VarCreation1 passed. create variable through variable_scope.

README.md

@@ -66,7 +66,7 @@ using(var sess = tf.Session())
 
 Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflownet.readthedocs.io/en/latest/FrontCover.html).
 
-More examples:
+### More examples:
 
 * [Hello World](test/TensorFlowNET.Examples/HelloWorld.cs)
 * [Basic Operations](test/TensorFlowNET.Examples/BasicOperations.cs)

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

@@ -7,7 +7,7 @@ namespace Tensorflow
     public static partial class tf
     {
         public static IInitializer zeros_initializer => new Zeros();
-        public static IInitializer glorot_uniform => new GlorotUniform();
+        public static IInitializer glorot_uniform_initializer => new GlorotUniform();
         
         public static variable_scope variable_scope(string name_or_scope,
                string default_name = null,

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

@@ -24,10 +24,34 @@ namespace Tensorflow
             if (!seed2.HasValue)
                 seed2 = 0;
 
-            var _op = _op_def_lib._apply_op_helper("RandomStandardNormal", name: name,
+            var _op = _op_def_lib._apply_op_helper("RandomStandardNormal", 
+                name: name,
                 args: new { shape, dtype, seed, seed2 });
 
             return _op.outputs[0];
         }
+
+        /// <summary>
+        /// Outputs random values from a uniform distribution.
+        /// </summary>
+        /// <param name="shape"></param>
+        /// <param name="dtype"></param>
+        /// <param name="seed"></param>
+        /// <param name="seed2"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor random_uniform(Tensor shape, TF_DataType dtype, int? seed = 0, int? seed2 = 0, string name = null)
+        {
+            if (!seed.HasValue)
+                seed = 0;
+            if (!seed2.HasValue)
+                seed2 = 0;
+
+            var _op = _op_def_lib._apply_op_helper("RandomUniform",
+                name: name,
+                args: new { shape, dtype, seed, seed2});
+
+            return _op.outputs[0];
+        }
     }
 }

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

@@ -56,7 +56,11 @@ namespace Tensorflow
             return with<ops.name_scope, Tensor>(new ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
             {
                 name = scope;
-                return null;
+                var tensorShape = _ShapeTensor(shape);
+                var minTensor = ops.convert_to_tensor(minval, dtype: dtype, name: "min");
+                var maxTensor = ops.convert_to_tensor(maxval, dtype: dtype, name: "max");
+                var rnd = gen_random_ops.random_uniform(tensorShape, dtype);
+                return math_ops.add(rnd * (maxTensor - minTensor), minTensor, name: name);
             });
         }
 

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -77,6 +77,11 @@ namespace Tensorflow
             return null;
         }
 
+        public TensorShape getShape()
+        {
+            return tensor_util.to_shape(shape);
+        }
+
         /// <summary>
         /// number of dimensions
         /// 0	Scalar (magnitude only)

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -1,4 +1,6 @@
-using System;
+using Google.Protobuf;
+using Google.Protobuf.Collections;
+using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
@@ -99,7 +101,7 @@ namespace Tensorflow
             if (initial_value is null)
                 throw new ValueError("initial_value must be specified.");
 
-            var init_from_fn = false;
+            var init_from_fn = initial_value.GetType().Name == "Func`1";
 
             if(collections == null)
             {
@@ -115,12 +117,27 @@ namespace Tensorflow
                 collections.Add(ops.GraphKeys.TRAINABLE_VARIABLES);
 
             ops.init_scope();
-            var values = init_from_fn ? new List<object>() : new List<object> { initial_value };
-            Python.with<ops.name_scope>(new ops.name_scope(name, "Variable", values), scope =>
+            var values = init_from_fn ? new object[0] : new object[] { initial_value };
+            with<ops.name_scope>(new ops.name_scope(name, "Variable", values), scope =>
             {
+                name = scope;
                 if (init_from_fn)
                 {
-
+                    // Use attr_scope and device(None) to simulate the behavior of
+                    // colocate_with when the variable we want to colocate with doesn't
+                    // yet exist.
+                    string true_name = ops._name_from_scope_name(name);
+                    var attr = new AttrValue
+                    {
+                        List = new AttrValue.Types.ListValue()
+                    };
+                    attr.List.S.Add(ByteString.CopyFromUtf8($"loc:{true_name}"));
+                    with<ops.name_scope>(new ops.name_scope("Initializer"), scope2 =>
+                    {
+                        _initial_value = (initial_value as Func<Tensor>)();
+                        _initial_value = ops.convert_to_tensor(_initial_value, name: "initial_value", dtype: dtype);
+                        _variable = state_ops.variable_op_v2(_initial_value.shape, _initial_value.dtype.as_base_dtype(), name: name);
+                    });
                 }
                 // Or get the initial value from a Tensor or Python object.
                 else
@@ -135,7 +152,9 @@ namespace Tensorflow
                 // Manually overrides the variable's shape with the initial value's.
                 if (validate_shape)
                 {
-                    var initial_value_shape = _initial_value.shape;
+                    var initial_value_shape = _initial_value.getShape();
+                    if (!initial_value_shape.is_fully_defined())
+                        throw new ValueError($"initial_value must have a shape specified: {_initial_value}");
                 }
 
