tf.stack

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

@@ -140,6 +140,16 @@ namespace Tensorflow
         public Tensor shape(Tensor input, string name = null, TF_DataType out_type = TF_DataType.TF_INT32)
             => array_ops.shape_internal(input, name, optimize: true, out_type: out_type);
 
+        /// <summary>
+        /// Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.
+        /// </summary>
+        /// <param name="values"></param>
+        /// <param name="axis"></param>
+        /// <param name="name"></param>
+        /// <returns>A stacked `Tensor` with the same type as `values`.</returns>
+        public Tensor stack(Tensor[] values, int axis = 0, string name = "stack")
+            => array_ops.stack(values, axis: axis, name: name);
+
         /// <summary>
         /// Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
         /// </summary>

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

@@ -308,6 +308,18 @@ namespace Tensorflow
         public static (Tensor, Tensor) unique(Tensor x, TF_DataType out_idx = TF_DataType.TF_INT32, string name = null)
             => gen_array_ops.unique(x, out_idx: out_idx, name: name);
 
+        public static Tensor stack(Tensor[] values, int axis = 0, string name = "stack")
+        {
+            if (axis == 0)
+            {
+                return ops.convert_to_tensor(values, name: name);
+            }
+
+            var value_shape = ops.convert_to_tensor(values[0], name: name).TensorShape;
+
+            return gen_array_ops.pack(values, axis: axis, name: name);
+        }
+
         public static Tensor[] unstack(Tensor value, int? num = null, int axis = 0, string name = "unstack")
         {
             if(num == null)