Finished NER training but missing run_evaluate function.

test/TensorFlowNET.Examples/TextProcess/NER/LstmCrfNer.cs

@@ -39,6 +39,9 @@ namespace TensorFlowNET.Examples.Text.NER
         Tensor labels_tensor;
         Tensor dropout_tensor;
         Tensor lr_tensor;
+        Operation train_op;
+        Tensor loss;
+        Tensor merged;
 
         public bool Run()
         {
@@ -47,6 +50,9 @@ namespace TensorFlowNET.Examples.Text.NER
 
             tf.train.import_meta_graph("graph/lstm_crf_ner.meta");
 
+            float loss_value = 0f;
+
+            //add_summary();
             word_ids_tensor = graph.OperationByName("word_ids");
             sequence_lengths_tensor = graph.OperationByName("sequence_lengths");
             char_ids_tensor = graph.OperationByName("char_ids");
@@ -54,6 +60,9 @@ namespace TensorFlowNET.Examples.Text.NER
             labels_tensor = graph.OperationByName("labels");
             dropout_tensor = graph.OperationByName("dropout");
             lr_tensor = graph.OperationByName("lr");
+            train_op = graph.OperationByName("train_step/Adam");
+            loss = graph.OperationByName("Mean");
+            //merged = graph.OperationByName("Merge/MergeSummary");
 
             var init = tf.global_variables_initializer();
 
@@ -63,24 +72,28 @@ namespace TensorFlowNET.Examples.Text.NER
 
                 foreach (var epoch in range(hp.epochs))
                 {
-                    print($"Epoch {epoch + 1} out of {hp.epochs}");
-                    run_epoch(train, dev, epoch);
+                    Console.Write($"Epoch {epoch + 1} out of {hp.epochs}, ");
+                    loss_value = run_epoch(sess, train, dev, epoch);
+                    print($"train loss: {loss_value}");
                 }
-
             });
 
-            return true;
+            return loss_value < 0.1;
         }
 
-        private void run_epoch(CoNLLDataset train, CoNLLDataset dev, int epoch)
+        private float run_epoch(Session sess, CoNLLDataset train, CoNLLDataset dev, int epoch)
         {
-            int i = 0;
+            NDArray results = null;
+
             // iterate over dataset
             var batches = minibatches(train, hp.batch_size);
             foreach (var(words, labels) in batches)
             {
-                get_feed_dict(words, labels, hp.lr, hp.dropout);
+                var (fd, _) = get_feed_dict(words, labels, hp.lr, hp.dropout);
+                results = sess.run(new ITensorOrOperation[] { train_op, loss }, feed_dict: fd);
             }
+
+            return results[1];
         }
 
         private IEnumerable<((int[][], int[])[], int[][])> minibatches(CoNLLDataset data, int minibatch_size)
@@ -115,7 +128,7 @@ namespace TensorFlowNET.Examples.Text.NER
         /// <param name="labels">list of ids</param>
         /// <param name="lr">learning rate</param>
         /// <param name="dropout">keep prob</param>
-        private FeedItem[] get_feed_dict((int[][], int[])[] words, int[][] labels, float lr = 0f, float dropout = 0f)
+        private (FeedItem[], int[]) get_feed_dict((int[][], int[])[] words, int[][] labels, float lr = 0f, float dropout = 0f)
         {
             int[] sequence_lengths;
             int[][] word_lengths;
@@ -140,14 +153,21 @@ namespace TensorFlowNET.Examples.Text.NER
                 feeds.Add(new FeedItem(word_lengths_tensor, np.array(word_lengths)));
             }
 
-            throw new NotImplementedException("get_feed_dict");
+            (labels, _) = pad_sequences(labels, 0);
+            feeds.Add(new FeedItem(labels_tensor, np.array(labels)));
+
+            feeds.Add(new FeedItem(lr_tensor, lr));
+
+            feeds.Add(new FeedItem(dropout_tensor, dropout));
+
+            return (feeds.ToArray(), sequence_lengths);
         }
 
         public void PrepareData()
         {
             hp = new HyperParams("LstmCrfNer")
             {
-                epochs = 15,
+                epochs = 50,
                 dropout = 0.5f,
                 batch_size = 20,
                 lr_method = "adam",