<VariableV1>)
+            return (ops.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope) as List<VariableV1>)
                 .ToArray();
         }
 

src/TensorFlowNET.Core/Framework/meta_graph.py.cs

@@ -95,7 +95,7 @@ namespace Tensorflow
                         break;
                     case KindOneofCase.BytesList:
                         //var proto_type = ops.get_collection_proto_type(key)
-                        if (ops.GraphKeys._VARIABLE_COLLECTIONS.Contains(col.Key))
+                        if (tf.GraphKeys._VARIABLE_COLLECTIONS.Contains(col.Key))
                         {
                             foreach (var value in col.Value.BytesList.Value)
                             {
@@ -146,7 +146,7 @@ namespace Tensorflow
                 }
             }
 
-            var variables = graph.get_collection<VariableV1>(ops.GraphKeys.GLOBAL_VARIABLES,
+            var variables = graph.get_collection<VariableV1>(tf.GraphKeys.GLOBAL_VARIABLES,
                                      scope: scope_to_prepend_to_names);
             var var_list = new Dictionary<string, VariableV1>();
             variables.ForEach(v => var_list[ops.strip_name_scope(v.name, scope_to_prepend_to_names)] = v);
@@ -180,7 +180,7 @@ namespace Tensorflow
             var graph = ops.get_default_graph();
 
             var var_list = new Dictionary<string, RefVariable>();
-            var variables = graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) as List<RefVariable>;
+            var variables = graph.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) as List<RefVariable>;
 
             if (variables != null)
             {

src/TensorFlowNET.Core/Layers/Layer.cs

@@ -81,7 +81,7 @@ namespace Tensorflow.Layers
 
 
             // Update global default collections.
-            _add_elements_to_collection(_updates.ToArray(), new string[] { ops.GraphKeys.UPDATE_OPS });
+            _add_elements_to_collection(_updates.ToArray(), new string[] { tf.GraphKeys.UPDATE_OPS });
 
             return outputs;
         }

src/TensorFlowNET.Core/Operations/ControlFlows/CondContext.cs

@@ -152,9 +152,9 @@ namespace Tensorflow.Operations
         public (T, Tensor) BuildCondBranch<T>(Func<T> fn)
         {
             // Add the subgraph defined by fn() to the graph.
-            var pre_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION);
+            var pre_summaries = ops.get_collection(tf.GraphKeys._SUMMARY_COLLECTION);
             var original_result = fn();
-            var post_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION);
+            var post_summaries = ops.get_collection(tf.GraphKeys._SUMMARY_COLLECTION);
 
             //TODO: port this chunck of missing code:
             /*
@@ -191,9 +191,9 @@ namespace Tensorflow.Operations
         public (T[], Tensor[]) BuildCondBranch<T>(Func<T[]> fn)
         {
             // Add the subgraph defined by fn() to the graph.
-            var pre_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION);
+            var pre_summaries = ops.get_collection(tf.GraphKeys._SUMMARY_COLLECTION);
             var original_result = fn();
-            var post_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION);
+            var post_summaries = ops.get_collection(tf.GraphKeys._SUMMARY_COLLECTION);
 
             switch (original_result)
             {

src/TensorFlowNET.Core/Operations/ControlFlows/WhileContext.cs

@@ -195,7 +195,7 @@ namespace Tensorflow.Operations
             // their associated TensorArrays for calling the body.
             var packed_vars_for_body = _convert_flows_to_tensorarrays(flat_loop_vars, vars_for_body);
             var body_result = body(packed_vars_for_body[0]);
-            var post_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION);
+            var post_summaries = ops.get_collection(tf.GraphKeys._SUMMARY_COLLECTION);
 
             // Store body_result to keep track of TensorArrays returned by body
             var original_body_result = new[] { body_result };

src/TensorFlowNET.Core/Operations/Losses/Util.cs

@@ -2,7 +2,7 @@
 {
     public class Util
     {
-        public static void add_loss(Tensor loss, string loss_collection = ops.GraphKeys.LOSSES)
+        public static void add_loss(Tensor loss, string loss_collection = "losses")
         {
             if (!string.IsNullOrEmpty(loss_collection))
                 ops.add_to_collection(loss_collection, loss);

