rename with to tf_with, only use to construct graph purpose.

src/KerasNET.Core/Model.cs

@@ -115,7 +115,7 @@ namespace Keras
             var init = tf.global_variables_initializer();
 
             float loss_value = 0;
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 sess.run(init);
                 var step = 0;
@@ -133,7 +133,7 @@ namespace Keras
                         Console.WriteLine($"Step {step} loss: {loss_value}");
                 }
                 Console.WriteLine($"Final loss: {loss_value}");
-            });
+            }
 
             return loss_value;
         }

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

@@ -136,7 +136,7 @@ namespace Tensorflow
 
             public static Tensor bias_add(Tensor value, RefVariable bias, string data_format = null, string name = null)
             {
-                return Python.with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
+                return Python.tf_with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
                 {
                     name = scope;
                     return gen_nn_ops.bias_add(value, bias, data_format: data_format, name: name);
@@ -169,7 +169,7 @@ namespace Tensorflow
             /// <returns></returns>
             public static Tensor softmax_cross_entropy_with_logits(Tensor labels, Tensor logits, int dim = -1, string name = null)
             {
-                with(ops.name_scope(name, "softmax_cross_entropy_with_logits_sg", new { logits, labels }), scope =>
+                tf_with(ops.name_scope(name, "softmax_cross_entropy_with_logits_sg", new { logits, labels }), scope =>
                 {
                     name = scope;
                     labels = array_ops.stop_gradient(labels, name: "labels_stop_gradient");

src/TensorFlowNET.Core/Buffers/Buffer.cs

@@ -66,7 +66,7 @@ namespace Tensorflow
             return buffer.Data;
         }
 
-        protected override void DisposeUnManagedState()
-            => c_api.TF_DeleteBuffer(_handle);
+        protected override void DisposeUnManagedState(IntPtr handle)
+            => c_api.TF_DeleteBuffer(handle);
     }
 }

src/TensorFlowNET.Core/Clustering/_InitializeClustersOpFactory.cs

@@ -68,7 +68,7 @@ namespace Tensorflow.Clustering
 
         private Tensor _initialize()
         {
-            return with(ops.control_dependencies(new Operation[]
+            return tf_with(ops.control_dependencies(new Operation[]
             {
                 check_ops.assert_positive(_num_remaining)
             }), delegate

src/TensorFlowNET.Core/DisposableObject.cs

@@ -34,38 +34,27 @@ namespace Tensorflow
             _handle = handle;
         }
 
-        private bool disposedValue = false; // To detect redundant calls
-
         protected virtual void DisposeManagedState()
         {
         }
 
-        protected abstract void DisposeUnManagedState();
+        protected abstract void DisposeUnManagedState(IntPtr handle);
 
         protected virtual void Dispose(bool disposing)
         {
-            if (!disposedValue)
+            if (disposing)
             {
-                if (disposing)
+                // free unmanaged resources (unmanaged objects) and override a finalizer below.
+                if (_handle != IntPtr.Zero)
                 {
                     // dispose managed state (managed objects).
                     DisposeManagedState();
-                }
 
-                // free unmanaged resources (unmanaged objects) and override a finalizer below.
-                /*IntPtr h = IntPtr.Zero;
-                lock (this)
-                {
-                    h = _handle;
-                    _handle = IntPtr.Zero;
-                }*/
-                if (_handle != IntPtr.Zero)
-                    DisposeUnManagedState();
-
-                // set large fields to null.
-                _handle = IntPtr.Zero;
+                    // set large fields to null.
+                    DisposeUnManagedState(_handle);
 
-                disposedValue = true;
+                    _handle = IntPtr.Zero;
+                }
             }
         }
 

src/TensorFlowNET.Core/Framework/importer.py.cs

@@ -42,7 +42,7 @@ namespace Tensorflow
 
             string prefix = "";
             var graph = ops.get_default_graph();
-            with(ops.name_scope(name, "import", input_map.Values), scope =>
+            tf_with(ops.name_scope(name, "import", input_map.Values), scope =>
             {
                 prefix = scope;
                 /*if (!string.IsNullOrEmpty(prefix))

src/TensorFlowNET.Core/Gradients/gradients_util.cs

@@ -55,7 +55,7 @@ namespace Tensorflow
              **/
             var grads = new Dictionary<string, List<List<Tensor>>>();
 
-            with(ops.name_scope(name, "gradients",
+            tf_with(ops.name_scope(name, "gradients",
                 values: ys.Concat(xs).Concat(stop_gradients).Concat(grad_ys)), scope =>
                 {
                     string grad_scope = scope;
@@ -141,7 +141,7 @@ namespace Tensorflow
                                     }
                                 }
 
-                                with(ops.name_scope(op.name + "_grad"), scope1 =>
+                                tf_with(ops.name_scope(op.name + "_grad"), scope1 =>
                                 {
                                     string name1 = scope1;
                                     if (grad_fn != null)

src/TensorFlowNET.Core/Gradients/math_grad.cs

@@ -90,7 +90,7 @@ namespace Tensorflow.Gradients
         {
             var grad = grads[0];
             var y = op.outputs[0];  // y = e^x
-            return with(ops.control_dependencies(new Operation[] { grad }), dp => {
+            return tf_with(ops.control_dependencies(new Operation[] { grad }), dp => {
                 y = math_ops.conj(y);
                 return new Tensor[] { math_ops.mul_no_nan(y, grad) };
             });
@@ -107,7 +107,7 @@ namespace Tensorflow.Gradients
         {
             var grad = grads[0];
             var x = op.inputs[0];
-            return with(ops.control_dependencies(new Operation[] { grad }), dp => {
+            return tf_with(ops.control_dependencies(new Operation[] { grad }), dp => {
                 x = math_ops.conj(x);
                 return new Tensor[] { grad * math_ops.digamma(x) };
             });
@@ -118,7 +118,7 @@ namespace Tensorflow.Gradients
         {
             var grad = grads[0];
             var x = op.inputs[0];
-            return with(ops.control_dependencies(new Operation[] { grad }), dp => {
+            return tf_with(ops.control_dependencies(new Operation[] { grad }), dp => {
                 x = math_ops.conj(x);
                 return new Tensor[] { grad * math_ops.reciprocal(x) };
             });
@@ -431,7 +431,7 @@ namespace Tensorflow.Gradients
             var grad = grads[0];
             var y = op.outputs[0];
 
-            return with(ops.control_dependencies(grads), delegate
+            return tf_with(ops.control_dependencies(grads), delegate
             {
                 y = math_ops.conj(y);
                 return new Tensor[] { gen_math_ops.sigmoid_grad(y, grad) };
@@ -453,7 +453,7 @@ namespace Tensorflow.Gradients
             var grad = grads[0];
             var x = op.inputs[0];
 
-            return with(ops.control_dependencies(grads), delegate
+            return tf_with(ops.control_dependencies(grads), delegate
             {
                 x = math_ops.conj(x);
                 var y = constant_op.constant(2.0f, dtype: x.dtype);
@@ -467,7 +467,7 @@ namespace Tensorflow.Gradients
             var grad = grads[0];
             var y = op.outputs[0];
 
-            return with(ops.control_dependencies(grads), delegate
+            return tf_with(ops.control_dependencies(grads), delegate
             {
                 y = math_ops.conj(y);
                 return new Tensor[] { gen_math_ops.tanh_grad(y, grad) };

src/TensorFlowNET.Core/Keras/Layers/BatchNormalization.cs

@@ -207,7 +207,7 @@ namespace Tensorflow.Keras.Layers
 
         public Tensor _assign_moving_average(RefVariable variable, Tensor value, Tensor momentum)
         {
-            return Python.with(ops.name_scope(null, "AssignMovingAvg", new { variable, value, momentum }), scope =>
+            return Python.tf_with(ops.name_scope(null, "AssignMovingAvg", new { variable, value, momentum }), scope =>
             {
                 // var cm = ops.colocate_with(variable);
                 var decay = ops.convert_to_tensor(1.0f - momentum, name: "decay");

src/TensorFlowNET.Core/Keras/Layers/Layer.cs

@@ -125,7 +125,7 @@ namespace Tensorflow.Keras.Layers
                 // Symbolic execution on symbolic tensors. We will attempt to build
                 // the corresponding TF subgraph inside `backend.get_graph()`
                 var graph = backend.get_graph().as_default();
-                with(ops.name_scope(_name_scope()), delegate
+                tf_with(ops.name_scope(_name_scope()), delegate
                 {
                     // Build layer if applicable (if the `build` method has been
                     // overridden).

src/TensorFlowNET.Core/Layers/Layer.cs

@@ -72,7 +72,7 @@ namespace Tensorflow.Layers
             }
 
             Tensor outputs = null;
-            with(scope_context_manager, scope2 =>
+            tf_with(scope_context_manager, scope2 =>
             {
                 _current_scope = scope2;
                 // Actually call layer
@@ -136,12 +136,12 @@ namespace Tensorflow.Layers
 
             _set_scope();
             var reuse = built || (_reuse != null && _reuse.Value);
-            return with(tf.variable_scope(_scope,
+            return tf_with(tf.variable_scope(_scope,
                 reuse: reuse,
                 auxiliary_name_scope: false), scope =>
                 {
                     _current_scope = scope;
-                    return with(ops.name_scope(_name_scope()), delegate
+                    return tf_with(ops.name_scope(_name_scope()), delegate
                     {
                         var variable = base.add_weight(name,
                             shape,
@@ -183,7 +183,7 @@ namespace Tensorflow.Layers
                 }
                 else
                 {
-                    with(tf.variable_scope(scope, default_name: _base_name), captured_scope =>
+                    tf_with(tf.variable_scope(scope, default_name: _base_name), captured_scope =>
                     {
                         // convert variable_scope to VariableScope
                         _scope = captured_scope;

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

@@ -122,7 +122,7 @@ namespace Tensorflow.Operations
                     _external_values[result.name] = result;
                 }
                 
-                with(ops.control_dependencies(null), ctrl =>
+                tf_with(ops.control_dependencies(null), ctrl =>
                 {
                     var results = control_flow_ops._SwitchRefOrTensor(result, _pred);
                     result = results[_branch];

src/TensorFlowNET.Core/Operations/Distributions/distribution.py.cs

@@ -58,7 +58,7 @@ namespace Tensorflow
 
         private Tensor _call_log_prob (Tensor value, string name)
         {
-            return with(ops.name_scope(name, "moments", new { value }), scope =>
+            return tf_with(ops.name_scope(name, "moments", new { value }), scope =>
             {
                 try
                 {

src/TensorFlowNET.Core/Operations/Distributions/normal.py.cs

@@ -50,9 +50,9 @@ namespace Tensorflow
             parameters.Add("validate_args", validate_args);
             parameters.Add("allow_nan_stats", allow_nan_stats);
 
-            with(ops.name_scope(name, "", new { loc, scale }), scope => 
+            tf_with(ops.name_scope(name, "", new { loc, scale }), scope => 
             {
-                with(ops.control_dependencies(validate_args ? new Operation[] { scale.op} : new Operation[] { }), cd =>
+                tf_with(ops.control_dependencies(validate_args ? new Operation[] { scale.op} : new Operation[] { }), cd =>
                 {
                     this._loc = array_ops.identity(loc, name);
                     this._scale = array_ops.identity(scale, name);

src/TensorFlowNET.Core/Operations/Losses/losses_impl.py.cs

@@ -24,7 +24,7 @@ namespace Tensorflow
         public Tensor compute_weighted_loss(Tensor losses, Tensor weights = null, string scope = null,
             string loss_collection = ops.GraphKeys.LOSSES, string reduction = Reduction.SUM_BY_NONZERO_WEIGHTS)
         {
-            return with(ops.name_scope(scope, default_name: "weighted_loss", (losses, weights)), delegate
+            return tf_with(ops.name_scope(scope, default_name: "weighted_loss", (losses, weights)), delegate
             {
                 // Save the `reduction` argument for loss normalization when distributing
                 // to multiple replicas. Used only for estimator + v1 optimizer flow.
@@ -77,7 +77,7 @@ namespace Tensorflow
 
         public Tensor _num_present(Tensor losses, Tensor weights, bool per_batch = false)
         {
-            return with(ops.name_scope(null, default_name: "num_present", (losses, weights)), name_scope =>
+            return tf_with(ops.name_scope(null, default_name: "num_present", (losses, weights)), name_scope =>
             {
                 string scope = name_scope;
                 weights = math_ops.cast(weights, dtype: dtypes.float32);
@@ -104,7 +104,7 @@ namespace Tensorflow
             string loss_collection= ops.GraphKeys.LOSSES,
             string reduction = Reduction.SUM_BY_NONZERO_WEIGHTS)
         {
-            return with(ops.name_scope(scope,
+            return tf_with(ops.name_scope(scope,
                 "sparse_softmax_cross_entropy_loss",
                 (logits, labels, weights)),
                 name_scope =>

src/TensorFlowNET.Core/Operations/NnOps/MaxPoolFunction.cs

@@ -30,7 +30,7 @@ namespace Tensorflow.Operations
             string data_format = "NHWC",
             string name = null)
         {
-            return with(ops.name_scope(name, "MaxPool", value), scope => 
+            return tf_with(ops.name_scope(name, "MaxPool", value), scope => 
             {
                 name = scope;
                 value = ops.convert_to_tensor(value, name: "input");

src/TensorFlowNET.Core/Operations/NnOps/rnn.cs

@@ -29,7 +29,7 @@ namespace Tensorflow.Operations
             TF_DataType dtype = TF_DataType.DtInvalid,
             int? parallel_iterations = null, bool swap_memory = false, bool time_major = false)
         {
-            with(tf.variable_scope("rnn"), scope =>
+            tf_with(tf.variable_scope("rnn"), scope =>
             {
                 VariableScope varscope = scope;
                 var flat_input = nest.flatten(inputs_tensor);
@@ -139,7 +139,7 @@ namespace Tensorflow.Operations
             var time = array_ops.constant(0, dtype: dtypes.int32, name: "time");
 
             string base_name = null;
-            with(ops.name_scope("dynamic_rnn"), scope => base_name = scope);
+            tf_with(ops.name_scope("dynamic_rnn"), scope => base_name = scope);
 
             Func<string, TensorShape, TF_DataType, Tensor> _create_ta = (name, element_shape, dtype_) =>
             {

src/TensorFlowNET.Core/Operations/OpDefLibrary.cs

@@ -58,7 +58,7 @@ namespace Tensorflow
             var input_types = new List<TF_DataType>();
             object values = null;
 
-            return with(ops.name_scope(name), scope =>
+            return tf_with(ops.name_scope(name), scope =>
             {
                 var inferred_from = new Dictionary<string, object>();
                 var base_types = new List<TF_DataType>();

src/TensorFlowNET.Core/Operations/RNNCell.cs

@@ -82,7 +82,7 @@ namespace Tensorflow
         {
             Tensor output = null;
             var state_size = this.state_size;
-            with(ops.name_scope($"{this.GetType().Name}ZeroState", values: new { batch_size }), delegate
+            tf_with(ops.name_scope($"{this.GetType().Name}ZeroState", values: new { batch_size }), delegate
             {
                 output = _zero_state_tensors(state_size, batch_size, dtype);
             });

src/TensorFlowNET.Core/Operations/_GraphTensorArray.cs

@@ -66,7 +66,7 @@ namespace Tensorflow.Operations
                 _element_shape = new List<TensorShape> { };
             }
 
