condcontext

src/TensorFlowNET.Core/Operations/ControlFlows/CondContext.cs

@@ -75,6 +75,8 @@ namespace Tensorflow.Operations
             {
                 case Operation[] results:
                     return (original_result, _BuildCondTensor(results));
+                case Tensor tensor:
+                    return (original_result, tensor);
                 case float[] fv:
                     var result = ops.convert_to_tensor(fv[0]);
                     return (original_result, result );

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

@@ -217,20 +217,24 @@ namespace Tensorflow
                 var (orig_res_f, res_f) = context_f.BuildCondBranch(false_fn);
                 context_f.Exit();
 
-                var res_t_flat = res_t;
-                var res_f_flat = res_f;
+                var res_t_flat = new Tensor[] { res_t };
+                var res_f_flat = new Tensor[] { res_f };
+
+                foreach(var (val_x, val_y) in zip(res_t_flat, res_f_flat))
+                {
 
-                return new Tensor(IntPtr.Zero);
-                /*var merges = zip(res_f_flat, res_t_flat)
+                }
+                
+                var merges = zip(res_f_flat, res_t_flat)
                     .Select(pair => merge(new Tensor[] { pair.Item1, pair.Item2 }))
                     .ToArray();
 
-                merges = _convert_flows_to_tensorarrays(orig_res_t, merges);
+                merges = _convert_flows_to_tensorarrays(new Tensor[] { (Tensor)orig_res_t }, merges);
 
                 ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_t);
                 ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_f);
 
-                return merges;*/
+                return merges[0];
             });
         }
 

src/TensorFlowNET.Core/Operations/embedding_ops.cs

@@ -51,10 +51,15 @@ namespace Tensorflow
                 ids = ops.convert_to_tensor(ids, name: "ids");
                 if (np == 1)
                 {
-
+                    ops.colocate_with(@params[0]);
+                    var result = _clip(array_ops.gather(@params[0], ids, name: name), ids, max_norm);
+                    return array_ops.identity(result);
+                }
+                else
+                {
+                    // Flatten the ids. There are two cases where we need to do this.
+                    throw new NotImplementedException("_embedding_lookup_and_transform");
                 }
-                return array_ops.identity(null);
-                throw new NotImplementedException("_embedding_lookup_and_transform");
             });
         }
 

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

@@ -20,7 +20,7 @@ namespace Tensorflow
             float epsilon = 1e-12f,
             string name = null)
         {
-            return with(ops.name_scope(name, "", new { x }), scope =>
+            return with(ops.name_scope(name, "l2_normalize", new { x }), scope =>
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 var square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keepdims: true);