diff --git a/‎src/TensorFlowNET.Core/APIs/tf.array.cs‎
Lines changed: 16 additions & 0 deletions b/‎src/TensorFlowNET.Core/APIs/tf.array.cs‎
Lines changed: 16 additions & 0 deletions
diff --git a/‎test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs‎
Lines changed: 8 additions & 1 deletion b/‎test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs‎
Lines changed: 8 additions & 1 deletion
@@ -1,11 +1,27 @@
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using System.Text;
 
 namespace Tensorflow
 {
     public static partial class tf
     {
+        /// <summary>
+        /// Concatenates tensors along one dimension.
+        /// </summary>
+        /// <param name="values">A list of `Tensor` objects or a single `Tensor`.</param>
+        /// <param name="axis"></param>
+        /// <param name="name"></param>
+        /// <returns>A `Tensor` resulting from concatenation of the input tensors.</returns>
+        public static Tensor concat(IList<Tensor> values, int axis, string name = "concat")
+        {
+            if (values.Count == 1)
+                throw new NotImplementedException("tf.concat length is 1");
+
+            return gen_array_ops.concat_v2(values.ToArray(), axis, name: name);
+        }
+
         /// <summary>
         /// Inserts a dimension of 1 into a tensor's shape.
         /// </summary>
 
@@ -195,7 +195,14 @@ public Graph BuildGraph()
                 pooled_outputs.Add(pool);
             }
 
-            // var h_pool = tf.concat(pooled_outputs, 3);
+            var h_pool = tf.concat(pooled_outputs, 3);
+            var h_pool_flat = tf.reshape(h_pool, new TensorShape(-1, num_filters * filter_sizes.Rank));
+
+            with(tf.name_scope("dropout"), delegate
+            {
+                // var h_drop = tf.nn.dropout(h_pool_flat, self.keep_prob);
+            });
+
             return graph;
         }
Original file line number	Diff line number	Diff line change
@@ -195,7 +195,14 @@ public Graph BuildGraph()
195	195	pooled_outputs.Add(pool);
196	196	}
197	197
198		- // var h_pool = tf.concat(pooled_outputs, 3);
	198	+ var h_pool = tf.concat(pooled_outputs, 3);
	199	+ var h_pool_flat = tf.reshape(h_pool, new TensorShape(-1, num_filters * filter_sizes.Rank));
	200	+
	201	+ with(tf.name_scope("dropout"), delegate
	202	+ {
	203	+ // var h_drop = tf.nn.dropout(h_pool_flat, self.keep_prob);
	204	+ });
	205	+
199	206	return graph;
200	207	}
201	208