-            with(ops.name_scope(name, "", new { handle, size, flow }), scope =>
+            tf_with(ops.name_scope(name, "", new { handle, size, flow }), scope =>
             {
                 if(handle != null)
                 {

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

@@ -43,7 +43,7 @@ namespace Tensorflow
         public static Tensor zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return with(ops.name_scope(name, "zeros", shape), scope =>
+            return tf_with(ops.name_scope(name, "zeros", shape), scope =>
             {
                 name = scope;
                 switch (dtype)
@@ -67,7 +67,7 @@ namespace Tensorflow
         public static Tensor zeros(Tensor shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return with(ops.name_scope(name, "zeros", shape), scope =>
+            return tf_with(ops.name_scope(name, "zeros", shape), scope =>
             {
                 name = scope;
                 switch (dtype)
@@ -140,7 +140,7 @@ namespace Tensorflow
         {
             var must_pack = false;
             var converted_elems = new List<object>();
-            return with(ops.name_scope(name), scope =>
+            return tf_with(ops.name_scope(name), scope =>
             {
                 foreach (var (i, elem) in enumerate(list_or_tuple))
                 {
@@ -189,7 +189,7 @@ namespace Tensorflow
 
         public static Tensor rank_internal(Tensor input, string name = null, bool optimize = true)
         {
-            return with(ops.name_scope(name, "Rank", new List<Tensor> { input }), scope =>
+            return tf_with(ops.name_scope(name, "Rank", new List<Tensor> { input }), scope =>
             {
                 name = scope;
                 var input_tensor = ops.convert_to_tensor(input);
@@ -217,7 +217,7 @@ namespace Tensorflow
 
         private static Tensor ones_like_impl<T>(T tensor, TF_DataType dtype, string name, bool optimize = true)
         {
-            return with(ops.name_scope(name, "ones_like", new { tensor }), scope =>
+            return tf_with(ops.name_scope(name, "ones_like", new { tensor }), scope =>
             {
                 name = scope;
                 var tensor1 = ops.convert_to_tensor(tensor, name: "tensor");
@@ -233,7 +233,7 @@ namespace Tensorflow
         public static Tensor ones(Tensor shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return with(ops.name_scope(name, "ones", new { shape }), scope =>
+            return tf_with(ops.name_scope(name, "ones", new { shape }), scope =>
             {
                 name = scope;
                 var output = gen_array_ops.fill(shape, constant_op.constant(1.0f, dtype: dtype), name: name);
@@ -244,7 +244,7 @@ namespace Tensorflow
         public static Tensor ones(Tensor[] shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return with(ops.name_scope(name, "ones", new { shape }), scope =>
+            return tf_with(ops.name_scope(name, "ones", new { shape }), scope =>
             {
                 name = scope;
                 var output = _constant_if_small(1, shape[0]);
@@ -257,7 +257,7 @@ namespace Tensorflow
         public static Tensor ones(int[] dims, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return with(ops.name_scope(name, "ones", new { dims }), scope =>
+            return tf_with(ops.name_scope(name, "ones", new { dims }), scope =>
             {
                 name = scope;
                 var shape = ops.convert_to_tensor(dims, dtype: TF_DataType.TF_INT32);
@@ -273,7 +273,7 @@ namespace Tensorflow
             int axis = -1,
             string name = null)
         {
-            return with(ops.name_scope(name, "one_hot", new { indices, depth, dtype }), scope =>
+            return tf_with(ops.name_scope(name, "one_hot", new { indices, depth, dtype }), scope =>
             {
                 name = scope;
                 var on_exists = false;
@@ -341,7 +341,7 @@ namespace Tensorflow
 
         private static Tensor shape_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
         {
-            return with(ops.name_scope(name, "Shape", new { input }), scope =>
+            return tf_with(ops.name_scope(name, "Shape", new { input }), scope =>
             {
                 name = scope;
 
@@ -362,7 +362,7 @@ namespace Tensorflow
 
         private static Tensor size_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
         {
-            return with(ops.name_scope(name, "Size", new { input }), scope =>
+            return tf_with(ops.name_scope(name, "Size", new { input }), scope =>
             {
                 name = scope;
 
@@ -382,7 +382,7 @@ namespace Tensorflow
 
         public static Tensor zeros_like(Tensor tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
         {
-            return with(ops.name_scope(name, "zeros_like", new Tensor[] { tensor }), scope =>
+            return tf_with(ops.name_scope(name, "zeros_like", new Tensor[] { tensor }), scope =>
             {
                 name = scope;
                 tensor = ops.convert_to_tensor(tensor, name: "tensor");
@@ -516,7 +516,7 @@ namespace Tensorflow
         {
             if(values.Length == 1) // Degenerate case of one tensor.
             {
-                return with(ops.name_scope(name), scope => {
+                return tf_with(ops.name_scope(name), scope => {
                     var t = ops.convert_to_tensor(axis, name: "concat_dim", dtype: TF_DataType.TF_INT32);
                     return identity(values[0], name: scope);
                 });
@@ -535,7 +535,7 @@ namespace Tensorflow
 
         public static Tensor transpose<T1, T2>(T1 a, T2 perm, string name = "transpose", bool conjugate = false)
         {
-            return with(ops.name_scope(name, "transpose", new { a }), scope =>
+            return tf_with(ops.name_scope(name, "transpose", new { a }), scope =>
             {
                 return gen_array_ops.transpose(a, perm, name: scope);
             });

src/TensorFlowNET.Core/Operations/check_ops.cs

@@ -31,7 +31,7 @@ namespace Tensorflow
             if (message == null)
                 message = "";
 
-            return with(ops.name_scope(name, "assert_equal", new { t1, t2, data }), delegate
+            return tf_with(ops.name_scope(name, "assert_equal", new { t1, t2, data }), delegate
             {
                 var x = ops.convert_to_tensor(t1, name: "x");
                 var y = ops.convert_to_tensor(t2, name: "y");
@@ -62,7 +62,7 @@ namespace Tensorflow
             if (message == null)
                 message = "";
 
-            return with(ops.name_scope(name, "assert_positive", new { x, data }), delegate
+            return tf_with(ops.name_scope(name, "assert_positive", new { x, data }), delegate
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 if (data == null)
@@ -86,7 +86,7 @@ namespace Tensorflow
             if (message == null)
                 message = "";
 
-            return with(ops.name_scope(name, "assert_less", new { x, y, data }), delegate
+            return tf_with(ops.name_scope(name, "assert_less", new { x, y, data }), delegate
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 y = ops.convert_to_tensor(y, name: "y");

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

@@ -34,7 +34,7 @@ namespace Tensorflow
             int expected_rank_diff = 0,
             string name = null)
         {
-            return with(ops.name_scope(name, default_name: "remove_squeezable_dimensions", (labels, predictions)), delegate
+            return tf_with(ops.name_scope(name, default_name: "remove_squeezable_dimensions", (labels, predictions)), delegate
             {
                 predictions = ops.convert_to_tensor(predictions);
                 labels = ops.convert_to_tensor(labels);

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

@@ -28,7 +28,7 @@ namespace Tensorflow
     {
         public static Operation Assert(Tensor condition, object[] data, int? summarize = null, string name = null)
         {
-            return with(ops.name_scope(name, "Assert", new { condition, data }), scope =>
+            return tf_with(ops.name_scope(name, "Assert", new { condition, data }), scope =>
             {
                 name = scope;
                 var xs = ops.convert_n_to_tensor(data);
@@ -53,7 +53,7 @@ namespace Tensorflow
 
         public static Operation group<T>(T[] inputs, string name = null) where T : ITensorOrOperation
         {
-            return with(ops.name_scope(name, "group_deps", inputs), scope =>
+            return tf_with(ops.name_scope(name, "group_deps", inputs), scope =>
             {
                 name = scope;
 
@@ -91,7 +91,7 @@ namespace Tensorflow
 
         private static Operation _GroupControlDeps(string dev, Operation[] deps, string name = null)
         {
-            return with(ops.control_dependencies(deps), ctl =>
+            return tf_with(ops.control_dependencies(deps), ctl =>
             {
                 if (dev == null)
                 {
@@ -135,7 +135,7 @@ namespace Tensorflow
 
         public static Tensor[] tuple(Tensor[] tensors, string name = null, Operation[] control_inputs = null)
         {
-            return with(ops.name_scope(name, "tuple", tensors), scope =>
+            return tf_with(ops.name_scope(name, "tuple", tensors), scope =>
             {
                 name = scope;
                 var gating_ops = tensors.Where(x => x != null).Select(x => x.op).ToList();
@@ -189,13 +189,13 @@ namespace Tensorflow
             values.AddRange(dependencies);
             values.Add(output_tensor);
 
-            return with(ops.name_scope(name, "control_dependency", values), scope =>
+            return tf_with(ops.name_scope(name, "control_dependency", values), scope =>
             {
                 name = scope;
                 // TODO: missing original code
                 //with ops.colocate_with(output_tensor):
                 {
-                    return with(ops.control_dependencies(dependencies), ctl =>
+                    return tf_with(ops.control_dependencies(dependencies), ctl =>
                     {
                         output_tensor = ops.convert_to_tensor_or_composite(output_tensor);
                         return _Identity(output_tensor, name: name);
@@ -306,7 +306,7 @@ namespace Tensorflow
             bool strict = false,
             string name = null)
         {
-            return with(ops.name_scope(name, "cond", new { pred }), delegate
+            return tf_with(ops.name_scope(name, "cond", new { pred }), delegate
             {
                 // TODO: here a chunk of original code is missing
                 /*
@@ -398,7 +398,7 @@ namespace Tensorflow
             bool strict = false,
             string name = null)
         {
-            return with(ops.name_scope(name, "cond", new { pred }), delegate
+            return tf_with(ops.name_scope(name, "cond", new { pred }), delegate
             {
                 // Add the Switch to the graph.
                 var switch_result = @switch(pred, pred);
@@ -467,7 +467,7 @@ namespace Tensorflow
         {
             if (inputs.Any(x => x == null))
                 throw new ValueError($"At least one of the merge inputs is null: {inputs}");
-            return with(ops.name_scope(name, "Merge", inputs), scope =>
+            return tf_with(ops.name_scope(name, "Merge", inputs), scope =>
             {
                 name = scope;
                 inputs = inputs.Select(inp =>
@@ -489,7 +489,7 @@ namespace Tensorflow
             TF_DataType dtype = TF_DataType.DtInvalid, 
             string name = null)
         {
-            return with(ops.name_scope(name, "Switch", new { data, pred }), scope =>
+            return tf_with(ops.name_scope(name, "Switch", new { data, pred }), scope =>
             {
                 name = scope;
                 data = ops.internal_convert_to_tensor_or_indexed_slices(data,

src/TensorFlowNET.Core/Operations/embedding_ops.cs

@@ -35,7 +35,7 @@ namespace Tensorflow
                 string name = null,
                 string max_norm = null)
         {
-            return with(ops.name_scope(name, "embedding_lookup", new { @params, ids }), scope =>
+            return tf_with(ops.name_scope(name, "embedding_lookup", new { @params, ids }), scope =>
             {
                 name = scope;
                 int np = 1;
@@ -58,7 +58,7 @@ namespace Tensorflow
                 string name = null,
                 string max_norm = null)
         {
-            return with(ops.name_scope(name, "embedding_lookup", new { @params, ids }), scope =>
+            return tf_with(ops.name_scope(name, "embedding_lookup", new { @params, ids }), scope =>
             {
                 name = scope;
                 int np = @params.Length;

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

@@ -28,7 +28,7 @@ namespace Tensorflow
             if (dtype == image.dtype)
                 return array_ops.identity(image, name: name);
 
-            return with(ops.name_scope(name, "convert_image", image), scope =>
+            return tf_with(ops.name_scope(name, "convert_image", image), scope =>
             {
                 name = scope;
 

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -29,7 +29,7 @@ namespace Tensorflow
     {
         public static Tensor abs(Tensor x, string name = null)
         {
-            return with(ops.name_scope(name, "Abs", new { x }), scope =>
+            return tf_with(ops.name_scope(name, "Abs", new { x }), scope =>
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 if (x.dtype.is_complex())
@@ -69,7 +69,7 @@ namespace Tensorflow
             if(base_type == x.dtype)
                 return x;
 
-            return with(ops.name_scope(name, "Cast", new { x }), scope =>
+            return tf_with(ops.name_scope(name, "Cast", new { x }), scope =>
             {
                 name = scope;
                 x = ops.convert_to_tensor(x, name: "x");
@@ -82,7 +82,7 @@ namespace Tensorflow
 
         public static Tensor cumsum(Tensor x, int axis = 0, bool exclusive = false, bool reverse = false, string name = null)
         {
-            return with(ops.name_scope(name, "Cumsum", new {x}), scope =>
+            return tf_with(ops.name_scope(name, "Cumsum", new {x}), scope =>
             {
                 name = scope;
                 x = ops.convert_to_tensor(x, name: "x");
@@ -110,7 +110,7 @@ namespace Tensorflow
         /// <returns>`x / y` returns the quotient of x and y.</returns>
         public static Tensor div(Tensor x, Tensor y, string name = null)
         {
-            return with(ops.name_scope(name, "div", (x, y)), name_scope =>
+            return tf_with(ops.name_scope(name, "div", (x, y)), name_scope =>
             {
                 name = name_scope;
                 x = ops.convert_to_tensor(x, name: "x");
@@ -146,7 +146,7 @@ namespace Tensorflow
         /// </remarks>
         public static Tensor div_no_nan(Tensor x, Tensor y, string name = null)
         {
-            return with(ops.name_scope(name, "div_no_nan", (x, y)), name_scope =>
+            return tf_with(ops.name_scope(name, "div_no_nan", (x, y)), name_scope =>
             {
                 name = name_scope;
                 x = ops.convert_to_tensor(x, name: "x");
@@ -229,7 +229,7 @@ namespace Tensorflow
 
         public static Tensor sign(Tensor x, string name = null)
         {
-            return with(ops.name_scope(name, "Sign", new {x}), scope =>
+            return tf_with(ops.name_scope(name, "Sign", new {x}), scope =>
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 return gen_math_ops.sign(x);
@@ -337,7 +337,7 @@ namespace Tensorflow
         /// <returns> The reduced tensor.</returns>
         public static Tensor reduce_logsumexp(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
         {
-            return with(ops.name_scope(name, "ReduceLogSumExp", new { input_tensor }), scope =>
+            return tf_with(ops.name_scope(name, "ReduceLogSumExp", new { input_tensor }), scope =>
             {
                 var raw_max = reduce_max(input_tensor, axis, true);
                 var my_max = array_ops.stop_gradient(array_ops.where(gen_math_ops.is_finite(raw_max), raw_max, array_ops.zeros_like(raw_max)));
@@ -497,7 +497,7 @@ namespace Tensorflow
             if (delta == null)
                 delta = 1;
 