                 // If 'initial_value' makes use of other variables, make sure we don't

src/TensorFlowNET.Core/Variables/_VariableStore.cs

@@ -74,7 +74,9 @@ namespace Tensorflow
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
             VariableAggregation aggregation = VariableAggregation.NONE)
         {
-            bool initializing_from_value = false;
+            bool initializing_from_value = true;
+            if (use_resource == null)
+                use_resource = false;
 
             if (_vars.ContainsKey(name))
             {
@@ -86,15 +88,18 @@ namespace Tensorflow
                 throw new NotImplementedException("_get_single_variable");
             }
 
-            Tensor init_val = null;
-
+            RefVariable v = null;
             // Create the tensor to initialize the variable with default value.
             if (initializer == null)
             {
                 if (dtype.is_floating())
-                    initializer = tf.glorot_uniform;
+                {
+                    initializer = tf.glorot_uniform_initializer;
+                    initializing_from_value = false;
+                }
             }
 
+            // Create the variable.
             ops.init_scope();
             {
                 if (initializing_from_value)
@@ -103,23 +108,19 @@ namespace Tensorflow
                 }
                 else
                 {
-                    init_val = initializer.call(shape, dtype);
+                    Func<Tensor> init_val = () => initializer.call(shape, dtype);
                     var variable_dtype = dtype.as_base_dtype();
+
+                    v = variable_scope.default_variable_creator(init_val,
+                        name: name,
+                        trainable: trainable,
+                        dtype: TF_DataType.DtInvalid,
+                        validate_shape: validate_shape,
+                        synchronization: synchronization,
+                        aggregation: aggregation);
                 }
             }
 
-            // Create the variable.
-            if (use_resource == null)
-                use_resource = false;
-
-            var v = variable_scope.default_variable_creator(init_val, 
-                name: name, 
-                trainable: trainable, 
-                dtype: TF_DataType.DtInvalid,
-                validate_shape: validate_shape,
-                synchronization: synchronization,
-                aggregation: aggregation);
-
             _vars[name] = v;
 
             return v;

src/TensorFlowNET.Core/Variables/state_ops.cs

@@ -0,0 +1,28 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public class state_ops
+    {
+        /// <summary>
+        /// Create a variable Operation.
+        /// </summary>
+        /// <param name="shape"></param>
+        /// <param name="dtype"></param>
+        /// <param name="name"></param>
+        /// <param name="container"></param>
+        /// <param name="shared_name"></param>
+        /// <returns></returns>
+        public static Tensor variable_op_v2(long[] shape,
+            TF_DataType dtype,
+            string name = "Variable",
+            string container = "",
+            string shared_name = "") => gen_state_ops.variable_v2(shape, 
+                dtype, 
+                name: name, 
+                container: container, 
+                shared_name: shared_name);
+    }
+}

test/TensorFlowNET.Examples/LinearRegression.cs

@@ -16,7 +16,7 @@ namespace TensorFlowNET.Examples
 
         // Parameters
         float learning_rate = 0.01f;
-        int training_epochs = 10000;
+        int training_epochs = 1000;
         int display_step = 50;
 
         public void Run()

test/TensorFlowNET.UnitTest/VariableTest.cs

@@ -30,14 +30,18 @@ namespace TensorFlowNET.UnitTest
             var mammal2 = tf.Variable("Tiger");
         }
 
+        /// <summary>
+        /// https://www.tensorflow.org/api_docs/python/tf/variable_scope
+        /// </summary>
         [TestMethod]
-        public void SimpleScope()
+        public void VarCreation1()
         {
             with(tf.variable_scope("foo"), delegate
             {
                 with(tf.variable_scope("bar"), delegate
                 {
                     var v = tf.get_variable("v", new TensorShape(1));
+                    Assert.AreEqual(v.name, "foo/bar/v:0");
                 });
             });
         }