src/TensorFlowNET.Core/Operations/Losses/losses_impl.py.cs

@@ -22,7 +22,7 @@ namespace Tensorflow
     public class LossesImpl
     {
         public Tensor compute_weighted_loss(Tensor losses, Tensor weights = null, string scope = null,
-            string loss_collection = ops.GraphKeys.LOSSES, string reduction = Reduction.SUM_BY_NONZERO_WEIGHTS)
+            string loss_collection = "losses", string reduction = Reduction.SUM_BY_NONZERO_WEIGHTS)
         {
             return tf_with(ops.name_scope(scope, default_name: "weighted_loss", (losses, weights)), delegate
             {
@@ -101,7 +101,7 @@ namespace Tensorflow
             Tensor logits,
             float weights = 1.0f,
             string scope = null,
-            string loss_collection= ops.GraphKeys.LOSSES,
+            string loss_collection= "losses",
             string reduction = Reduction.SUM_BY_NONZERO_WEIGHTS)
         {
             return tf_with(ops.name_scope(scope,

src/TensorFlowNET.Core/Operations/control_flow_ops.py.cs

@@ -431,8 +431,8 @@ namespace Tensorflow
 
                 merges = _convert_flows_to_tensorarrays(new Tensor[] { (Tensor)orig_res_t }, merges);
 
-                ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_t);
-                ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_f);
+                ops.add_to_collection(tf.GraphKeys.COND_CONTEXT, context_t);
+                ops.add_to_collection(tf.GraphKeys.COND_CONTEXT, context_f);
 
                 return merges[0];
             });
@@ -479,8 +479,8 @@ namespace Tensorflow
 
                 merges = _convert_flows_to_tensorarrays(orig_res_t, merges);
 
-                ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_t);
-                ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_f);
+                ops.add_to_collection(tf.GraphKeys.COND_CONTEXT, context_t);
+                ops.add_to_collection(tf.GraphKeys.COND_CONTEXT, context_f);
 
                 return merges;
             });
@@ -596,7 +596,7 @@ namespace Tensorflow
                     swap_memory: swap_memory);
 
                 if (loop_context.outer_context == null)
-                    ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, loop_context);
+                    ops.add_to_collection(tf.GraphKeys.WHILE_CONTEXT, loop_context);
 
                 var results = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants,
                                     return_same_structure);

src/TensorFlowNET.Core/Summaries/Summary.cs

@@ -33,11 +33,11 @@ namespace Tensorflow.Summaries
         {
             var (tag, scope) = summary_scope(name, family: family, values: new Tensor[] { tensor }, default_name: "HistogramSummary");
             var val = gen_logging_ops.histogram_summary(tag: tag, values: tensor, name: scope);
-            collect(val, collections?.ToList(), new List<string> { ops.GraphKeys.SUMMARIES });
+            collect(val, collections?.ToList(), new List<string> { tf.GraphKeys.SUMMARIES });
             return val;
         }
 
-        public Tensor merge_all(string key = ops.GraphKeys.SUMMARIES, string scope= null, string name= null)
+        public Tensor merge_all(string key = "summaries", string scope= null, string name= null)
         {
             var summary_ops = ops.get_collection(key, scope: scope);
             if (summary_ops == null)
@@ -67,7 +67,7 @@ namespace Tensorflow.Summaries
         {
             var (tag, scope) = summary_scope(name, family: family, values: new Tensor[] { tensor });
             var val = gen_logging_ops.scalar_summary(tags: tag, values: tensor, name: scope);
-            collect(val, collections?.ToList(), new List<string> { ops.GraphKeys.SUMMARIES });
+            collect(val, collections?.ToList(), new List<string> { tf.GraphKeys.SUMMARIES });
             return val;
         }
 

src/TensorFlowNET.Core/Train/Optimizer.cs

@@ -198,7 +198,7 @@ namespace Tensorflow
 
                 if (!tf.context.executing_eagerly())
                 {
-                    var train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP) as List<ITensorOrOperation>;
+                    var train_op = ops.get_collection_ref(tf.GraphKeys.TRAIN_OP) as List<ITensorOrOperation>;
                     if (train_op != null && train_op.Contains(apply_updates))
                         train_op.Add(apply_updates);
                 }
@@ -359,7 +359,7 @@ namespace Tensorflow
 
 
             var tmp = variables.trainable_variables();
-            var vars = ops.get_collection<RefVariable>(ops.GraphKeys.TRAINABLE_RESOURCE_VARIABLES);
+            var vars = ops.get_collection<RefVariable>(tf.GraphKeys.TRAINABLE_RESOURCE_VARIABLES);
             switch (tmp)
             {
                 case List<RefVariable> values:
@@ -370,7 +370,7 @@ namespace Tensorflow
                     break;
             }
 