-            return with(ops.name_scope(name, "Range", new { start, limit, delta }), scope =>
+            return tf_with(ops.name_scope(name, "Range", new { start, limit, delta }), scope =>
             {
                 name = scope;
                 var start1 = ops.convert_to_tensor(start, name: "start");
@@ -510,7 +510,7 @@ namespace Tensorflow
 
         public static Tensor floordiv(Tensor x, Tensor y, string name = null)
         {
-            return with(ops.name_scope(name, "floordiv", new { x, y }), scope =>
+            return tf_with(ops.name_scope(name, "floordiv", new { x, y }), scope =>
             {
                 return gen_math_ops.floor_div(x, y, scope);
             });
@@ -527,7 +527,7 @@ namespace Tensorflow
         {
             Tensor result = null;
 
-            with(ops.name_scope(name, "MatMul", new Tensor[] { a, b }), scope =>
+            tf_with(ops.name_scope(name, "MatMul", new Tensor[] { a, b }), scope =>
             {
                 name = scope;
 
@@ -551,7 +551,7 @@ namespace Tensorflow
         {
             Tensor result = null;
 
-            with(ops.name_scope(name, "MatMul", new Tensor[] { x, y }), scope =>
+            tf_with(ops.name_scope(name, "MatMul", new Tensor[] { x, y }), scope =>
             {
                 name = scope;
 
@@ -576,7 +576,7 @@ namespace Tensorflow
             if (dt.is_floating() || dt.is_integer())
                 return x;
 
-            return with(ops.name_scope(name, "Conj", new List<Tensor> { x }), scope =>
+            return tf_with(ops.name_scope(name, "Conj", new List<Tensor> { x }), scope =>
             {
 
                 return x;
@@ -591,7 +591,7 @@ namespace Tensorflow
 
         public static Tensor _truediv_python3(Tensor x, Tensor y, string name = null)
         {
-            return with(ops.name_scope(name, "truediv", new { x, y }), scope =>
+            return tf_with(ops.name_scope(name, "truediv", new { x, y }), scope =>
             {
                 name = scope;
                 var x_dtype = x.dtype.as_base_dtype();

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

@@ -34,7 +34,7 @@ namespace Tensorflow
             float epsilon = 1e-12f,
             string name = null)
         {
-            return with(ops.name_scope(name, "l2_normalize", new { x }), scope =>
+            return tf_with(ops.name_scope(name, "l2_normalize", new { x }), scope =>
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 var sq = math_ops.square(x);
@@ -57,7 +57,7 @@ namespace Tensorflow
             string name = null,
             bool keep_dims = false)
         {
-            return with(ops.name_scope(name, "moments", new { x, axes }), scope =>
+            return tf_with(ops.name_scope(name, "moments", new { x, axes }), scope =>
             {
                 // The dynamic range of fp16 is too limited to support the collection of
                 // sufficient statistics. As a workaround we simply perform the operations
@@ -123,7 +123,7 @@ namespace Tensorflow
         /// <returns>number of nonzero values with type dtype</returns>
         private static Tensor _count_nonzero(Tensor input_tensor, TF_DataType dtype = TF_DataType.TF_INT64)
         {
-            return with(ops.name_scope("count_nonzero", "count_nonzero", new { input_tensor }), scope =>
+            return tf_with(ops.name_scope("count_nonzero", "count_nonzero", new { input_tensor }), scope =>
             {
                 var zero = array_ops.zeros(new NumSharp.Shape(), dtype: input_tensor.dtype);
                 var nonzero_count = math_ops.reduce_sum(
@@ -140,7 +140,7 @@ namespace Tensorflow
         /// <returns>The fraction of zeros in value, with type float32.</returns>
         public static Tensor zero_fraction(Tensor value, string name = null)
         {
-            return with(ops.name_scope(name, "zero_fraction", new { value }), scope =>
+            return tf_with(ops.name_scope(name, "zero_fraction", new { value }), scope =>
             {
                 value = ops.convert_to_tensor(value, name: "value");
                 Tensor size = array_ops.size(value, out_type: dtypes.int64);
@@ -153,7 +153,7 @@ namespace Tensorflow
                         () => _count_nonzero(value, dtype: dtypes.int64)
                         );
 
-                with(ops.name_scope("counts_to_fraction"), count_scope =>
+                tf_with(ops.name_scope("counts_to_fraction"), count_scope =>
                 {
                     var num_zero = math_ops.subtract(math_ops.cast(size, TF_DataType.TF_INT64), num_nonzero);
                     var num_zero_float32 = math_ops.cast(num_zero, dtype: dtypes.float32);

src/TensorFlowNET.Core/Operations/nn_ops.cs

@@ -50,7 +50,7 @@ namespace Tensorflow
             string data_format = null, 
             string name = null)
         {
-            return Python.with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
+            return Python.tf_with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
             {
                 name = scope;
                 value = ops.convert_to_tensor(value, name: "input");
@@ -70,7 +70,7 @@ namespace Tensorflow
         /// <returns></returns>
         public static Tensor dropout_v2(Tensor x, Tensor rate, Tensor noise_shape = null, int? seed = null, string name = null)
         {
-            return with(ops.name_scope(name, "dropout", x), scope =>
+            return tf_with(ops.name_scope(name, "dropout", x), scope =>
             {
                 name = scope;
                 x = ops.convert_to_tensor(x, name: "x");
@@ -134,7 +134,7 @@ namespace Tensorflow
         /// <returns></returns>
         public static Tensor max_pool(Tensor value, int[] ksize, int[] strides, string padding, string data_format = "NHWC", string name = null)
         {
-            return with(ops.name_scope(name, "MaxPool", value), scope =>
+            return tf_with(ops.name_scope(name, "MaxPool", value), scope =>
             {
                 name = scope;
                 value = ops.convert_to_tensor(value, name: "input");
@@ -171,7 +171,7 @@ namespace Tensorflow
             Tensor logits = null, string name = null)
         {
             // Reshape logits and labels to rank 2.
-            return with(ops.name_scope(name, default_name: "SparseSoftmaxCrossEntropyWithLogits", (labels, logits)), delegate
+            return tf_with(ops.name_scope(name, default_name: "SparseSoftmaxCrossEntropyWithLogits", (labels, logits)), delegate
             {
                 labels = ops.convert_to_tensor(labels);
                 logits = ops.convert_to_tensor(logits);
@@ -206,7 +206,7 @@ namespace Tensorflow
             int axis = -1,
             string name = null)
         {
-            return with(ops.name_scope(name, "softmax_cross_entropy_with_logits", new { logits, labels }), scope =>
+            return tf_with(ops.name_scope(name, "softmax_cross_entropy_with_logits", new { logits, labels }), scope =>
             {
                 name = scope;
                 var precise_logits = logits;

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

@@ -37,7 +37,7 @@ namespace Tensorflow
             int? seed = null, 
             string name = null)
         {
-            return with(ops.name_scope(name, "random_normal", new { shape, mean, stddev }), scope =>
+            return tf_with(ops.name_scope(name, "random_normal", new { shape, mean, stddev }), scope =>
             {
                 var shape_tensor = _ShapeTensor(shape);
                 var mean_tensor = ops.convert_to_tensor(mean, dtype: dtype, name: "mean");
@@ -67,7 +67,7 @@ namespace Tensorflow
             int? seed = null, 
             string name = null)
         {
-            return with(ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
+            return tf_with(ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
             {
                 name = scope;
                 var tensorShape = _ShapeTensor(shape);
@@ -85,7 +85,7 @@ namespace Tensorflow
             int? seed = null,
             string name = null)
         {
-            return with(ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
+            return tf_with(ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
             {
                 name = scope;
                 var minTensor = ops.convert_to_tensor(minval, dtype: dtype, name: "min");
@@ -110,7 +110,7 @@ namespace Tensorflow
             int? seed = null,
             string name = null)
         {
-            return with(ops.name_scope(name, "truncated_normal", new { shape, mean, stddev }), scope =>
+            return tf_with(ops.name_scope(name, "truncated_normal", new { shape, mean, stddev }), scope =>
             {
                 name = scope;
                 var shape_tensor = _ShapeTensor(shape);

src/TensorFlowNET.Core/Operations/weights_broadcast_ops.cs

@@ -22,7 +22,7 @@ namespace Tensorflow
     {
         public static Tensor broadcast_weights(Tensor weights, Tensor values)
         {
-            return with(ops.name_scope(null, "broadcast_weights", (weights, values)), scope =>
+            return tf_with(ops.name_scope(null, "broadcast_weights", (weights, values)), scope =>
             {
                 values = ops.convert_to_tensor(values, name: "values");
                 weights = ops.convert_to_tensor(

src/TensorFlowNET.Core/Python.cs

@@ -75,7 +75,7 @@ namespace Tensorflow
         }
 
         [DebuggerNonUserCode()] // with "Just My Code" enabled this lets the debugger break at the origin of the exception
-        public static void with(IPython py, Action<IPython> action)
+        public static void tf_with(IPython py, Action<IPython> action)
         {
             try
             {
@@ -95,7 +95,7 @@ namespace Tensorflow
         }
 
         [DebuggerNonUserCode()] // with "Just My Code" enabled this lets the debugger break at the origin of the exception
-        public static void with<T>(T py, Action<T> action) where T : IPython
+        public static void tf_with<T>(T py, Action<T> action) where T : IPython
         {
             try
             {
@@ -115,7 +115,7 @@ namespace Tensorflow
         }
 
         [DebuggerNonUserCode()] // with "Just My Code" enabled this lets the debugger break at the origin of the exception
-        public static TOut with<TIn, TOut>(TIn py, Func<TIn, TOut> action) where TIn : IPython
+        public static TOut tf_with<TIn, TOut>(TIn py, Func<TIn, TOut> action) where TIn : IPython
         {
             try
             {

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -326,11 +326,11 @@ namespace Tensorflow
             Dispose();
         }
 
-        protected override void DisposeUnManagedState()
+        protected override void DisposeUnManagedState(IntPtr handle)
         {
             using (var status = new Status())
             {
-                c_api.TF_DeleteSession(_handle, status);
+                c_api.TF_DeleteSession(handle, status);
                 status.Check(true);
             }
         }

src/TensorFlowNET.Core/Sessions/SessionOptions.cs

@@ -32,8 +32,8 @@ namespace Tensorflow
             _handle = handle;
         }
 
-        protected override void DisposeUnManagedState()
-            => c_api.TF_DeleteSessionOptions(_handle);
+        protected override void DisposeUnManagedState(IntPtr handle)
+            => c_api.TF_DeleteSessionOptions(handle);
 
         public void SetConfig(ConfigProto config)
         {

src/TensorFlowNET.Core/Status/Status.cs

@@ -65,7 +65,7 @@ namespace Tensorflow
             return status._handle;
         }
 
-        protected override void DisposeUnManagedState()
-            => c_api.TF_DeleteStatus(_handle);
+        protected override void DisposeUnManagedState(IntPtr handle)
+            => c_api.TF_DeleteStatus(handle);
     }
 }

src/TensorFlowNET.Core/Summaries/Summary.cs

@@ -55,7 +55,7 @@ namespace Tensorflow.Summaries
         /// <returns></returns>
         public Tensor merge(Tensor[] inputs, string[] collections = null, string name = null)
         {
-            return with(ops.name_scope(name, "Merge", inputs), delegate
+            return tf_with(ops.name_scope(name, "Merge", inputs), delegate
             {
                 var val = gen_logging_ops.merge_summary(inputs: inputs, name: name);
                 collect(val, collections?.ToList(), new List<string>());
@@ -88,7 +88,7 @@ namespace Tensorflow.Summaries
         public (string, string) summary_scope(string name, string family = null, string default_name = null, Tensor[] values = null)
         {
             string scope_base_name = string.IsNullOrEmpty(family) ? name : $"{family}/{name}";
-            return with(ops.name_scope(scope_base_name, default_name: default_name, values), scope =>
+            return tf_with(ops.name_scope(scope_base_name, default_name: default_name, values), scope =>
             {
                 var tag = scope._name_scope;
                 if (string.IsNullOrEmpty(family))

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -5,7 +5,7 @@
     <AssemblyName>TensorFlow.NET</AssemblyName>
     <RootNamespace>Tensorflow</RootNamespace>
     <TargetTensorFlow>1.14.0</TargetTensorFlow>
-    <Version>0.10.7.2</Version>
+    <Version>0.10.8</Version>
     <Authors>Haiping Chen, Meinrad Recheis</Authors>
     <Company>SciSharp STACK</Company>
     <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
@@ -17,7 +17,7 @@
     <PackageTags>TensorFlow, NumSharp, SciSharp, MachineLearning, TensorFlow.NET, C#</PackageTags>
     <Description>Google's TensorFlow full binding in .NET Standard.
 Docs: https://tensorflownet.readthedocs.io</Description>
-    <AssemblyVersion>0.10.7.2</AssemblyVersion>
+    <AssemblyVersion>0.10.8.0</AssemblyVersion>
     <PackageReleaseNotes>Changes since v0.9.0:
 
 1. Added full connected Convolution Neural Network example.
@@ -34,9 +34,10 @@ Docs: https://tensorflownet.readthedocs.io</Description>
 12. Add Tensor operator overloads.
 13. Fix default graph and operation issue when import model.
 14. Fix TF_String endcode and decode.
-15. Fix Tensor memory leak.</PackageReleaseNotes>
+15. Fix Tensor memory leak.
+16. Rename with to tf_with that is only used to build graph purpose.</PackageReleaseNotes>
     <LangVersion>7.2</LangVersion>
-    <FileVersion>0.10.7.2</FileVersion>
+    <FileVersion>0.10.8.0</FileVersion>
     <PackageLicenseFile>LICENSE</PackageLicenseFile>
     <PackageRequireLicenseAcceptance>true</PackageRequireLicenseAcceptance>
     <SignAssembly>true</SignAssembly>

src/TensorFlowNET.Core/Tensors/Tensor.Operators.cs

@@ -123,7 +123,7 @@ namespace Tensorflow
                 dtype = tr.dtype.as_base_dtype();
 
             var namescope = ops.name_scope(null, name, new { x, y });
-            return with(namescope, scope =>
+            return tf_with(namescope, scope =>
             {
                 Tensor result = null;
                 var x1 = ops.convert_to_tensor(x, dtype: dtype, name: "x");

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -300,7 +300,7 @@ namespace Tensorflow
                 index += 1;
             }
 
-            return with(ops.name_scope(null, "strided_slice", new { begin, end, strides }), scope =>
+            return tf_with(ops.name_scope(null, "strided_slice", new { begin, end, strides }), scope =>
             {
                 string name = scope;
                 if (begin != null)
@@ -349,7 +349,7 @@ namespace Tensorflow
                 index += 1;
             }
 
-            return with(ops.name_scope(null, "strided_slice", new { begin, end, strides }), scope =>
+            return tf_with(ops.name_scope(null, "strided_slice", new { begin, end, strides }), scope =>
             {
                 string name = scope;
                 if (begin != null)
@@ -392,8 +392,13 @@ namespace Tensorflow
             return $"tf.Tensor '{name}' shape=({string.Join(",", shape)}) dtype={dtype}";
         }
 
