Attempting to use uninitialized value v1 #172

src/TensorFlowNET.Core/Gradients/gradients_impl.py.cs

@@ -95,9 +95,9 @@ namespace Tensorflow
                 {
                     // 'ready' handles the case where one output gradient relies on
                     // another output's gradient.
-                    if (!pending_count.ContainsKey(op.Name))
-                        pending_count[op.Name] = 0;
-                    bool ready = pending_count[op.Name] == 0;
+                    if (!pending_count.ContainsKey(op.name))
+                        pending_count[op.name] = 0;
+                    bool ready = pending_count[op.name] == 0;
                     if(ready && !to_ops_set.Contains(op) && reachable_to_ops.Contains(op))
                     {
                         to_ops_set.Add(op);
@@ -131,7 +131,7 @@ namespace Tensorflow
                             // for ops that do not have gradients.
                             var grad_fn = ops.get_gradient_function(op);
 
-                            Python.with<ops.name_scope>(new ops.name_scope(op.Name + "_grad"), scope1 =>
+                            Python.with<ops.name_scope>(new ops.name_scope(op.name + "_grad"), scope1 =>
                             {
                                 string name1 = scope1;
                                 if (grad_fn != null)
@@ -193,12 +193,12 @@ namespace Tensorflow
         {
             foreach(var x in _NonEagerInputs(op, xs))
             {
-                if (!pending_count.ContainsKey(x.op.Name))
-                    pending_count[x.op.Name] = 0;
+                if (!pending_count.ContainsKey(x.op.name))
+                    pending_count[x.op.name] = 0;
 
-                pending_count[x.op.Name] -= 1;
+                pending_count[x.op.name] -= 1;
 
-                var ready = pending_count[x.op.Name] == 0;
+                var ready = pending_count[x.op.name] == 0;
 
                 if(loop_state != null && !ready)
                 {
@@ -281,10 +281,10 @@ namespace Tensorflow
                 bool is_stop_op = true;
                 foreach(var inp in _NonEagerInputs(op, xs))
                 {
-                    if (!pending_count.ContainsKey(inp.op.Name))
-                        pending_count[inp.op.Name] = 0;
+                    if (!pending_count.ContainsKey(inp.op.name))
+                        pending_count[inp.op.name] = 0;
 
-                    if (pending_count[inp.op.Name] > 0)
+                    if (pending_count[inp.op.name] > 0)
                     {
                         is_stop_op = false;
                         break;
@@ -300,17 +300,17 @@ namespace Tensorflow
         private static Tensor _GetGrad(Dictionary<string, Tensor[][]> grads, Tensor t)
         {
             var op = t.op;
-            if (!grads.ContainsKey(op.Name))
+            if (!grads.ContainsKey(op.name))
                 return null;
-            Tensor[][] op_grads = grads[op.Name];
+            Tensor[][] op_grads = grads[op.name];
             var t_grad = op_grads[t.value_index];
             return t_grad[0];
         }
 
         private static Tensor[][] _GetGrads(Dictionary<string, Tensor[][]> grads, Operation op)
         {
-            if (grads.ContainsKey(op.Name))
-                return grads[op.Name];
+            if (grads.ContainsKey(op.name))
+                return grads[op.name];
             else
                 return op.outputs.Select(x => new Tensor[0]).ToArray();
         }
@@ -324,11 +324,11 @@ namespace Tensorflow
         private static void _SetGrad(Dictionary<string, Tensor[][]> grads, Tensor t, Tensor grad)
         {
             var op = t.op;
-            Tensor[][] op_grads = grads.ContainsKey(op.Name) ? grads[op.Name] : null;
+            Tensor[][] op_grads = grads.ContainsKey(op.name) ? grads[op.name] : null;
             if (op_grads == null)
             {
                 op_grads = op.outputs.Select(x => new Tensor[1]).ToArray();
-                grads[op.Name] = op_grads;
+                grads[op.name] = op_grads;
             }
             var t_grads = op_grads[t.value_index];
             t_grads[0] = grad;
@@ -421,10 +421,10 @@ namespace Tensorflow
                 {
                     if (between_ops.Contains(x.op))
                     {
-                        if (!pending_count.ContainsKey(x.op.Name))
-                            pending_count[x.op.Name] = 0;
+                        if (!pending_count.ContainsKey(x.op.name))
+                            pending_count[x.op.name] = 0;
 
-                        pending_count[x.op.Name] += 1;
+                        pending_count[x.op.name] += 1;
                     }
                 }
             }

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -150,8 +150,9 @@ namespace Tensorflow
             _create_op_helper(op, true);
 
