Fix inferred_value of KerasTensor. #1142

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

@@ -31,6 +31,6 @@ namespace Tensorflow
         public Tensor reshape(Tensor tensor,
                 object[] shape,
                 string name = null)
-            => gen_array_ops.reshape(tensor, ops.convert_to_tensor(shape), name);
+            => array_ops.reshape(tensor, shape, name);
     }
 }

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

@@ -23,7 +23,7 @@ namespace Tensorflow
             => gen_array_ops.tile(input, multiples, name);
 
         public Tensor tile(Tensor input, object[] multiples, string name = null)
-            => gen_array_ops.tile(input, ops.convert_to_tensor(multiples), name);
+            => array_ops.tile(input, multiples, name);
 
         public Tensor tile(Tensor input, Shape multiples, string name = null) 
         {

src/TensorFlowNET.Core/GlobalUsing.cs

@@ -5,4 +5,5 @@ global using System.Collections;
 global using System.Data;
 global using System.Linq;
 global using Tensorflow.Keras.Engine;
-global using Tensorflow.Framework.Models;
+global using Tensorflow.Framework.Models;
+global using static Tensorflow.Binding;

src/TensorFlowNET.Core/Keras/Engine/KerasTensor.cs

@@ -30,21 +30,32 @@ public class KerasTensor
     public static KerasTensor from_tensor(Tensor tensor)
     {
         var type_spec = tensor.ToTensorSpec();
-        var kt = new KerasTensor(type_spec, name: tensor.name);
+        Shape? inferred_value = default;
+        if (tensor.dtype == TF_DataType.TF_INT32 && tensor.rank < 2)
+        {
+            inferred_value = tf.ones(tensor).shape;
+        }
+        var kt = new KerasTensor(type_spec, inferred_value: inferred_value, name: tensor.name);
         kt.original_tensors = tensor;
         return kt;
     }
 
+    public KerasTensor this[int idx] 
+        => _original_tensors.First()[idx];
+
+    public KerasTensor this[params Slice[] slices]
+        => _original_tensors.First()[slices];
+
     public override string ToString()
         => _original_tensors.Length switch
         {
-            > 1 => "[" + string.Join(", ", _original_tensors.Select(x => $"KerasTensor: shape={x.shape} dtype={x.dtype}")) + "]",
-            1 => $"KerasTensor: shape={_original_tensors.shape} {GetInferredValueString()} dtype={_original_tensors.dtype}",
+            > 1 => "[" + string.Join(", ", _original_tensors.Select(x => $"KerasTensor: shape={x.shape} dtype={x.dtype.as_numpy_name()}{GetInferredValueString()}")) + "]",
+            1 => $"KerasTensor: shape={_original_tensors.shape} dtype={_original_tensors.dtype.as_numpy_name()}{GetInferredValueString()}",
             _ => _original_tensors.ToString(),
         };
 
     private string GetInferredValueString()
-        => _inferred_value == null ? "" : "";
+        => _inferred_value == null ? "" : $" inferred_value={_inferred_value}";
 
     public static implicit operator Tensors(KerasTensor kt)
         => kt._original_tensors;

src/TensorFlowNET.Core/Operations/array_ops.cs

@@ -137,7 +137,7 @@ namespace Tensorflow
             if(shape.Length > 1)
             {
                 shapeTensor = ops.convert_to_tensor(shape, dtypes.int32);
-                if(shapeTensor.ndim > 1)
+                if (shapeTensor.ndim > 1)
                 {
                     shapeTensor = array_ops.reshape(shapeTensor, new Shape(-1));
                 }
@@ -304,6 +304,10 @@ namespace Tensorflow
                         {
                             elems_as_tensors.Add(tensor);
                         }
+                        else if (elem is KerasTensor kt)
+                        {
+                            elems_as_tensors.Add(kt);
+                        }
                         else
                         {
                             var elem_tensor = constant_op.constant(elem, dtype: dtype, name: i.ToString());
@@ -404,7 +408,10 @@ namespace Tensorflow
             => gen_array_ops.reshape(tensor, shape, name: name);
 
         public static Tensor reshape(Tensor tensor, object[] shape, string name = null)
-            => gen_array_ops.reshape(tensor, ops.convert_to_tensor(shape), name: name);
+        {
+            var dims = shape_utils.from_object_array(shape);
+            return gen_array_ops.reshape(tensor, dims, name: name);
+        }
 
         private static Tensor ones_like_impl<T>(T tensor, TF_DataType dtype, string name, bool optimize = true)
         {
@@ -425,6 +432,10 @@ namespace Tensorflow
             return tf_with(ops.name_scope(name, "ones", new { shape }), scope =>
             {
                 name = scope;
+                if (shape._shape_tuple().Length == 0)
+                {
+                    shape = reshape(shape, new Shape(-1));
+                }
                 var output = gen_array_ops.fill(shape, constant_op.constant(1.0f, dtype: dtype), name: name);
                 return output;
             });
@@ -647,6 +658,20 @@ namespace Tensorflow
                 }
             });
 