-        protected override void DisposeUnManagedState()
-            => c_api.TF_DeleteTensor(_handle);
+        protected override void DisposeUnManagedState(IntPtr handle)
+        {
+            if(handle != IntPtr.Zero)
+            {
+                c_api.TF_DeleteTensor(handle);
+            }
+        }
 
         public bool IsDisposed
         {

src/TensorFlowNET.Core/Train/AdamOptimizer.cs

@@ -81,7 +81,7 @@ namespace Tensorflow.Train
             var m = get_slot(var, "m");
             var m_scaled_g_values = grad * (1 - beta1_t);
             var m_t = state_ops.assign(m, m * beta1_t, use_locking: _use_locking);
-            with(ops.control_dependencies(new[] { m_t }), delegate
+            tf_with(ops.control_dependencies(new[] { m_t }), delegate
             {
                 m_t = scatter_add(m, indices, m_scaled_g_values);
             });
@@ -89,7 +89,7 @@ namespace Tensorflow.Train
             var v = get_slot(var, "v");
             var v_scaled_g_values = (grad * grad) * (1 - beta2_t);
             var v_t = state_ops.assign(v, v * beta2_t, use_locking: _use_locking);
-            with(ops.control_dependencies(new[] { v_t }), delegate
+            tf_with(ops.control_dependencies(new[] { v_t }), delegate
             {
                 v_t = scatter_add(v, indices, v_scaled_g_values);
             });
@@ -117,7 +117,7 @@ namespace Tensorflow.Train
             var operations = new List<ITensorOrOperation>();
             operations.AddRange(update_ops);
 
-            with(ops.control_dependencies(update_ops), delegate
+            tf_with(ops.control_dependencies(update_ops), delegate
             {
                 var (beta1_power, beta2_power) = _get_beta_accumulators();
                 ops.colocate_with(beta1_power);

src/TensorFlowNET.Core/Train/Optimizer.cs

@@ -151,7 +151,7 @@ namespace Tensorflow
             _create_slots(var_list);
 
             var update_ops = new List<Operation>();
-            return with(ops.name_scope(name, Name), scope =>
+            return tf_with(ops.name_scope(name, Name), scope =>
             {
                 name = scope;
                 _prepare();
@@ -162,7 +162,7 @@ namespace Tensorflow
                         continue;
 
                     var scope_name = var.op.name;
-                    with(ops.name_scope("update_" + scope_name), scope2 =>
+                    tf_with(ops.name_scope("update_" + scope_name), scope2 =>
                     {
                         var op = processor.update_op(this, grad);
                         update_ops.Add(op);
@@ -176,7 +176,7 @@ namespace Tensorflow
                 }
                 else
                 {
-                    with(ops.control_dependencies(new object[] {_finish(update_ops.ToArray(), "update")}), dep =>
+                    tf_with(ops.control_dependencies(new object[] {_finish(update_ops.ToArray(), "update")}), dep =>
                     {
                         ops.colocate_with(global_step);
                         // TODO: port this if branch once ResourceVariable has been ported!

src/TensorFlowNET.Core/Train/Saving/BaseSaverBuilder.cs

@@ -102,7 +102,7 @@ namespace Tensorflow
             Tensor save_tensor = null;
             Operation restore_op = null;
 
-            return with(ops.name_scope(name, "save", saveables.Select(x => x.op).ToArray()), scope =>
+            return tf_with(ops.name_scope(name, "save", saveables.Select(x => x.op).ToArray()), scope =>
             {
                 name = scope;
 

src/TensorFlowNET.Core/Train/SlotCreator.cs

@@ -57,7 +57,7 @@ namespace Tensorflow.Train
         {
             var validate_shape = shape.is_fully_defined();
             var prefix = primary.op.name;
-            return with(new variable_scope(string.Empty, prefix + "/" + name), delegate
+            return tf_with(new variable_scope(string.Empty, prefix + "/" + name), delegate
             {
                 return _create_slot_var(primary, initializer, "", validate_shape, shape, dtype);
             });

src/TensorFlowNET.Core/Variables/RefVariable.Operators.cs

@@ -32,7 +32,7 @@ namespace Tensorflow
         private static Tensor op_helper<T>(string default_name, RefVariable x, T y)
         {
             var tensor1 = x.value();
-            return with(ops.name_scope(null, default_name, new { tensor1, y }), scope => {
+            return tf_with(ops.name_scope(null, default_name, new { tensor1, y }), scope => {
                 var tensor2 = ops.convert_to_tensor(y, tensor1.dtype.as_base_dtype(), "y");
                 return gen_math_ops.add(tensor1, tensor2, scope);
             });

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -134,7 +134,7 @@ namespace Tensorflow
 
             ops.init_scope();
             var values = init_from_fn ? new object[0] : new object[] { initial_value };
-            with(ops.name_scope(name, "Variable", values), scope =>
+            tf_with(ops.name_scope(name, "Variable", values), scope =>
             {
                 name = scope;
                 if (init_from_fn)
@@ -148,7 +148,7 @@ namespace Tensorflow
                         List = new AttrValue.Types.ListValue()
                     };
                     attr.List.S.Add(ByteString.CopyFromUtf8($"loc:{true_name}"));
-                    with(ops.name_scope("Initializer"), scope2 =>
+                    tf_with(ops.name_scope("Initializer"), scope2 =>
                     {
                         _initial_value = (initial_value as Func<Tensor>)();
                         _initial_value = ops.convert_to_tensor(_initial_value, name: "initial_value", dtype: dtype);

src/TensorFlowNET.Core/Variables/VariableScope.cs

@@ -56,7 +56,7 @@ namespace Tensorflow
             VariableAggregation aggregation= VariableAggregation.None)
         {
             string full_name = !string.IsNullOrEmpty(this.name) ? this.name + "/" + name : name;
-            return with(ops.name_scope(null), scope =>
+            return tf_with(ops.name_scope(null), scope =>
             {
                 if (dtype == TF_DataType.DtInvalid)
                     dtype = _dtype;

src/TensorFlowNET.Core/ops.py.cs

@@ -295,7 +295,7 @@ namespace Tensorflow
             // inner_device_stack = default_graph._device_function_stack
             // var outer_context = default_graph.as_default;
 
-            with(ops.control_dependencies(null), delegate
+            tf_with(ops.control_dependencies(null), delegate
             {
                 var outer_graph = get_default_graph();
                 // outer_device_stack = None

test/TensorFlowNET.Examples.FSharp/FunctionApproximation.fs

@@ -77,7 +77,7 @@ let run()=
     let init = tf.global_variables_initializer()
     
     
-    Tensorflow.Python.``with``(tf.Session(), fun (sess:Session) ->
+    Tensorflow.Python.``tf_with``(tf.Session(), fun (sess:Session) ->
         sess.run(init)  |> ignore  
         // Loop over epochs
         for epoch in [0..training_epochs] do

test/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs

@@ -52,10 +52,10 @@ namespace TensorFlowNET.Examples
         {
             PrepareData();
             var graph = ImportGraph();
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 Train(sess);
-            });
+            }
 
             return accuray_test > 0.70;
         }

test/TensorFlowNET.Examples/BasicModels/LinearRegression.cs

@@ -71,7 +71,7 @@ namespace TensorFlowNET.Examples
             var init = tf.global_variables_initializer();
 
             // Start training
-            return with(tf.Session(), sess => 
+            using (var sess = tf.Session())
             {
                 // Run the initializer
                 sess.run(init);
@@ -114,7 +114,7 @@ namespace TensorFlowNET.Examples
                 Console.WriteLine($"Absolute mean square loss difference: {diff}");
 
                 return diff < 0.01;
-            });
+            }
         }
 
         public void PrepareData()

test/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs

@@ -73,7 +73,7 @@ namespace TensorFlowNET.Examples
 
             var sw = new Stopwatch();
 
-            return with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 // Run the initializer
                 sess.run(init);
@@ -119,7 +119,7 @@ namespace TensorFlowNET.Examples
                 print($"Accuracy: {acc.ToString("F4")}");
 
                 return acc > 0.9;
-            });
+            }
         }
 
         public void PrepareData()

test/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs

@@ -48,14 +48,14 @@ namespace TensorFlowNET.Examples
             float y_max = X.amax(0).Data<float>(1) + 0.5f;
 
             var (xx, yy) = np.meshgrid(np.linspace(x_min, x_max, 30), np.linspace(y_min, y_max, 30));
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 //var samples = np.vstack<float>(xx.ravel(), yy.ravel());
                 //samples = np.transpose(samples);
                 var array = np.Load<double[,]>(Path.Join("nb", "nb_example.npy"));
                 var samples = np.array(array).astype(np.float32);
                 var Z = sess.run(predict(samples));
-            });
+            }
 
             return true;
         }

test/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs

@@ -54,7 +54,7 @@ namespace TensorFlowNET.Examples
             float accuracy = 0f;
             // Initialize the variables (i.e. assign their default value)
             var init = tf.global_variables_initializer();
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 // Run the initializer
                 sess.run(init);
@@ -77,7 +77,7 @@ namespace TensorFlowNET.Examples
                 }
 
                 print($"Accuracy: {accuracy}");
-            });
+            }
 
             return accuracy > 0.8;
         }

test/TensorFlowNET.Examples/BasicModels/NeuralNetXor.cs

@@ -90,7 +90,7 @@ namespace TensorFlowNET.Examples
             var init = tf.global_variables_initializer();
             float loss_value = 0;
             // Start tf session
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 sess.run(init);
                 var step = 0;
@@ -110,7 +110,7 @@ namespace TensorFlowNET.Examples
                         Console.WriteLine($"Step {step} loss: {loss_value}");
                 }
                 Console.WriteLine($"Final loss: {loss_value}");
-            });
+            }
 
             return loss_value;
         }
@@ -128,7 +128,7 @@ namespace TensorFlowNET.Examples
 
             float loss_value = 0;
             // Start tf session
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 sess.run(init);
                 var step = 0;
@@ -143,7 +143,7 @@ namespace TensorFlowNET.Examples
                         Console.WriteLine($"Step {step} loss: {loss_value}");
                 }
                 Console.WriteLine($"Final loss: {loss_value}");
-            });
+            }
 
             return loss_value;
         }

test/TensorFlowNET.Examples/BasicOperations.cs

@@ -134,7 +134,7 @@ namespace TensorFlowNET.Examples
                     3, 3, 2));
             var batchMul = tf.batch_matmul(firstTensor, secondTensor);
             var checkTensor = np.array<float>(0, 6, 0, 15, 0, 24, 3, 1, 6, 4, 9, 7, 6, 0, 15, 0, 24, 0);
-            return with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 var result = sess.run(batchMul);
                 Console.WriteLine(result.ToString());
@@ -152,7 +152,7 @@ namespace TensorFlowNET.Examples
                 //         [24,  0]]])
                 return np.reshape(result, 18)
                     .array_equal(checkTensor);
-            });
+            }
         }
 
         public void PrepareData()

test/TensorFlowNET.Examples/HelloWorld.cs

@@ -25,13 +25,13 @@ namespace TensorFlowNET.Examples
             var hello = tf.constant(str);
 
             // Start tf session
-            return with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 // Run the op
                 var result = sess.run(hello);
                 Console.WriteLine(result.ToString());
                 return result.ToString().Equals(str);
-            });
+            }
         }
 
         public void PrepareData()

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs

@@ -79,11 +79,11 @@ namespace TensorFlowNET.Examples
             PrepareData();
             BuildGraph();
 
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 Train(sess);
                 Test(sess);
-            });
+            }
 
             return loss_test < 0.05 && accuracy_test > 0.98;
         }
@@ -92,7 +92,7 @@ namespace TensorFlowNET.Examples
         {
             var graph = new Graph().as_default();
 
-            with(tf.name_scope("Input"), delegate
+            tf_with(tf.name_scope("Input"), delegate
             {
                 // Placeholders for inputs (x) and outputs(y)
                 x = tf.placeholder(tf.float32, shape: (-1, img_h, img_w, n_channels), name: "X");
@@ -107,25 +107,25 @@ namespace TensorFlowNET.Examples
             var fc1 = fc_layer(layer_flat, h1, "FC1", use_relu: true);
             var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false);
 
-            with(tf.variable_scope("Train"), delegate
+            tf_with(tf.variable_scope("Train"), delegate
             {
-                with(tf.variable_scope("Loss"), delegate
+                tf_with(tf.variable_scope("Loss"), delegate
                 {
                     loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits), name: "loss");
                 });
 
-                with(tf.variable_scope("Optimizer"), delegate
+                tf_with(tf.variable_scope("Optimizer"), delegate
                 {
                     optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss);
                 });
 
-                with(tf.variable_scope("Accuracy"), delegate
+                tf_with(tf.variable_scope("Accuracy"), delegate
                 {
                     var correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name: "correct_pred");
                     accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy");
                 });
 
-                with(tf.variable_scope("Prediction"), delegate
+                tf_with(tf.variable_scope("Prediction"), delegate
                 {
                     cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions");
                 });
@@ -204,7 +204,7 @@ namespace TensorFlowNET.Examples
         /// <returns>The output array</returns>
         private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride, string name)
         {
-            return with(tf.variable_scope(name), delegate {
+            return tf_with(tf.variable_scope(name), delegate {
 
                 var num_in_channel = x.shape[x.NDims - 1];
                 var shape = new[] { filter_size, filter_size, num_in_channel, num_filters };
@@ -244,7 +244,7 @@ namespace TensorFlowNET.Examples
         /// <returns>flattened array</returns>
         private Tensor flatten_layer(Tensor layer)
         {
-            return with(tf.variable_scope("Flatten_layer"), delegate
+            return tf_with(tf.variable_scope("Flatten_layer"), delegate
             {
                 var layer_shape = layer.TensorShape;
                 var num_features = layer_shape[new Slice(1, 4)].Size;
@@ -293,7 +293,7 @@ namespace TensorFlowNET.Examples
         /// <returns>The output array</returns>
         private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true)
         {
-            return with(tf.variable_scope(name), delegate
+            return tf_with(tf.variable_scope(name), delegate
             {
                 var in_dim = x.shape[1];
 

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs

@@ -59,11 +59,11 @@ namespace TensorFlowNET.Examples
             PrepareData();
             BuildGraph();
 
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 Train(sess);
                 Test(sess);
-            });
+            };
 
             return loss_test < 0.09 && accuracy_test > 0.95;
         }

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs

@@ -64,11 +64,11 @@ namespace TensorFlowNET.Examples
             PrepareData();
             BuildGraph();
 