-            var_list = var_list.Concat(ops.get_collection<RefVariable>(ops.GraphKeys._STREAMING_MODEL_PORTS)).ToList();
+            var_list = var_list.Concat(ops.get_collection<RefVariable>(tf.GraphKeys._STREAMING_MODEL_PORTS)).ToList();
             var processors = var_list.Select(v => optimizer._get_processor(v)).ToList();
             var var_refs = processors.Select(x => x.target()).ToArray();
 

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -121,7 +121,7 @@ namespace Tensorflow
 
             if(collections == null)
             {
-                collections = new List<string> { ops.GraphKeys.GLOBAL_VARIABLES };
+                collections = new List<string> { tf.GraphKeys.GLOBAL_VARIABLES };
             }
 
             // Store the graph key so optimizers know how to only retrieve variables from
@@ -129,8 +129,8 @@ namespace Tensorflow
             _graph_key = ops.get_default_graph().graph_key;
 
             _trainable = trainable;
-            if (trainable && !collections.Contains(ops.GraphKeys.TRAINABLE_VARIABLES))
-                collections.Add(ops.GraphKeys.TRAINABLE_VARIABLES);
+            if (trainable && !collections.Contains(tf.GraphKeys.TRAINABLE_VARIABLES))
+                collections.Add(tf.GraphKeys.TRAINABLE_VARIABLES);
 
             ops.init_scope();
             var values = init_from_fn ? new object[0] : new object[] { initial_value };

src/TensorFlowNET.Core/Variables/variables.py.cs

@@ -17,6 +17,7 @@
 using System;
 using System.Collections.Generic;
 using System.Linq;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {
@@ -28,7 +29,7 @@ namespace Tensorflow
         /// <returns></returns>
         public static object trainable_variables()
         {
-            return ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES);
+            return ops.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES);
         }
 
         /// <summary>
@@ -40,11 +41,11 @@ namespace Tensorflow
         {
             var all = new List<VariableV1>();
 
-            var collection = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, scope);
+            var collection = ops.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope);
             if(collection != null)
                 all.AddRange(collection as List<VariableV1>);
 
-            collection = ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS, scope);
+            collection = ops.get_collection(tf.GraphKeys.SAVEABLE_OBJECTS, scope);
             if (collection != null)
                 all.AddRange(collection as List<VariableV1>);
 
@@ -64,7 +65,7 @@ namespace Tensorflow
         /// <returns>A list of `Variable` objects.</returns>
         public static List<VariableV1> global_variables(string scope = null)
         {
-            var result = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, scope);
+            var result = ops.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope);
 
             return result == null ? new List<VariableV1>() : result as List<VariableV1>;
         }

src/TensorFlowNET.Core/ops.GraphKeys.cs

@@ -27,57 +27,57 @@ namespace Tensorflow
         /// specified, but it is also possible to pass an explicit list of
         /// variables.
         /// </summary>
-        public static class GraphKeys
+        public class GraphKeys
         {
             /// <summary>
             /// the subset of `Variable` objects that will be trained by an optimizer.
             /// </summary>
-            public static string TRAINABLE_VARIABLES = "trainable_variables";
+            public string TRAINABLE_VARIABLES = "trainable_variables";
 
             /// <summary>
             /// Trainable resource-style variables.
             /// </summary>
-            public static string TRAINABLE_RESOURCE_VARIABLES = "trainable_resource_variables";
+            public string TRAINABLE_RESOURCE_VARIABLES = "trainable_resource_variables";
 
             /// <summary>
             /// Key for streaming model ports.
             /// </summary>
-            public static string _STREAMING_MODEL_PORTS = "streaming_model_ports";
+            public string _STREAMING_MODEL_PORTS = "streaming_model_ports";
 
             /// <summary>
             /// Key to collect losses
             /// </summary>
-            public const string LOSSES = "losses";
+            public string LOSSES = "losses";
 
             /// <summary>
             /// Key to collect Variable objects that are global (shared across machines).
             /// Default collection for all variables, except local ones.
             /// </summary>
-            public static string GLOBAL_VARIABLES = "variables";
+            public string GLOBAL_VARIABLES = "variables";
 
