print Accuracy of LogisticRegression

Author: Oceania2018

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

@@ -21,6 +21,9 @@ public static Tensor subtract<T>(Tensor x, T[] y, string name = null) where T :
         public static Tensor log(Tensor x, string name = null)
             => gen_math_ops.log(x, name);
 
+        public static Tensor equal(Tensor x, Tensor y, string name = null)
+            => gen_math_ops.equal(x, y, name);
+
         public static Tensor multiply(Tensor x, Tensor y) 
             => gen_math_ops.mul(x, y);
 

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -128,6 +128,20 @@ public static Tensor sub<Tx, Ty>(Tx x, Ty y, string name = null)
             return _op.outputs[0];
         }
 
+        /// <summary>
+        /// Returns the truth value of (x == y) element-wise.
+        /// </summary>
+        /// <param name="x"></param>
+        /// <param name="y"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor equal(Tensor x, Tensor y, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("Equal", name, args: new { x, y });
+
+            return _op.outputs[0];
+        }
+
         public static Tensor mul(Tensor x, Tensor y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Mul", name, args: new { x, y });

src/TensorFlowNET.Core/Sessions/_ElementFetchMapper.cs

@@ -43,6 +43,9 @@ public override NDArray build_results(List<object> values)
                     case NDArray value:
                         result = value;
                         break;
+                    case float fVal:
+                        result = fVal;
+                        break;
                     default:
                         break;
                 }

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -168,7 +168,12 @@ public Tensor MaybeMove()
         /// <param name="feed_dict">A dictionary that maps `Tensor` objects to feed values.</param>
         /// <param name="session">The `Session` to be used to evaluate this tensor.</param>
         /// <returns></returns>
-        public NDArray eval(FeedItem[] feed_dict = null, Session session = null)
+        public NDArray eval(params FeedItem[] feed_dict)
+        {
+            return ops._eval_using_default_session(this, feed_dict, graph);
+        }
+
+        public NDArray eval(Session session, FeedItem[] feed_dict = null)
         {
             return ops._eval_using_default_session(this, feed_dict, graph, session);
         }

test/TensorFlowNET.Examples/LogisticRegression.cs

@@ -17,17 +17,14 @@ namespace TensorFlowNET.Examples
     public class LogisticRegression : Python, IExample
     {
         private float learning_rate = 0.01f;
-        private int training_epochs = 25;
+        private int training_epochs = 5;
         private int batch_size = 100;
         private int display_step = 1;
 
         public void Run()
         {
-            PrepareData();
-        }
+            var mnist = PrepareData();
 
-        private void PrepareData()
-        {
             // tf Graph Input
             var x = tf.placeholder(tf.float32, new TensorShape(-1, 784)); // mnist data image of shape 28*28=784
             var y = tf.placeholder(tf.float32, new TensorShape(-1, 10)); // 0-9 digits recognition => 10 classes
@@ -50,12 +47,12 @@ private void PrepareData()
 
             with(tf.Session(), sess =>
             {
-                var mnist = MnistDataSet.read_data_sets("logistic_regression", one_hot: true);
+                
                 // Run the initializer
                 sess.run(init);
 
                 // Training cycle
-                foreach(var epoch in range(training_epochs))
+                foreach (var epoch in range(training_epochs))
                 {
                     var avg_cost = 0.0f;
                     var total_batch = mnist.train.num_examples / batch_size;
@@ -81,7 +78,18 @@ private void PrepareData()
                 print("Optimization Finished!");
 
                 // Test model
+                var correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1));
+                // Calculate accuracy
+                var accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32));
+                float acc = accuracy.eval(new FeedItem(x, mnist.test.images), new FeedItem(y, mnist.test.labels));
+                print($"Accuracy: {acc.ToString("F4")}");
             });
         }
+
+        private Datasets PrepareData()
+        {
+            var mnist = MnistDataSet.read_data_sets("logistic_regression", one_hot: true);
+            return mnist;
+        }
     }
 }