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 Train(sess);
                 Test(sess);
-            });
+            }
 
             return loss_test < 0.09 && accuracy_test > 0.95;
         }

test/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs

@@ -32,11 +32,11 @@ namespace TensorFlowNET.Examples
 
             Tensor output = graph.OperationByName("SemanticPredictions");
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 // Runs inference on a single image.
                 sess.run(output, new FeedItem(output, "[np.asarray(resized_image)]"));
-            });
+            }
 
             return false;
         }

test/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs

@@ -45,7 +45,7 @@ namespace TensorFlowNET.Examples
             var result_labels = new List<string>();
             var sw = new Stopwatch();
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 foreach (var nd in file_ndarrays)
                 {
@@ -58,7 +58,7 @@ namespace TensorFlowNET.Examples
                     Console.WriteLine($"{labels[idx]} {results[idx]} in {sw.ElapsedMilliseconds}ms", Color.Tan);
                     result_labels.Add(labels[idx]);
                 }
-            });
+            }
             
             return result_labels.Contains("military uniform");
         }
@@ -69,19 +69,19 @@ namespace TensorFlowNET.Examples
                                 int input_mean = 117,
                                 int input_std = 1)
         {
-            return with(tf.Graph().as_default(), graph =>
-            {
-                var file_reader = tf.read_file(file_name, "file_reader");
-                var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
-                var cast = tf.cast(decodeJpeg, tf.float32);
-                var dims_expander = tf.expand_dims(cast, 0);
-                var resize = tf.constant(new int[] { input_height, input_width });
-                var bilinear = tf.image.resize_bilinear(dims_expander, resize);
-                var sub = tf.subtract(bilinear, new float[] { input_mean });
-                var normalized = tf.divide(sub, new float[] { input_std });
-
-                return with(tf.Session(graph), sess => sess.run(normalized));
-            });
+            var graph = tf.Graph().as_default();
+
+            var file_reader = tf.read_file(file_name, "file_reader");
+            var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
+            var cast = tf.cast(decodeJpeg, tf.float32);
+            var dims_expander = tf.expand_dims(cast, 0);
+            var resize = tf.constant(new int[] { input_height, input_width });
+            var bilinear = tf.image.resize_bilinear(dims_expander, resize);
+            var sub = tf.subtract(bilinear, new float[] { input_mean });
+            var normalized = tf.divide(sub, new float[] { input_std });
+
+            using (var sess = tf.Session(graph))
+                return sess.run(normalized);
         }
 
         public void PrepareData()

test/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs

@@ -45,9 +45,12 @@ namespace TensorFlowNET.Examples
             var input_operation = graph.get_operation_by_name(input_name);
             var output_operation = graph.get_operation_by_name(output_name);
 
-            var results = with(tf.Session(graph),
-                sess => sess.run(output_operation.outputs[0],
-                    new FeedItem(input_operation.outputs[0], nd)));
+            NDArray results;
+            using (var sess = tf.Session(graph))
+            {
+                results = sess.run(output_operation.outputs[0],
+                    new FeedItem(input_operation.outputs[0], nd));
+            }
 
             results = np.squeeze(results);
 
@@ -69,19 +72,19 @@ namespace TensorFlowNET.Examples
                                 int input_mean = 0,
                                 int input_std = 255)
         {
-            return with(tf.Graph().as_default(), graph =>
-            {
-                var file_reader = tf.read_file(file_name, "file_reader");
-                var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
-                var caster = tf.cast(image_reader, tf.float32);
-                var dims_expander = tf.expand_dims(caster, 0);
-                var resize = tf.constant(new int[] { input_height, input_width });
-                var bilinear = tf.image.resize_bilinear(dims_expander, resize);
-                var sub = tf.subtract(bilinear, new float[] { input_mean });
-                var normalized = tf.divide(sub, new float[] { input_std });
-
-                return with(tf.Session(graph), sess => sess.run(normalized));
-            });
+            var graph = tf.Graph().as_default();
+
+            var file_reader = tf.read_file(file_name, "file_reader");
+            var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
+            var caster = tf.cast(image_reader, tf.float32);
+            var dims_expander = tf.expand_dims(caster, 0);
+            var resize = tf.constant(new int[] { input_height, input_width });
+            var bilinear = tf.image.resize_bilinear(dims_expander, resize);
+            var sub = tf.subtract(bilinear, new float[] { input_mean });
+            var normalized = tf.divide(sub, new float[] { input_std });
+
+            using (var sess = tf.Session(graph))
+                return sess.run(normalized);
         }
 
         public void PrepareData()

test/TensorFlowNET.Examples/ImageProcessing/ObjectDetection.cs

@@ -51,7 +51,8 @@ namespace TensorFlowNET.Examples
 
             var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
 
-            with(tf.Session(graph), sess => Predict(sess));
+            using (var sess = tf.Session(graph))
+                Predict(sess);
 
             return true;
         }
@@ -101,14 +102,15 @@ namespace TensorFlowNET.Examples
 
         private NDArray ReadTensorFromImageFile(string file_name)
         {
-            return with(tf.Graph().as_default(), graph =>
-            {
-                var file_reader = tf.read_file(file_name, "file_reader");
-                var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
-                var casted = tf.cast(decodeJpeg, TF_DataType.TF_UINT8);
-                var dims_expander = tf.expand_dims(casted, 0);
-                return with(tf.Session(graph), sess => sess.run(dims_expander));
-            });
+            var graph = tf.Graph().as_default();
+
+            var file_reader = tf.read_file(file_name, "file_reader");
+            var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
+            var casted = tf.cast(decodeJpeg, TF_DataType.TF_UINT8);
+            var dims_expander = tf.expand_dims(casted, 0);
+
+            using (var sess = tf.Session(graph))
+                return sess.run(dims_expander);
         }
 
         private void buildOutputImage(NDArray[] resultArr)

test/TensorFlowNET.Examples/ImageProcessing/RetrainImageClassifier.cs

@@ -92,10 +92,10 @@ namespace TensorFlowNET.Examples
 
             var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 Train(sess);
-            });
+            }
 
             return test_accuracy > 0.75f;
         }
@@ -141,20 +141,18 @@ namespace TensorFlowNET.Examples
             Tensor evaluation_step = null;
             Tensor prediction = null;
 
-            with(eval_graph.as_default(), graph =>
-            {
-                // Add the new layer for exporting.
-                var (_, _, bottleneck_input, ground_truth_input, final_tensor) =
-                    add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
-                        wants_quantization, is_training: false);
+            var graph = eval_graph.as_default();
+            // Add the new layer for exporting.
+            var (_, _, bottleneck_input, ground_truth_input, final_tensor) =
+                add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
+                    wants_quantization, is_training: false);
 
-                // Now we need to restore the values from the training graph to the eval
-                // graph.
-                tf.train.Saver().restore(eval_sess, CHECKPOINT_NAME);
+            // Now we need to restore the values from the training graph to the eval
+            // graph.
+            tf.train.Saver().restore(eval_sess, CHECKPOINT_NAME);
 
-                (evaluation_step, prediction) = add_evaluation_step(final_tensor,
-                                                      ground_truth_input);
-            });
+            (evaluation_step, prediction) = add_evaluation_step(final_tensor,
+                                                    ground_truth_input);
 
             return (eval_sess, resized_input_tensor, bottleneck_input, ground_truth_input,
                 evaluation_step, prediction);
@@ -180,7 +178,7 @@ namespace TensorFlowNET.Examples
             Tensor bottleneck_tensor, bool quantize_layer, bool is_training)
         {
             var (batch_size, bottleneck_tensor_size) = (bottleneck_tensor.TensorShape.Dimensions[0], bottleneck_tensor.TensorShape.Dimensions[1]);
-            with(tf.name_scope("input"), scope =>
+            tf_with(tf.name_scope("input"), scope =>
             {
                 bottleneck_input = tf.placeholder_with_default(
                     bottleneck_tensor,
@@ -193,10 +191,10 @@ namespace TensorFlowNET.Examples
             // Organizing the following ops so they are easier to see in TensorBoard.
             string layer_name = "final_retrain_ops";
             Tensor logits = null;
-            with(tf.name_scope(layer_name), scope =>
+            tf_with(tf.name_scope(layer_name), scope =>
             {
                 RefVariable layer_weights = null;
-                with(tf.name_scope("weights"), delegate
+                tf_with(tf.name_scope("weights"), delegate
                 {
                     var initial_value = tf.truncated_normal(new int[] { bottleneck_tensor_size, class_count }, stddev: 0.001f);
                     layer_weights = tf.Variable(initial_value, name: "final_weights");
@@ -204,13 +202,13 @@ namespace TensorFlowNET.Examples
                 });
 
                 RefVariable layer_biases = null;
-                with(tf.name_scope("biases"), delegate
+                tf_with(tf.name_scope("biases"), delegate
                 {
                     layer_biases = tf.Variable(tf.zeros(class_count), name: "final_biases");
                     variable_summaries(layer_biases);
                 });
 
-                with(tf.name_scope("Wx_plus_b"), delegate
+                tf_with(tf.name_scope("Wx_plus_b"), delegate
                 {
                     logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases;
                     tf.summary.histogram("pre_activations", logits);
@@ -239,7 +237,7 @@ namespace TensorFlowNET.Examples
                 return (null, null, bottleneck_input, ground_truth_input, final_tensor);
 
             Tensor cross_entropy_mean = null;
-            with(tf.name_scope("cross_entropy"), delegate
+            tf_with(tf.name_scope("cross_entropy"), delegate
             {
                 cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(
                     labels: ground_truth_input, logits: logits);
@@ -247,7 +245,7 @@ namespace TensorFlowNET.Examples
 
             tf.summary.scalar("cross_entropy", cross_entropy_mean);
 
-            with(tf.name_scope("train"), delegate
+            tf_with(tf.name_scope("train"), delegate
             {
                 var optimizer = tf.train.GradientDescentOptimizer(learning_rate);
                 train_step = optimizer.minimize(cross_entropy_mean);
@@ -259,12 +257,12 @@ namespace TensorFlowNET.Examples
 
         private void variable_summaries(RefVariable var)
         {
-            with(tf.name_scope("summaries"), delegate
+            tf_with(tf.name_scope("summaries"), delegate
             {
                 var mean = tf.reduce_mean(var);
                 tf.summary.scalar("mean", mean);
                 Tensor stddev = null;
-                with(tf.name_scope("stddev"), delegate
+                tf_with(tf.name_scope("stddev"), delegate
                 {
                     stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)));
                 });
@@ -279,7 +277,7 @@ namespace TensorFlowNET.Examples
         {
             var (height, width) = (299, 299);
 
-            return with(tf.Graph().as_default(), graph =>
+            return tf_with(tf.Graph().as_default(), graph =>
             {
                 tf.train.import_meta_graph("graph/InceptionV3.meta");
                 Tensor resized_input_tensor = graph.OperationByName("Placeholder"); //tf.placeholder(tf.float32, new TensorShape(-1, height, width, 3));
@@ -350,15 +348,15 @@ namespace TensorFlowNET.Examples
         {
             Tensor evaluation_step = null, correct_prediction = null, prediction = null;
 
-            with(tf.name_scope("accuracy"), scope =>
+            tf_with(tf.name_scope("accuracy"), scope =>
             {
-                with(tf.name_scope("correct_prediction"), delegate
+                tf_with(tf.name_scope("correct_prediction"), delegate
                 {
                     prediction = tf.argmax(result_tensor, 1);
                     correct_prediction = tf.equal(prediction, ground_truth_tensor);
                 });
 
-                with(tf.name_scope("accuracy"), delegate
+                tf_with(tf.name_scope("accuracy"), delegate
                 {
                     evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32));
                 });
@@ -596,7 +594,7 @@ namespace TensorFlowNET.Examples
                 create_module_graph();
 
             // Add the new layer that we'll be training.
-            with(graph.as_default(), delegate
+            tf_with(graph.as_default(), delegate
             {
                 (train_step, cross_entropy, bottleneck_input,
                  ground_truth_input, final_tensor) = add_final_retrain_ops(
@@ -745,13 +743,13 @@ namespace TensorFlowNET.Examples
             Tensor input = graph.OperationByName("Placeholder");
             Tensor output = graph.OperationByName("final_result");
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 var result = sess.run(output, new FeedItem(input, fileBytes));
                 var prob = np.squeeze(result);
                 var idx = np.argmax(prob);
                 print($"Prediction result: [{labels[idx]} {prob[idx][0]}] for {img_path}.");
-            });
+            }
         }
 
         private NDArray ReadTensorFromImageFile(string file_name,
@@ -760,19 +758,19 @@ namespace TensorFlowNET.Examples
                         int input_mean = 0,
                         int input_std = 255)
         {
-            return with(tf.Graph().as_default(), graph =>
-            {
-                var file_reader = tf.read_file(file_name, "file_reader");
-                var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
-                var caster = tf.cast(image_reader, tf.float32);
-                var dims_expander = tf.expand_dims(caster, 0);
-                var resize = tf.constant(new int[] { input_height, input_width });
-                var bilinear = tf.image.resize_bilinear(dims_expander, resize);
-                var sub = tf.subtract(bilinear, new float[] { input_mean });
-                var normalized = tf.divide(sub, new float[] { input_std });
-
-                return with(tf.Session(graph), sess => sess.run(normalized));
-            });
+            var graph = tf.Graph().as_default();
+
+            var file_reader = tf.read_file(file_name, "file_reader");
+            var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
+            var caster = tf.cast(image_reader, tf.float32);
+            var dims_expander = tf.expand_dims(caster, 0);
+            var resize = tf.constant(new int[] { input_height, input_width });
+            var bilinear = tf.image.resize_bilinear(dims_expander, resize);
+            var sub = tf.subtract(bilinear, new float[] { input_mean });
+            var normalized = tf.divide(sub, new float[] { input_std });
+
+            using (var sess = tf.Session(graph))
+                return sess.run(normalized);
         }
 
         public void Test(Session sess_)
@@ -783,7 +781,7 @@ namespace TensorFlowNET.Examples
             var graph = Graph.ImportFromPB(output_graph);
             var (jpeg_data_tensor, decoded_image_tensor) = add_jpeg_decoding();
 
-            with(tf.Session(graph), sess =>
+            tf_with(tf.Session(graph), sess =>
             {
                 (test_accuracy, predictions) = run_final_eval(sess, null, class_count, image_lists,
                                jpeg_data_tensor, decoded_image_tensor, resized_image_tensor,

test/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs

@@ -64,7 +64,9 @@ namespace TensorFlowNET.Examples
         {
             PrepareData();
             var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
-            with(tf.Session(graph), sess => Train(sess));
+
+            using (var sess = tf.Session(graph))
+                Train(sess);
 
             return max_accuracy > 0.9;
         }

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

@@ -63,7 +63,7 @@ namespace TensorFlowNET.Examples.Text.NER
 
             var init = tf.global_variables_initializer();
 
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 sess.run(init);
 
@@ -73,7 +73,7 @@ namespace TensorFlowNET.Examples.Text.NER
                     loss_value = run_epoch(sess, train, dev, epoch);
                     print($"train loss: {loss_value}");
                 }
-            });
+            }
 
             return loss_value < 0.1;
         }

test/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs

@@ -66,7 +66,7 @@ namespace TensorFlowNET.Examples
             // Initialize the variables (i.e. assign their default value)
             var init = tf.global_variables_initializer();
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 // Run the initializer
                 sess.run(init);
@@ -112,7 +112,7 @@ namespace TensorFlowNET.Examples
                         }
                     }
                 }
-            });
+            }
 
             return average_loss < 100;
         }

test/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs

@@ -26,7 +26,7 @@ namespace TensorFlowNET.Examples.Text
             Tensor conv3 = null, conv4 = null, conv5 = null, conv6 = null;
             Tensor h_pool = null;
 