-            public static string TRAIN_OP = "train_op";
+            public string TRAIN_OP = "train_op";
 
-            public static string GLOBAL_STEP = GLOBAL_STEP = "global_step";
+            public string GLOBAL_STEP = "global_step";
 
-            public static string[] _VARIABLE_COLLECTIONS = new string[] { "variables", "trainable_variables", "model_variables" }; 
+            public string[] _VARIABLE_COLLECTIONS = new string[] { "variables", "trainable_variables", "model_variables" }; 
             /// <summary>
             /// Key to collect BaseSaverBuilder.SaveableObject instances for checkpointing.
             /// </summary>
-            public static string SAVEABLE_OBJECTS = "saveable_objects";
+            public string SAVEABLE_OBJECTS = "saveable_objects";
             /// <summary>
             /// Key to collect update_ops
             /// </summary>
-            public static string UPDATE_OPS = "update_ops";
+            public string UPDATE_OPS = "update_ops";
 
             // Key to collect summaries.
-            public const string SUMMARIES = "summaries";
+            public string SUMMARIES = "summaries";
 
             // Used to store v2 summary names.
-            public static string _SUMMARY_COLLECTION = "_SUMMARY_V2";
+            public string _SUMMARY_COLLECTION = "_SUMMARY_V2";
 
             // Key for control flow context.
-            public static string COND_CONTEXT = "cond_context";
-            public static string WHILE_CONTEXT = "while_context";
+            public string COND_CONTEXT = "cond_context";
+            public string WHILE_CONTEXT = "while_context";
         }
     }
 }

test/TensorFlowNET.Examples/BasicModels/LinearRegression.cs

@@ -80,26 +80,18 @@ namespace TensorFlowNET.Examples
                 for (int epoch = 0; epoch < training_epochs; epoch++)
                 {
                     foreach (var (x, y) in zip<float>(train_X, train_Y))
-                    {
-                        sess.run(optimizer, 
-                            new FeedItem(X, x),
-                            new FeedItem(Y, y));
-                    }
+                        sess.run(optimizer, (X, x), (Y, y));
 
                     // Display logs per epoch step
                     if ((epoch + 1) % display_step == 0)
                     {
-                        var c = sess.run(cost, 
-                            new FeedItem(X, train_X),
-                            new FeedItem(Y, train_Y));
+                        var c = sess.run(cost, (X, train_X), (Y, train_Y));
                         Console.WriteLine($"Epoch: {epoch + 1} cost={c} " + $"W={sess.run(W)} b={sess.run(b)}");
                     }
                 }
 
                 Console.WriteLine("Optimization Finished!");
-                var training_cost = sess.run(cost,
-                    new FeedItem(X, train_X),
-                    new FeedItem(Y, train_Y));
+                var training_cost = sess.run(cost, (X, train_X), (Y, train_Y));
                 Console.WriteLine($"Training cost={training_cost} W={sess.run(W)} b={sess.run(b)}");
 
                 // Testing example
@@ -107,8 +99,7 @@ namespace TensorFlowNET.Examples
                 var test_Y = np.array(1.84f, 2.273f, 3.2f, 2.831f, 2.92f, 3.24f, 1.35f, 1.03f);
                 Console.WriteLine("Testing... (Mean square loss Comparison)");
                 var testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * test_X.shape[0]),
-                    new FeedItem(X, test_X), 
-                    new FeedItem(Y, test_Y));
+                    (X, test_X), (Y, test_Y));
                 Console.WriteLine($"Testing cost={testing_cost}");
                 var diff = Math.Abs((float)training_cost - (float)testing_cost);
                 Console.WriteLine($"Absolute mean square loss difference: {diff}");

test/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs

@@ -118,7 +118,7 @@ namespace TensorFlowNET.Examples.Text
                 var y_one_hot = tf.one_hot(y, num_class);
                 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits: logits, labels: y_one_hot));
 
-                var update_ops = tf.get_collection(ops.GraphKeys.UPDATE_OPS) as List<object>;
+                var update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) as List<object>;
                 tf_with(tf.control_dependencies(update_ops.Select(x => (Operation)x).ToArray()), delegate
                 {
                     var adam = tf.train.AdamOptimizer(learning_rate);