             Console.Write($"create_op: {op_type} '{node_def.Name}'");
-            Console.Write($", inputs: {(inputs.Length == 0 ? "empty" : String.Join(",", inputs.Select(x => x.name)))}");
-            Console.Write($", outputs: {(op.outputs.Length == 0 ? "empty" : String.Join(",", op.outputs.Select(x => x.name)))}");
+            Console.Write($", inputs: {(inputs.Length == 0 ? "empty" : String.Join(", ", inputs.Select(x => x.name)))}");
+            Console.Write($", control_inputs: {(control_inputs.Length == 0 ? "empty" : String.Join(", ", control_inputs.Select(x => x.name)))}");
+            Console.Write($", outputs: {(op.outputs.Length == 0 ? "empty" : String.Join(", ", op.outputs.Select(x => x.name)))}");
             Console.WriteLine();
 
             return op;
@@ -182,7 +183,7 @@ namespace Tensorflow
             }
             else if (tensor_or_op is Operation)
             {
-                return !_unfetchable_ops.Contains((tensor_or_op as Operation).Name);
+                return !_unfetchable_ops.Contains((tensor_or_op as Operation).name);
             }
 
             return false;

src/TensorFlowNET.Core/ITensorOrOperation.cs

@@ -12,5 +12,6 @@ namespace Tensorflow
     {
         string Device { get; }
         Operation op { get; }
+        string name { get; }
     }
 }

src/TensorFlowNET.Core/Operations/Operation.cs

@@ -20,7 +20,7 @@ namespace Tensorflow
 
         private Status status = new Status();
 
-        public string Name => c_api.StringPiece(c_api.TF_OperationName(_handle));
+        public string name => c_api.StringPiece(c_api.TF_OperationName(_handle));
         public string OpType => c_api.StringPiece(c_api.TF_OperationOpType(_handle));
         public string Device => c_api.StringPiece(c_api.TF_OperationDevice(_handle));
 
@@ -210,7 +210,7 @@ namespace Tensorflow
 
         public override string ToString()
         {
-            return _handle == IntPtr.Zero ? "tf.Operation Undefined" : $"tf.Operation '{Name}' type={OpType}";
+            return _handle == IntPtr.Zero ? "tf.Operation Undefined" : $"tf.Operation '{name}' type={OpType}";
         }
 
         public static implicit operator Operation(IntPtr handle) => new Operation(handle);

src/TensorFlowNET.Core/Sessions/_FetchHandler.cs

@@ -73,7 +73,7 @@ namespace Tensorflow
         {
             if (!graph.is_fetchable(op))
             {
-                throw new Exception($"Operation {op.Name} has been marked as not fetchable.");
+                throw new Exception($"Operation {op.name} has been marked as not fetchable.");
             }
         }
 

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -25,7 +25,7 @@ namespace Tensorflow
         /// <summary>
         /// The string name of this tensor.
         /// </summary>
-        public string name => $"{(op == null ? "Operation was not named" : $"{op.Name}:{value_index}")}";
+        public string name => $"{(op == null ? "Operation was not named" : $"{op.name}:{value_index}")}";
 
         public int value_index { get; }
 

src/TensorFlowNET.Core/Train/Optimizer.cs

@@ -97,7 +97,7 @@ namespace Tensorflow
                     if (grad == null)
                         continue;
 
-                    var scope_name = var.op.Name;
+                    var scope_name = var.op.name;
                     Python.with<ops.name_scope>(new ops.name_scope("update_" + scope_name), scope2 =>
                     {
                         update_ops.Add(processor.update_op(this, grad));

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

@@ -116,7 +116,7 @@ namespace Tensorflow
                 {
                     FilenameTensorName = filename_tensor.name,
                     SaveTensorName = save_tensor.name,
-                    RestoreOpName = restore_op.Name,
+                    RestoreOpName = restore_op.name,
                     MaxToKeep = max_to_keep,
                     Sharded = sharded,
                     KeepCheckpointEveryNHours = keep_checkpoint_every_n_hours,

src/TensorFlowNET.Core/Train/Saving/saveable_object_util.py.cs

@@ -84,9 +84,9 @@ namespace Tensorflow
                         }
 
                         if (var.op.type == "ReadVariableOp")
-                            name = var.op.inputs[0].op.Name;
+                            name = var.op.inputs[0].op.name;
                         else
-                            name = var.op.Name;
+                            name = var.op.name;
 
                         if (names_to_saveables.ContainsKey(name))
                             throw new ValueError($"At least two variables have the same name: {name}");