-            with(tf.name_scope("conv-maxpool-1"), delegate
+            tf_with(tf.name_scope("conv-maxpool-1"), delegate
             {
                 var conv1 = tf.layers.conv2d(x_expanded,
                     filters: num_filters,
@@ -40,7 +40,7 @@ namespace TensorFlowNET.Examples.Text
                 pool1 = tf.transpose(pool1, new[] { 0, 1, 3, 2 });
             });
 
-            with(tf.name_scope("conv-maxpool-2"), delegate
+            tf_with(tf.name_scope("conv-maxpool-2"), delegate
             {
                 var conv2 = tf.layers.conv2d(pool1,
                     filters: num_filters,
@@ -54,7 +54,7 @@ namespace TensorFlowNET.Examples.Text
                 pool2 = tf.transpose(pool2, new[] { 0, 1, 3, 2 });
             });
 
-            with(tf.name_scope("conv-3"), delegate
+            tf_with(tf.name_scope("conv-3"), delegate
             {
                 conv3 = tf.layers.conv2d(pool2,
                     filters: num_filters,
@@ -64,7 +64,7 @@ namespace TensorFlowNET.Examples.Text
                 conv3 = tf.transpose(conv3, new[] { 0, 1, 3, 2 });
             });
 
-            with(tf.name_scope("conv-4"), delegate
+            tf_with(tf.name_scope("conv-4"), delegate
             {
                 conv4 = tf.layers.conv2d(conv3,
                     filters: num_filters,
@@ -74,7 +74,7 @@ namespace TensorFlowNET.Examples.Text
                 conv4 = tf.transpose(conv4, new[] { 0, 1, 3, 2 });
             });
 
-            with(tf.name_scope("conv-5"), delegate
+            tf_with(tf.name_scope("conv-5"), delegate
             {
                 conv5 = tf.layers.conv2d(conv4,
                     filters: num_filters,
@@ -84,7 +84,7 @@ namespace TensorFlowNET.Examples.Text
                 conv5 = tf.transpose(conv5, new[] { 0, 1, 3, 2 });
             });
 
-            with(tf.name_scope("conv-maxpool-6"), delegate
+            tf_with(tf.name_scope("conv-maxpool-6"), delegate
             {
                 conv6 = tf.layers.conv2d(conv5,
                     filters: num_filters,
@@ -105,7 +105,7 @@ namespace TensorFlowNET.Examples.Text
             Tensor logits = null;
             Tensor predictions = null;
 
-            with(tf.name_scope("fc-1"), delegate
+            tf_with(tf.name_scope("fc-1"), delegate
             {
                 fc1_out = tf.layers.dense(h_pool,
                     1024,
@@ -113,7 +113,7 @@ namespace TensorFlowNET.Examples.Text
                     kernel_initializer: kernel_initializer);
             });
 
-            with(tf.name_scope("fc-2"), delegate
+            tf_with(tf.name_scope("fc-2"), delegate
             {
                 fc2_out = tf.layers.dense(fc1_out,
                     1024,
@@ -121,7 +121,7 @@ namespace TensorFlowNET.Examples.Text
                     kernel_initializer: kernel_initializer);
             });
 
-            with(tf.name_scope("fc-3"), delegate
+            tf_with(tf.name_scope("fc-3"), delegate
             {
                 logits = tf.layers.dense(fc2_out,
                     num_class,
@@ -129,7 +129,7 @@ namespace TensorFlowNET.Examples.Text
                 predictions = tf.argmax(logits, -1, output_type: tf.int32);
             });
 
-            with(tf.name_scope("loss"), delegate
+            tf_with(tf.name_scope("loss"), delegate
             {
                 var y_one_hot = tf.one_hot(y, num_class);
                 var loss = tf.reduce_mean(
@@ -137,7 +137,7 @@ namespace TensorFlowNET.Examples.Text
                 var optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step: global_step);
             });
 
-            with(tf.name_scope("accuracy"), delegate
+            tf_with(tf.name_scope("accuracy"), delegate
             {
                 var correct_predictions = tf.equal(predictions, y);
                 var accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32), name: "accuracy");

test/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs

@@ -41,7 +41,7 @@ namespace TensorFlowNET.Examples.Text
             global_step = tf.Variable(0, trainable: false);
 
             // Embedding Layer
-            with(tf.name_scope("embedding"), delegate
+            tf_with(tf.name_scope("embedding"), delegate
             {
                 var init_embeddings = tf.random_uniform(new int[] { alphabet_size, embedding_size }, -1.0f, 1.0f);
                 embeddings = tf.get_variable("embeddings", initializer: init_embeddings);
@@ -59,7 +59,7 @@ namespace TensorFlowNET.Examples.Text
             Tensor fc2_out = null;
 
             // First Convolution Layer
-            with(tf.variable_scope("conv-0"), delegate
+            tf_with(tf.variable_scope("conv-0"), delegate
             {
                 conv0 = tf.layers.conv2d(x_expanded,
                     filters: num_filters[0],
@@ -70,25 +70,25 @@ namespace TensorFlowNET.Examples.Text
                 conv0 = tf.transpose(conv0, new int[] { 0, 1, 3, 2 });
             });
 
-            with(tf.name_scope("conv-block-1"), delegate {
+            tf_with(tf.name_scope("conv-block-1"), delegate {
                 conv1 = conv_block(conv0, 1);
             });
 
-            with(tf.name_scope("conv-block-2"), delegate {
+            tf_with(tf.name_scope("conv-block-2"), delegate {
                 conv2 = conv_block(conv1, 2);
             });
 
-            with(tf.name_scope("conv-block-3"), delegate {
+            tf_with(tf.name_scope("conv-block-3"), delegate {
                 conv3 = conv_block(conv2, 3);
             });
 
-            with(tf.name_scope("conv-block-4"), delegate
+            tf_with(tf.name_scope("conv-block-4"), delegate
             {
                 conv4 = conv_block(conv3, 4, max_pool: false);
             });
 
             // ============= k-max Pooling =============
-            with(tf.name_scope("k-max-pooling"), delegate
+            tf_with(tf.name_scope("k-max-pooling"), delegate
             {
                 var h = tf.transpose(tf.squeeze(conv4, new int[] { -1 }), new int[] { 0, 2, 1 });
                 var top_k = tf.nn.top_k(h, k: 8, sorted: false)[0];
@@ -96,30 +96,30 @@ namespace TensorFlowNET.Examples.Text
             });
 
             // ============= Fully Connected Layers =============
-            with(tf.name_scope("fc-1"), scope =>
+            tf_with(tf.name_scope("fc-1"), scope =>
             {
                 fc1_out = tf.layers.dense(h_flat, 2048, activation: tf.nn.relu(), kernel_initializer: fc_initializer);
             });
 
-            with(tf.name_scope("fc-2"), scope =>
+            tf_with(tf.name_scope("fc-2"), scope =>
             {
                 fc2_out = tf.layers.dense(fc1_out, 2048, activation: tf.nn.relu(), kernel_initializer: fc_initializer);
             });
 
-            with(tf.name_scope("fc-3"), scope =>
+            tf_with(tf.name_scope("fc-3"), scope =>
             {
                 logits = tf.layers.dense(fc2_out, num_class, activation: null, kernel_initializer: fc_initializer);
                 predictions = tf.argmax(logits, -1, output_type: tf.int32);
             });
 
             // ============= Loss and Accuracy =============
-            with(tf.name_scope("loss"), delegate
+            tf_with(tf.name_scope("loss"), delegate
             {
                 var y_one_hot = tf.one_hot(y, num_class);
                 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits: logits, labels: y_one_hot));
 
                 var update_ops = tf.get_collection(ops.GraphKeys.UPDATE_OPS) as List<object>;
-                with(tf.control_dependencies(update_ops.Select(x => (Operation)x).ToArray()), delegate
+                tf_with(tf.control_dependencies(update_ops.Select(x => (Operation)x).ToArray()), delegate
                 {
                     var adam = tf.train.AdamOptimizer(learning_rate);
                     adam.minimize(loss, global_step: global_step);
@@ -129,13 +129,13 @@ namespace TensorFlowNET.Examples.Text
 
         private Tensor conv_block(Tensor input, int i, bool max_pool = true)
         {
-            return with(tf.variable_scope($"conv-block-{i}"), delegate
+            return tf_with(tf.variable_scope($"conv-block-{i}"), delegate
             {
                 Tensor conv = null;
                 // Two "conv-batch_norm-relu" layers.
                 foreach (var j in Enumerable.Range(0, 2))
                 {
-                    with(tf.variable_scope($"conv-{j}"), delegate
+                    tf_with(tf.variable_scope($"conv-{j}"), delegate
                     {
                         // convolution
                         conv = tf.layers.conv2d(

test/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs

@@ -36,7 +36,7 @@ namespace TensorFlowNET.Examples.Text
             var keep_prob = tf.where(is_training, 0.5f, 1.0f);
             Tensor x_emb = null;
 
-            with(tf.name_scope("embedding"), scope =>
+            tf_with(tf.name_scope("embedding"), scope =>
             {
                 var init_embeddings = tf.random_uniform(new int[] { vocabulary_size, embedding_size });
                 var embeddings = tf.get_variable("embeddings", initializer: init_embeddings);
@@ -68,20 +68,20 @@ namespace TensorFlowNET.Examples.Text
             var h_pool = tf.concat(pooled_outputs, 3);
             var h_pool_flat = tf.reshape(h_pool, new TensorShape(-1, num_filters * filter_sizes.Rank));
             Tensor h_drop = null;
-            with(tf.name_scope("dropout"), delegate
+            tf_with(tf.name_scope("dropout"), delegate
             {
                 h_drop = tf.nn.dropout(h_pool_flat, keep_prob);
             });
 
             Tensor logits = null;
             Tensor predictions = null;
-            with(tf.name_scope("output"), delegate
+            tf_with(tf.name_scope("output"), delegate
             {
                 logits = tf.layers.dense(h_drop, num_class);
                 predictions = tf.argmax(logits, -1, output_type: tf.int32);
             });
 
-            with(tf.name_scope("loss"), delegate
+            tf_with(tf.name_scope("loss"), delegate
             {
                 var sscel = tf.nn.sparse_softmax_cross_entropy_with_logits(logits: logits, labels: y);
                 var loss = tf.reduce_mean(sscel);
@@ -89,7 +89,7 @@ namespace TensorFlowNET.Examples.Text
                 var optimizer = adam.minimize(loss, global_step: global_step);
             });
 
-            with(tf.name_scope("accuracy"), delegate
+            tf_with(tf.name_scope("accuracy"), delegate
             {
                 var correct_predictions = tf.equal(predictions, y);
                 var accuracy = tf.reduce_mean(tf.cast(correct_predictions, TF_DataType.TF_FLOAT), name: "accuracy");

test/TensorFlowNET.UnitTest/Basics/NegativeTests.cs

@@ -12,13 +12,13 @@ namespace TensorFlowNET.UnitTest.Basics
         {
             var x = tf.constant(new[,] { { 1, 2 } });
             var neg_x = tf.negative(x);
-            with(tf.Session(), session =>
+            using (var sess = tf.Session())
             {
-                var result = session.run(neg_x);
+                var result = sess.run(neg_x);
 
                 Assert.AreEqual(result[0][0], -1);
                 Assert.AreEqual(result[0][1], -2);
-            });
+            }
         }
     }
 }

test/TensorFlowNET.UnitTest/ConstantTest.cs

@@ -94,18 +94,18 @@ namespace TensorFlowNET.UnitTest
         {
             // small size
             var tensor = tf.zeros(new Shape(3, 2), TF_DataType.TF_INT32, "small");
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 var result = sess.run(tensor);
 
                 Assert.AreEqual(result.shape[0], 3);
                 Assert.AreEqual(result.shape[1], 2);
                 Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 0, 0, 0, 0 }, result.Data<int>()));
-            });
+            }
 
             // big size
             tensor = tf.zeros(new Shape(200, 100), TF_DataType.TF_INT32, "big");
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 var result = sess.run(tensor);
 
@@ -116,21 +116,21 @@ namespace TensorFlowNET.UnitTest
                 Assert.AreEqual(0, data[0]);
                 Assert.AreEqual(0, data[500]);
                 Assert.AreEqual(0, data[result.size - 1]);
-            });
+            }
         }
 
         [TestMethod]
         public void OnesConst()
         {
             var ones = tf.ones(new Shape(3, 2), TF_DataType.TF_DOUBLE, "ones");
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 var result = sess.run(ones);
 
                 Assert.AreEqual(result.shape[0], 3);
                 Assert.AreEqual(result.shape[1], 2);
                 Assert.IsTrue(new[] { 1, 1, 1, 1, 1, 1 }.SequenceEqual(result.Data<int>()));
-            });
+            }
         }
 
         [TestMethod]
@@ -138,14 +138,14 @@ namespace TensorFlowNET.UnitTest
         {
             var ones = tf.ones(new Shape(3, 2), TF_DataType.TF_DOUBLE, "ones");
             var halfes = ones * 0.5;
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 var result = sess.run(halfes);
 
                 Assert.AreEqual(result.shape[0], 3);
                 Assert.AreEqual(result.shape[1], 2);
                 Assert.IsTrue(new[] { .5, .5, .5, .5, .5, .5 }.SequenceEqual(result.Data<double>()));
-            });
+            }
         }
 
         [TestMethod]
@@ -158,7 +158,7 @@ namespace TensorFlowNET.UnitTest
             });
 
             var tensor = tf.constant(nd);
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 var result = sess.run(tensor);
                 var data = result.Data<int>();
@@ -166,7 +166,7 @@ namespace TensorFlowNET.UnitTest
                 Assert.AreEqual(result.shape[0], 2);
                 Assert.AreEqual(result.shape[1], 3);
                 Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 3, 1, 1, 2, 1, 3 }, data));
-            });
+            }
         }
 
         [TestMethod]

test/TensorFlowNET.UnitTest/GradientTest.cs

@@ -33,7 +33,8 @@ namespace TensorFlowNET.UnitTest
         public void Gradient2x()
         {
             var graph = tf.Graph().as_default();
-            with(tf.Session(graph), sess => {
+            using (var sess = tf.Session(graph))
+            {
                 var x = tf.constant(7.0f);
                 var y = x * x * tf.constant(0.1f);
 
@@ -42,14 +43,14 @@ namespace TensorFlowNET.UnitTest
 
                 float r = sess.run(grad[0]);
                 Assert.AreEqual(r, 1.4f);
-            });
+            }
         }
 
         [TestMethod]
         public void Gradient3x()
         {
             var graph = tf.Graph().as_default();
-            with(tf.Session(graph), sess => {
+            tf_with(tf.Session(graph), sess => {
                 var x = tf.constant(7.0f);
                 var y = x * x * x * tf.constant(0.1f);
 
