compute_gradients

src/TensorFlowNET.Core/Train/Distribute.cs

@@ -0,0 +1,14 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public static class Distribute
+    {
+        public static VariableAggregationType get_loss_reduction()
+        {
+            return VariableAggregationType.MEAN;
+        }
+    }
+}

src/TensorFlowNET.Core/Train/GateGradientType.cs

@@ -0,0 +1,13 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public enum GateGradientType
+    {
+        GATE_NONE = 0,
+        GATE_OP = 1,
+        GATE_GRAPH = 2
+    }
+}

src/TensorFlowNET.Core/Train/GradientDescentOptimizer.cs

@@ -0,0 +1,16 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public class GradientDescentOptimizer : Optimizer
+    {
+        public GradientDescentOptimizer(double learning_rate, bool use_locking = false, string name = "GradientDescent") 
+            : base(learning_rate, use_locking, name)
+        {
+            LearningRate = learning_rate;
+            LearningRateTensor = null;
+        }
+    }
+}

src/TensorFlowNET.Core/Train/Optimizer.cs

@@ -0,0 +1,55 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using distribute_lib = Tensorflow.Distribute;
+
+namespace Tensorflow
+{
+    /// <summary>
+    /// Base class for optimizers.
+    /// This class defines the API to add Ops to train a model.  You never use this
+    /// class directly, but instead instantiate one of its subclasses such as
+    /// `GradientDescentOptimizer`, `AdagradOptimizer`, or `MomentumOptimizer`.
+    /// </summary>
+    public abstract class Optimizer
+    {
+        public string Name { get; set; }
+        public double LearningRate { get; set; }
+        public Tensor LearningRateTensor { get; set; }
+
+        public Optimizer(double learning_rate, bool use_locking, string name = "")
+        {
+            if (String.IsNullOrEmpty(name))
+                throw new NotImplementedException("Must specify the optimizer name");
+
+            Name = name;
+        }
+
+        /// <summary>
+        /// Add operations to minimize `loss` by updating `var_list`
+        /// </summary>
+        /// <param name="loss"></param>
+        /// <returns></returns>
+        public Optimizer minimize(Tensor loss, GateGradientType gate_gradients = GateGradientType.GATE_OP)
+        {
+            compute_gradients(loss, gate_gradients);
+            return this;
+        }
+
+        /// <summary>
+        /// Compute gradients of `loss` for the variables in `var_list`.
+        /// </summary>
+        /// <param name="loss"></param>
+        /// <param name="gate_gradients"></param>
+        public List<KeyValuePair<object, object>> compute_gradients(Tensor loss, GateGradientType gate_gradients = GateGradientType.GATE_OP)
+        {
+            int num_towers = 1;
+            if(distribute_lib.get_loss_reduction() == VariableAggregationType.MEAN)
+            {
+                
+            }
+
+            return null;
+        }
+    }
+}

src/TensorFlowNET.Core/Train/VariableAggregationType.cs

@@ -0,0 +1,14 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public enum VariableAggregationType
+    {
+        NONE = 0,
+        SUM = 1,
+        MEAN = 2,
+        ONLY_FIRST_TOWER = 3
+    }
+}

src/TensorFlowNET.Core/Train/tf.optimizers.cs

@@ -0,0 +1,17 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public static partial class tf
+    {
+        public static class train
+        {
+            public static Optimizer GradientDescentOptimizer(double learning_rate)
+            {
+                return new GradientDescentOptimizer(learning_rate);
+            }
+        }
+    }
+}

test/TensorFlowNET.Examples/LinearRegression.cs

@@ -47,7 +47,7 @@ namespace TensorFlowNET.Examples
 
             // radient descent
             // Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
-            // var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);
+            var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);
         }
     }
 }