+        public static Tensor tile(Tensor input, object[] multiples, string name = null)
+        {
+            Shape dims = shape_utils.from_object_array(multiples);
+
+            return tf.Context.ExecuteOp("Tile", name, new ExecuteOpArgs(input, dims)
+            {
+                GetGradientAttrs = (op) => new
+                {
+                    T = op.get_attr<TF_DataType>("T"),
+                    Tmultiples = op.get_attr<TF_DataType>("Tmultiples")
+                }
+            });
+        }
+
         public static Tensor zeros_like(Tensor tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
         {
             return tf_with(ops.name_scope(name, "zeros_like", new Tensor[] { tensor }), scope =>

src/TensorFlowNET.Core/Tensors/shape_utils.cs

@@ -1,5 +1,6 @@
 using System;
 using System.Linq;
+using Tensorflow.Eager;
 using static Tensorflow.Binding;
 
 namespace Tensorflow
@@ -13,5 +14,31 @@ namespace Tensorflow
 
             throw new NotImplementedException("");
         }
+
+        public static Shape from_object_array(object[] shape)
+        {
+            var dims = shape.Select(x =>
+            {
+                if (x is KerasTensor kt && kt.inferred_value != null)
+                {
+                    return kt.inferred_value.as_int_list()[0];
+                }
+                else if (x is EagerTensor et && et.dtype == TF_DataType.TF_INT32)
+                {
+                    return et.ToArray<int>()[0];
+                }
+                else if (x is int i)
+                {
+                    return i;
+                }
+                else if (x is long l)
+                {
+                    return l;
+                }
+                throw new NotImplementedException();
+            }).ToArray();
+
+            return new Shape(dims);
+        }
     }
 }

src/TensorFlowNET.Core/Tensors/tf.constant.cs

@@ -46,6 +46,9 @@ namespace Tensorflow
         public Tensor ones(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
             => array_ops.ones(shape, dtype, name);
 
+        public Tensor ones(Tensor shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
+            => array_ops.ones(shape, dtype, name);
+
         public Tensor size(Tensor input,
             string name = null,
             TF_DataType out_type = TF_DataType.TF_INT32) => array_ops.size(input,

src/TensorFlowNET.Core/ops.cs

@@ -144,11 +144,18 @@ namespace Tensorflow
                     }
                     if (!graph.building_function)
                     {
-                        throw new RuntimeError("Attempting to capture an EagerTensor without building a function.");
-                        // return eager_tensor.AsPlaceholder(name: name);
+                        // throw new RuntimeError("Attempting to capture an EagerTensor without building a function.");
+                        return eager_tensor.AsPlaceholder(name: name);
                     }
                 }
             }
+            else if (value is KerasTensor kt)
+            {
+                if (kt.inferred_value != null)
+                {
+                    return convert_to_tensor(kt.inferred_value, dtype: kt.dtype, name: name);
+                }
+            }
 
             // graph mode
             Tensor ret = value switch

src/TensorFlowNET.Keras/Tensorflow.Keras.csproj

@@ -141,7 +141,7 @@ Keras is an API designed for human beings, not machines. Keras follows best prac
 
   <ItemGroup>
     <PackageReference Include="HDF5-CSharp" Version="1.17.0" />
-    <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.148" />
+    <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.149" />
     <PackageReference Include="SharpZipLib" Version="1.4.2" />
   </ItemGroup>
 

test/TensorFlowNET.UnitTest/Tensorflow.Binding.UnitTest.csproj

@@ -41,8 +41,8 @@
 
   <ItemGroup>
     <PackageReference Include="FluentAssertions" Version="5.10.3" />
-    <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.148" />
-    <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.3.2" />
+    <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.149" />
+    <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.6.3" />
     <PackageReference Include="MSTest.TestAdapter" Version="2.2.10" />
     <PackageReference Include="MSTest.TestFramework" Version="2.2.10" />
   </ItemGroup>