@@ -91,14 +92,14 @@ namespace TensorFlowNET.UnitTest
 
             var g = tf.gradients(y, new Tensor[] { slice, slice });
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 var r = sess.run(slice);
 
                 Assert.IsTrue(Enumerable.SequenceEqual(r.shape, new[] { 2, 1, 2 }));
                 Assert.IsTrue(Enumerable.SequenceEqual(r[0].GetData<int>(), new[] { 11, 13 }));
                 Assert.IsTrue(Enumerable.SequenceEqual(r[1].GetData<int>(), new[] { 51, 53 }));
-            });
+            }
         }
     }
 }

test/TensorFlowNET.UnitTest/GraphTest.cs

@@ -419,7 +419,7 @@ namespace TensorFlowNET.UnitTest
         public void ImportGraphMeta()
         {
             var dir = "my-save-dir/";
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 var new_saver = tf.train.import_meta_graph(dir + "my-model-10000.meta");
                 new_saver.restore(sess, dir + "my-model-10000");
@@ -428,7 +428,7 @@ namespace TensorFlowNET.UnitTest
                 var logits = (tf.get_collection("logits") as List<ITensorOrOperation>)[0] as Tensor;
                 var loss = tf.losses.sparse_softmax_cross_entropy(labels: labels,
                                                 logits: logits);
-            });
+            }
         }
     }
 }

test/TensorFlowNET.UnitTest/NameScopeTest.cs

@@ -13,7 +13,7 @@ namespace TensorFlowNET.UnitTest
         [TestMethod]
         public void NestedNameScope()
         {
-            with(new ops.NameScope("scope1"), scope1 =>
+            tf_with(new ops.NameScope("scope1"), scope1 =>
             {
                 name = scope1;
                 Assert.AreEqual("scope1", g._name_stack);
@@ -22,7 +22,7 @@ namespace TensorFlowNET.UnitTest
                 var const1 = tf.constant(1.0);
                 Assert.AreEqual("scope1/Const:0", const1.name);
 
-                with(new ops.NameScope("scope2"), scope2 =>
+                tf_with(new ops.NameScope("scope2"), scope2 =>
                 {
                     name = scope2;
                     Assert.AreEqual("scope1/scope2", g._name_stack);

test/TensorFlowNET.UnitTest/PlaceholderTest.cs

@@ -13,12 +13,12 @@ namespace TensorFlowNET.UnitTest
             var x = tf.placeholder(tf.int32);
             var y = x * 3;
 
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 var result = sess.run(y, 
                     new FeedItem(x, 2));
                 Assert.AreEqual((int)result, 6);
-            });
+            }
         }
     }
 }

test/TensorFlowNET.UnitTest/PythonTest.cs

@@ -164,7 +164,7 @@ namespace TensorFlowNET.UnitTest
             //    return self._eval_helper(tensors)
             //  else:
             {
-                with(tf.Session(), s =>
+                using (var sess = tf.Session())
                 {
                     var ndarray=tensor.eval();
                     if (typeof(T) == typeof(double))
@@ -181,7 +181,8 @@ namespace TensorFlowNET.UnitTest
                     {
                         result = ndarray;
                     }
-                });
+                }
+
                 return (T)result;
             }
         }

test/TensorFlowNET.UnitTest/SessionTest.cs

@@ -82,11 +82,11 @@ namespace TensorFlowNET.UnitTest
             var a = constant_op.constant(np.array(3.0).reshape(1, 1));
             var b = constant_op.constant(np.array(2.0).reshape(1, 1));
             var c = math_ops.matmul(a, b, name: "matmul");
-            with(tf.Session(), delegate
+            using (var sess = tf.Session())
             {
                 var result = c.eval();
                 Assert.AreEqual(6, result.Data<double>()[0]);
-            });
+            }
         }
     }
 }

test/TensorFlowNET.UnitTest/TensorTest.cs

@@ -12,7 +12,7 @@ namespace TensorFlowNET.UnitTest
     [TestClass]
     public class TensorTest : CApiTest
     {
-        //[TestMethod]
+        [Ignore("Not for mult-thread")]
         public void TensorDeallocationThreadSafety()
         {
             var tensors = new Tensor[1000];

test/TensorFlowNET.UnitTest/TrainSaverTest.cs

@@ -17,10 +17,10 @@ namespace TensorFlowNET.UnitTest
 
         public void ImportGraph()
         {
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 var new_saver = tf.train.import_meta_graph("C:/tmp/my-model.meta");
-            });
+            }
 
             //tf.train.export_meta_graph(filename: "linear_regression.meta.bin");
             // import meta
@@ -42,7 +42,7 @@ namespace TensorFlowNET.UnitTest
 
         public void ImportSavedModel()
         {
-            with(Session.LoadFromSavedModel("mobilenet"), sess =>
+            tf_with(Session.LoadFromSavedModel("mobilenet"), sess =>
             {
                 
             });
@@ -63,14 +63,14 @@ namespace TensorFlowNET.UnitTest
             // Add ops to save and restore all the variables.
             var saver = tf.train.Saver();
 
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 sess.run(init_op);
 
                 // Save the variables to disk.
                 var save_path = saver.save(sess, "/tmp/model1.ckpt");
                 Console.WriteLine($"Model saved in path: {save_path}");
-            });
+            }
         }
 
         public void Save2()
@@ -87,7 +87,7 @@ namespace TensorFlowNET.UnitTest
             // Add ops to save and restore all the variables.
             var saver = tf.train.Saver();
 
-            with<Session>(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 sess.run(init_op);
                 // o some work with the model.
@@ -97,7 +97,7 @@ namespace TensorFlowNET.UnitTest
                 // Save the variables to disk.
                 var save_path = saver.save(sess, "/tmp/model2.ckpt");
                 Console.WriteLine($"Model saved in path: {save_path}");
-            });
+            }
         }
     }
 }

test/TensorFlowNET.UnitTest/VariableTest.cs

@@ -35,9 +35,9 @@ namespace TensorFlowNET.UnitTest
         public void VarCreation()
         {
             tf.Graph().as_default();
-            with(tf.variable_scope("foo"), delegate
+            tf_with(tf.variable_scope("foo"), delegate
             {
-                with(tf.variable_scope("bar"), delegate
+                tf_with(tf.variable_scope("bar"), delegate
                 {
                     var v = tf.get_variable("v", new TensorShape(1));
                     Assert.AreEqual(v.name, "foo/bar/v:0");
@@ -53,14 +53,14 @@ namespace TensorFlowNET.UnitTest
         {
             tf.Graph().as_default();
             variable_scope vs = null;
-            with(tf.variable_scope("foo"), v => vs = v);
+            tf_with(tf.variable_scope("foo"), v => vs = v);
 
             // Re-enter the variable scope.
-            with(tf.variable_scope(vs, auxiliary_name_scope: false), v =>
+            tf_with(tf.variable_scope(vs, auxiliary_name_scope: false), v =>
             {
                 var vs1 = (VariableScope)v;
                 // Restore the original name_scope.
-                with(tf.name_scope(vs1.original_name_scope), delegate
+                tf_with(tf.name_scope(vs1.original_name_scope), delegate
                 {
                     var v1 = tf.get_variable("v", new TensorShape(1));
                     Assert.AreEqual(v1.name, "foo/v:0");
@@ -89,21 +89,20 @@ namespace TensorFlowNET.UnitTest
         [TestMethod]
         public void Assign1()
         {
-            with(tf.Graph().as_default(), graph =>
-            {
-                var variable = tf.Variable(31, name: "tree");
-                var init = tf.global_variables_initializer();
+            var graph = tf.Graph().as_default();
 
-                var sess = tf.Session(graph);
-                sess.run(init);
+            var variable = tf.Variable(31, name: "tree");
+            var init = tf.global_variables_initializer();
 
-                var result = sess.run(variable);
-                Assert.IsTrue((int)result == 31);
+            var sess = tf.Session(graph);
+            sess.run(init);
 
-                var assign = variable.assign(12);
-                result = sess.run(assign);
-                Assert.IsTrue((int)result == 12);
-            });
+            var result = sess.run(variable);
+            Assert.IsTrue((int)result == 31);
+
+            var assign = variable.assign(12);
+            result = sess.run(assign);
+            Assert.IsTrue((int)result == 12);
         }
 
         [TestMethod]
@@ -115,12 +114,12 @@ namespace TensorFlowNET.UnitTest
             // Add an op to initialize the variables.
             var init_op = tf.global_variables_initializer();
 
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 sess.run(init_op);
                 // o some work with the model.
                 inc_v1.op.run();
-            });
+            }
         }
 
         /// <summary>

test/TensorFlowNET.UnitTest/control_flow_ops_test/CondTestCases.cs

@@ -16,7 +16,7 @@ namespace TensorFlowNET.UnitTest.control_flow_ops_test
         {
             var graph = tf.Graph().as_default();
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 var x = tf.constant(2, name: "x");
                 var y = tf.constant(5, name: "y");
@@ -27,7 +27,7 @@ namespace TensorFlowNET.UnitTest.control_flow_ops_test
 
                 int result = z.eval(sess);
                 assertEquals(result, 22);
-            });
+            }
         }
 
         [Ignore("need tesnroflow expose AddControlInput API")]
@@ -36,7 +36,7 @@ namespace TensorFlowNET.UnitTest.control_flow_ops_test
         {
             var graph = tf.Graph().as_default();
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 var x = tf.constant(2, name: "x");
                 var y = tf.constant(1, name: "y");
@@ -47,7 +47,7 @@ namespace TensorFlowNET.UnitTest.control_flow_ops_test
 
                 int result = z.eval(sess);
                 assertEquals(result, 11);
-            });
+            }
         }
 
         [Ignore("need tesnroflow expose AddControlInput API")]

test/TensorFlowNET.UnitTest/control_flow_ops_test/WhileContextTestCase.cs

@@ -11,7 +11,7 @@ namespace TensorFlowNET.UnitTest.control_flow_ops_test
         private void _testWhileContextHelper(int? maximum_iterations = null)
         {
             // TODO: implement missing code dependencies
-            with<Session>(this.cached_session(), sess =>
+            using (var sess = this.cached_session())
             {
                 var i = constant_op.constant(0, name: "i");
                 var c = new Func<Tensor, Tensor>(x => gen_math_ops.less(x, 10, name: "c"));
@@ -26,7 +26,7 @@ namespace TensorFlowNET.UnitTest.control_flow_ops_test
                             WhileContext.from_proto(
                                 control_flow_context.to_proto()).to_proto(), "");*/
                 }
-            });
+            }
         }
 
         [Ignore("TODO")]

test/TensorFlowNET.UnitTest/gradients_test/GradientsTest.cs

@@ -15,20 +15,18 @@ namespace TensorFlowNET.UnitTest.gradients_test
         [TestMethod]
         public void testGradients()
         {
-            with(tf.Graph().as_default(), g =>
-            {
-                var inp = tf.constant(1.0, shape: new[] { 32, 100 }, name: "in");
-                var w = tf.constant(1.0, shape: new[] { 100, 10 }, name: "w");
-                var b = tf.constant(1.0, shape: new[] { 10 }, name: "b");
-                var xw = math_ops.matmul(inp, w, name: "xw");
-                var h = nn_ops.bias_add(xw, b, name: "h");
-                var w_grad = gradients_impl.gradients(new[] { h }, new[] { w })[0];
-                self.assertEquals("MatMul", w_grad.op.type);
-                // TODO: Operation._original_op
-                //self.assertEquals(w_grad.op._original_op, xw.op);
-                self.assertTrue((bool)w_grad.op.get_attr("transpose_a"));
-                self.assertFalse((bool)w_grad.op.get_attr("transpose_b"));
-            });
+            var g = tf.Graph().as_default();
+            var inp = tf.constant(1.0, shape: new[] { 32, 100 }, name: "in");
+            var w = tf.constant(1.0, shape: new[] { 100, 10 }, name: "w");
+            var b = tf.constant(1.0, shape: new[] { 10 }, name: "b");
+            var xw = math_ops.matmul(inp, w, name: "xw");
+            var h = nn_ops.bias_add(xw, b, name: "h");
+            var w_grad = gradients_impl.gradients(new[] { h }, new[] { w })[0];
+            self.assertEquals("MatMul", w_grad.op.type);
+            // TODO: Operation._original_op
+            //self.assertEquals(w_grad.op._original_op, xw.op);
+            self.assertTrue((bool)w_grad.op.get_attr("transpose_a"));
+            self.assertFalse((bool)w_grad.op.get_attr("transpose_b"));
         }
 
         [TestMethod]
@@ -104,14 +102,14 @@ namespace TensorFlowNET.UnitTest.gradients_test
                 tf.constant(new[] { 1 }, tf.int32, new[] { 1 })
             );
             var g = tf.gradients(b, a);
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 var result = sess.run(new object[] { g, b });
                 var actualDeriv = np.squeeze(result[0]);
                 var actual = np.squeeze(result[1]);
                 self.assertEquals(new float[] { 1, 0 }, new float[] { actualDeriv[0], actualDeriv[1] });
                 self.assertEquals(0.9640276f, (float)actual);
-            });
+            }
         }
 
         [TestMethod]

test/TensorFlowNET.UnitTest/nn_test/ZeroFractionTest.cs

@@ -73,12 +73,13 @@ namespace TensorFlowNET.UnitTest.nn_test
         {
             var value = array_ops.placeholder(dtype: dtypes.float32);
             var sparsity = nn_impl.zero_fraction(value);
-            with<Session>(self.cached_session(), sess => {
+            using (var sess = self.cached_session())
+            {
                 // TODO: make this compile
-                      //self.assertAllClose(
-                      //    0.25,
-                      //    sess.run(sparsity, {value: [[0., 1.], [0.3, 2.]]}));
-            });
+                //self.assertAllClose(
+                //    0.25,
+                //    sess.run(sparsity, {value: [[0., 1.], [0.3, 2.]]}));
+            }
         }
 