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -143,11 +143,11 @@ namespace Tensorflow
         private Tensor _safe_initial_value_from_tensor(Tensor tensor, Dictionary<string, Operation> op_cache)
         {
             var op = tensor.op;
-            var new_op = op_cache.ContainsKey(op.Name) ? op_cache[op.Name] : null;
+            var new_op = op_cache.ContainsKey(op.name) ? op_cache[op.name] : null;
             if(new_op == null)
             {
                 new_op = _safe_initial_value_from_op(op, op_cache);
-                op_cache[op.Name] = new_op;
+                op_cache[op.name] = new_op;
             }
             return new_op.outputs[tensor.value_index];
         }
@@ -185,7 +185,7 @@ namespace Tensorflow
         /// A `Tensor` that will hold the new value of this variable after
         /// the assignment has completed.
         /// </returns>
-        public ITensorOrOperation assign(Tensor value, bool use_locking = false, string name = "", bool read_value = true)
+        public ITensorOrOperation assign(object value, bool use_locking = false, string name = "", bool read_value = true)
         {
             var assign = gen_state_ops.assign(_variable, value, use_locking: use_locking, name: name);
             if (read_value)

src/TensorFlowNET.Core/Variables/gen_state_ops.py.cs

@@ -47,7 +47,7 @@ namespace Tensorflow
         /// <param name="validate_shape"></param>
         /// <param name="use_locking"></param>
         /// <param name="name"></param>
-        public static Tensor assign(Tensor tensor, Tensor value, 
+        public static Tensor assign(Tensor tensor, object value, 
             bool validate_shape = true, 
             bool use_locking = true,
             string name = "")

src/TensorFlowNET.Core/ops.name_scope.cs

@@ -46,7 +46,7 @@ namespace Tensorflow
             public void Dispose()
             {
                 var g = get_default_graph();
-                Console.WriteLine($"name_scope: {g._name_stack} -> {old_stack}");
+                // Console.WriteLine($"name_scope: {g._name_stack} -> {old_stack}");
                 g._name_stack = old_stack;
             }
 

src/TensorFlowNET.Core/ops.py.cs

@@ -319,7 +319,7 @@ namespace Tensorflow
 
             return (oper, out_grads) =>
             {
-                Console.WriteLine($"get_gradient_function: {oper.type} '{oper.Name}'");
+                Console.WriteLine($"get_gradient_function: {oper.type} '{oper.name}'");
 
                 switch (oper.type)
                 {
@@ -414,6 +414,8 @@ namespace Tensorflow
                     return constant_op.constant(value as string[], name);
                 case "Int32":
                     return constant_op.constant(Convert.ToInt32(value), name);
+                case "Single":
+                    return constant_op.constant(Convert.ToSingle(value), name);
                 case "Double":
                     return constant_op.constant(Convert.ToDouble(value), name);
                 case "RefVariable":

src/TensorFlowNET.Core/tf.cs

@@ -17,6 +17,7 @@ namespace Tensorflow
         public static Context context = new Context(new ContextOptions(), new Status());
 
         public static Graph g = new Graph();
+        public static Session session = new Session();
 
         public static RefVariable Variable<T>(T data, string name = "", TF_DataType dtype = TF_DataType.DtInvalid)
         {
@@ -48,7 +49,7 @@ namespace Tensorflow
 
         public static Session Session()
         {
-            return new Session();
+            return session;
         }
     }
 }

test/TensorFlowNET.UnitTest/GraphTest.cs

@@ -23,7 +23,7 @@ namespace TensorFlowNET.UnitTest
 
             // Make a placeholder operation.
             var feed = c_test_util.Placeholder(graph, s);
-            EXPECT_EQ("feed", feed.Name);
+            EXPECT_EQ("feed", feed.name);
             EXPECT_EQ("Placeholder", feed.OpType);
             EXPECT_EQ("", feed.Device);
             EXPECT_EQ(1, feed.NumOutputs);
@@ -53,7 +53,7 @@ namespace TensorFlowNET.UnitTest
             EXPECT_EQ(TF_Code.TF_OK, s.Code);
 
             // Test TF_Operation*() query functions.
-            EXPECT_EQ("add", add.Name);
+            EXPECT_EQ("add", add.name);
             EXPECT_EQ("AddN", add.OpType);
             EXPECT_EQ("", add.Device);
             EXPECT_EQ(1, add.NumOutputs);

test/TensorFlowNET.UnitTest/VariableTest.cs

@@ -49,8 +49,7 @@ namespace TensorFlowNET.UnitTest
         [TestMethod]
         public void Assign()
         {
-            var v1 = tf.get_variable("v1", shape: new TensorShape(3), initializer: tf.zeros_initializer);
-
+            var v1 = tf.Variable(10.0f, name: "v1"); //tf.get_variable("v1", shape: new TensorShape(3), initializer: tf.zeros_initializer);
             var inc_v1 = v1.assign(v1 + 1.0f);
 
             // Add an op to initialize the variables.
@@ -73,10 +72,10 @@ namespace TensorFlowNET.UnitTest
             int result = 0;
             Tensor x = tf.Variable(10, name: "x");
 
-            var model = tf.global_variables_initializer();
+            var init_op = tf.global_variables_initializer();
             using (var session = tf.Session())
             {
-                session.run(model);
+                session.run(init_op);
                 for(int i = 0; i < 5; i++)
                 {
                     x = x + 1;