 

test/TensorFlowNET.UnitTest/ops_test/ControlDependenciesTest.cs

@@ -16,19 +16,18 @@ namespace TensorFlowNET.UnitTest.ops_test
         [TestMethod]
         public void TestBasic()
         {
-            var graph = tf.Graph().as_default();
+            var g = tf.Graph().as_default();
             Tensor a = null, b = null, c = null, d = null, e = null;
-            with<Graph>(graph, g =>
+
+            a = constant_op.constant(1.0);
+            b = constant_op.constant(1.0);
+            tf_with(g.control_dependencies(new[] { a }), x =>
             {
-                a = constant_op.constant(1.0);
-                b = constant_op.constant(1.0);
-                with(g.control_dependencies(new[] { a }), x =>
-                  {
-                      c = constant_op.constant(1.0);
-                      d = array_ops.identity(b);
-                      e = array_ops.identity(c);
-                  });
+                c = constant_op.constant(1.0);
+                d = array_ops.identity(b);
+                e = array_ops.identity(c);
             });
+
             Assert.IsTrue(Enumerable.SequenceEqual(c.op.control_inputs, new[] { a.op }));
             Assert.IsTrue(Enumerable.SequenceEqual(d.op.control_inputs, new[] { a.op }));
             // e should be dominated by c.
@@ -56,7 +55,7 @@ namespace TensorFlowNET.UnitTest.ops_test
                     // TODO: make this compile (see original Python code below)
                     a = constant_op.constant(1.0);
                     b = future; // <--- {henon} obviously, this doesn't compile, looks like control_dependencies needs to be able to take callables as well. 
-                    with(ops.control_dependencies(new object[] { a, b }), ctrl =>
+                    tf_with(ops.control_dependencies(new object[] { a, b }), ctrl =>
                       {
                           return c = constant_op.constant(3.0);
                       });
@@ -64,19 +63,15 @@ namespace TensorFlowNET.UnitTest.ops_test
                 }
                 else
                 {
-                    var graph = tf.Graph().as_default();
-                    with<Graph>(graph, g =>
+                    var g = tf.Graph().as_default();
+                    a = constant_op.constant(1.0);
+                    var b1 = future();
+                    tf_with(g.control_dependencies(new[] { a, b }), ctrl =>
                     {
-                        a = constant_op.constant(1.0);
-                        var b1 = future();
-                        with(g.control_dependencies(new[] { a, b }), ctrl =>
-                        {
-                            c = constant_op.constant(3.0);
-                        });
-                        Assert.IsTrue(Enumerable.SequenceEqual(c.op.control_inputs, new[] { a.op, b1.op }));
-                        Assert.AreEqual(1, calls);
+                        c = constant_op.constant(3.0);
                     });
-
+                    Assert.IsTrue(Enumerable.SequenceEqual(c.op.control_inputs, new[] { a.op, b1.op }));
+                    Assert.AreEqual(1, calls);
                 }
             }
             /*
@@ -139,17 +134,17 @@ namespace TensorFlowNET.UnitTest.ops_test
             var a_3 = constant_op.constant(4.0);
             var a_4 = constant_op.constant(5.0);
             Tensor b_1 = null, b_2 = null;
-            with(g.control_dependencies(new[] { a_1, a_2, a_3, a_4 }), ctrl =>
+            tf_with(g.control_dependencies(new[] { a_1, a_2, a_3, a_4 }), ctrl =>
              {
                  b_1 = constant_op.constant(6.0);
              });
-            with(g.control_dependencies(new[] { a_1 }), ctrl1 =>
+            tf_with(g.control_dependencies(new[] { a_1 }), ctrl1 =>
              {
-                 with(g.control_dependencies(new[] { a_2 }), ctrl2 =>
+                 tf_with(g.control_dependencies(new[] { a_2 }), ctrl2 =>
                  {
-                     with(g.control_dependencies(new[] { a_3 }), ctrl3 =>
+                     tf_with(g.control_dependencies(new[] { a_3 }), ctrl3 =>
                      {
-                         with(g.control_dependencies(new[] { a_4 }), ctrl4 =>
+                         tf_with(g.control_dependencies(new[] { a_4 }), ctrl4 =>
                          {
                              b_2 = constant_op.constant(7.0);
                          });
@@ -175,15 +170,15 @@ namespace TensorFlowNET.UnitTest.ops_test
             var a_3 = constant_op.constant(4.0);
             var a_4 = constant_op.constant(5.0);
             Operation b_3_4 = null, b_3 = null, b_none = null, b_1 = null, b_1_2 = null, b_none2 = null;
-            with(g.control_dependencies(new[] { a_1 }), ctrl1 =>
+            tf_with(g.control_dependencies(new[] { a_1 }), ctrl1 =>
             {
-                with(g.control_dependencies(new[] { a_2 }), ctrl2 =>
+                tf_with(g.control_dependencies(new[] { a_2 }), ctrl2 =>
                 {
-                    with(g.control_dependencies(null), ctrl3 =>
+                    tf_with(g.control_dependencies(null), ctrl3 =>
                     {
-                        with(g.control_dependencies(new[] { a_3 }), ctrl4 =>
+                        tf_with(g.control_dependencies(new[] { a_3 }), ctrl4 =>
                         {
-                            with(g.control_dependencies(new[] { a_4 }), ctrl5 =>
+                            tf_with(g.control_dependencies(new[] { a_4 }), ctrl5 =>
                             {
                                 // deps [a_3, a_4]
                                 b_3_4 = constant_op.constant(7.0);
@@ -199,7 +194,7 @@ namespace TensorFlowNET.UnitTest.ops_test
                 });
                 // deps back to [a_1]
                 b_1 = constant_op.constant(11.0);
-                with(g.control_dependencies(null), ctrl6 =>
+                tf_with(g.control_dependencies(null), ctrl6 =>
                 {
                     // deps are None again
                     b_none2 = constant_op.constant(12.0);
@@ -233,25 +228,25 @@ namespace TensorFlowNET.UnitTest.ops_test
             Operation c_1 = null, c_2 = null, c_3 = null, c_4 = null;
             Operation d_1 = null, d_2 = null, d_3 = null, d_4 = null;
             Operation e_1 = null, e_2 = null, e_3 = null, e_4 = null;
-            with(g.control_dependencies(new[] { a_1 }), ctrl1 =>
+            tf_with(g.control_dependencies(new[] { a_1 }), ctrl1 =>
             {
                 b_1 = tf.multiply(a_3, a_4);
                 c_1 = tf.multiply(a_1, b_1.output);
                 d_1 = tf.multiply(b_1.output, c_1.output);
                 e_1 = constant_op.constant(5.0);
-                with(g.control_dependencies(new[] { a_2 }), ctrl2 =>
+                tf_with(g.control_dependencies(new[] { a_2 }), ctrl2 =>
                 {
                     b_2 = tf.multiply(a_3, a_4);
                     c_2 = tf.multiply(a_1, b_1.output);
                     d_2 = tf.multiply(b_2.output, c_2.output);
                     e_2 = tf.multiply(e_1.output, e_1.output);
-                    with(g.control_dependencies(new[] { a_3 }), ctrl3 =>
+                    tf_with(g.control_dependencies(new[] { a_3 }), ctrl3 =>
                     {
                         b_3 = tf.multiply(a_3, a_4);
                         c_3 = tf.multiply(a_1, b_1.output);
                         d_3 = tf.multiply(b_3.output, c_3.output);
                         e_3 = tf.multiply(e_2.output, e_2.output);
-                        with(g.control_dependencies(new[] { a_4 }), ctrl4 =>
+                        tf_with(g.control_dependencies(new[] { a_4 }), ctrl4 =>
                         {
                             b_4 = tf.multiply(a_3, a_4);
                             c_4 = tf.multiply(a_1, b_1.output);
@@ -310,7 +305,7 @@ namespace TensorFlowNET.UnitTest.ops_test
             var g = tf.Graph().as_default();
             Operation b = null;
             var a = constant_op.constant(100.0);
-            with(g.control_dependencies(new[] { a }), ctrl1 =>
+            tf_with(g.control_dependencies(new[] { a }), ctrl1 =>
             {
                 b = array_ops.identity(a);
             });

test/TensorFlowNET.UnitTest/ops_test/CreateOpFromTfOperationTest.cs

@@ -24,81 +24,73 @@ namespace TensorFlowNET.UnitTest.ops_test
         [TestMethod]
         public void TestShape()
         {
-            var graph = tf.Graph().as_default();
-            with<Graph>(graph, g =>
-            {
-                var x = constant_op.constant(new[,] { { 1, 2, 3 }, { 4, 5, 6 } });
-                var (c_op, op_desc) = ops._create_c_op(g, ops._NodeDef("Identity", "myop"), new[] { x }, new Operation[0]);
-                var op = g._create_op_from_tf_operation(c_op);
-
-                Assert.AreEqual("myop", op.name);
-                Assert.AreEqual("Identity", op.type);
-                Assert.AreEqual(1, len(op.outputs));
-                assertItemsEqual(new[] { 2, 3 }, op.outputs[0].shape);
-            });
+            var g = tf.Graph().as_default();
+
+            var x = constant_op.constant(new[,] { { 1, 2, 3 }, { 4, 5, 6 } });
+            var (c_op, op_desc) = ops._create_c_op(g, ops._NodeDef("Identity", "myop"), new[] { x }, new Operation[0]);
+            var op = g._create_op_from_tf_operation(c_op);
+
+            Assert.AreEqual("myop", op.name);
+            Assert.AreEqual("Identity", op.type);
+            Assert.AreEqual(1, len(op.outputs));
+            assertItemsEqual(new[] { 2, 3 }, op.outputs[0].shape);
         }
 
         [TestMethod]
         public void TestUniqueName()
         {
             var graph = tf.Graph().as_default();
-            with<Graph>(graph, g =>
-            {
-                //var (c_op,op_desc) = ops._create_c_op(g, ops._NodeDef("Const", "myop"), new Tensor[0], new Operation[0]);
-                //var (c_op2, op_desc1) = ops._create_c_op(g, ops._NodeDef("Const", "myop_1"), new Tensor[0], new Operation[0]);
-                //var op = g._create_op_from_tf_operation(c_op);
-                //var op2 = g._create_op_from_tf_operation(c_op2);
-                var op = constant_op.constant(0, name: "myop").op;
-                var op2 = constant_op.constant(0, name: "myop_1").op;
-
-                // Create ops with same names as op1 and op2. We expect the new names to be
-                // uniquified.
-                var op3 = constant_op.constant(0, name: "myop").op;
-                var op4 = constant_op.constant(0, name: "myop_1").op;
-
-                self.assertEqual(op.name, "myop");
-                self.assertEqual(op2.name, "myop_1");
-                self.assertEqual(op3.name, "myop_2");
-                self.assertEqual(op4.name, "myop_1_1");
-            });
+            //var (c_op,op_desc) = ops._create_c_op(g, ops._NodeDef("Const", "myop"), new Tensor[0], new Operation[0]);
+            //var (c_op2, op_desc1) = ops._create_c_op(g, ops._NodeDef("Const", "myop_1"), new Tensor[0], new Operation[0]);
+            //var op = g._create_op_from_tf_operation(c_op);
+            //var op2 = g._create_op_from_tf_operation(c_op2);
+            var op = constant_op.constant(0, name: "myop").op;
+            var op2 = constant_op.constant(0, name: "myop_1").op;
+
+            // Create ops with same names as op1 and op2. We expect the new names to be
+            // uniquified.
+            var op3 = constant_op.constant(0, name: "myop").op;
+            var op4 = constant_op.constant(0, name: "myop_1").op;
+
+            self.assertEqual(op.name, "myop");
+            self.assertEqual(op2.name, "myop_1");
+            self.assertEqual(op3.name, "myop_2");
+            self.assertEqual(op4.name, "myop_1_1");
         }
 
         [Ignore("need tesnroflow expose UpdateEdge API")]
         [TestMethod]
         public void TestCond()
         {
-            var graph = tf.Graph().as_default();
-            with(graph, g =>
+            var g = tf.Graph().as_default();
+            var x = constant_op.constant(10);
+
+            var true_fn = new Func<Tensor>(() =>
             {
-                var x = constant_op.constant(10);
-
-                var true_fn = new Func<Tensor>(() =>
-                {
-                    var (c_op, op_desc) = ops._create_c_op(g, ops._NodeDef("Identity", "cond/myop"), new[] { x }, new Operation[0]);
-                    var new_ops = g._add_new_tf_operations();
-                    self.assertEqual(len(new_ops), 1);
-                    return x;
-                });
-
-                control_flow_ops.cond(x < 10, true_fn, () => x);
-
-                var op = g.get_operation_by_name("cond/myop");
-
-                //tf.train.export_meta_graph(@"D:\dev\tensorboard\logdir\sharp.meta.txt", as_text:true);
-                //tf.train.export_meta_graph(@"D:\dev\tensorboard\logdir\sharp.meta", as_text: false);
-
-                self.assertIsNotNone(op);
-                self.assertEqual(op.name, "cond/myop");
-                self.assertEqual(op.type, "Identity");
-                //self.assertEqual(op.outputs, new object[0]);
-                var op_input = op.inputs[0].op;
-                self.assertEqual(op_input.type, "Switch");
-                self.assertEqual(op_input.inputs[0].name, x.name);
-                self.assertEqual(op.graph, g);
-                self.assertIsNotNone(op._get_control_flow_context());
-                var cond_text = op._get_control_flow_context() as ControlFlowContext;
-                self.assertEqual(cond_text.name, "cond/cond_text");
+                var (c_op, op_desc) = ops._create_c_op(g, ops._NodeDef("Identity", "cond/myop"), new[] { x }, new Operation[0]);
+                var new_ops = g._add_new_tf_operations();
+                self.assertEqual(len(new_ops), 1);
+                return x;
             });
+
+            control_flow_ops.cond(x < 10, true_fn, () => x);
+
+            var op = g.get_operation_by_name("cond/myop");
+
+            //tf.train.export_meta_graph(@"D:\dev\tensorboard\logdir\sharp.meta.txt", as_text:true);
+            //tf.train.export_meta_graph(@"D:\dev\tensorboard\logdir\sharp.meta", as_text: false);
+
+            self.assertIsNotNone(op);
+            self.assertEqual(op.name, "cond/myop");
+            self.assertEqual(op.type, "Identity");
+            //self.assertEqual(op.outputs, new object[0]);
+            var op_input = op.inputs[0].op;
+            self.assertEqual(op_input.type, "Switch");
+            self.assertEqual(op_input.inputs[0].name, x.name);
+            self.assertEqual(op.graph, g);
+            self.assertIsNotNone(op._get_control_flow_context());
+            var cond_text = op._get_control_flow_context() as ControlFlowContext;
+            self.assertEqual(cond_text.name, "cond/cond_text");
         }
 
         [Ignore("Todo: Port")]
@@ -107,20 +99,17 @@ namespace TensorFlowNET.UnitTest.ops_test
         {
             var graph = tf.Graph().as_default();
             Operation x=null;
-            with<Graph>(graph, g =>
+            x = constant_op.constant(42);
+            var body = new Func<int, int>(i =>
             {
-                x = constant_op.constant(42);
-                var body = new Func<int, int>(i =>
-                {
-                    ops._create_c_op(ops.get_default_graph(), ops._NodeDef("Identity", "myloop/myop"), new[] {x},
-                        new Operation[0]);
-                    var new_ops = g._add_new_tf_operations();
-                    self.assertEqual(len(new_ops), 1);
-                    return i;
-                });
-                // TODO: port control_flow_ops.while_loop
-                //control_flow_ops.while_loop( i => i < 10, body, new int[]{0}, name = "myloop");
+                ops._create_c_op(ops.get_default_graph(), ops._NodeDef("Identity", "myloop/myop"), new[] {x},
+                    new Operation[0]);
+                var new_ops = graph._add_new_tf_operations();
+                self.assertEqual(len(new_ops), 1);
+                return i;
             });
+            // TODO: port control_flow_ops.while_loop
+            //control_flow_ops.while_loop( i => i < 10, body, new int[]{0}, name = "myloop");
             var op = graph.get_operation_by_name("myloop/myop");
             self.assertIsNotNone(op);
             self.assertEqual(op.name, "myloop/myop");