Change to NumSharp compact version.

Oceania2018 · Oceania2018 · commit ded0325f4230 · 2020-02-02T18:20:47.000-06:00
diff --git a/src/TensorFlowNET.Core/Operations/random_ops.cs b/src/TensorFlowNET.Core/Operations/random_ops.cs
@@ -80,7 +80,7 @@ public static Tensor random_uniform(int[] shape,
         }
 
         public static Tensor random_uniform(Tensor shape,
-            long minval = 0,
+            int minval = 0,
             Tensor maxval = null,
             TF_DataType dtype = TF_DataType.TF_FLOAT,
             int? seed = null,
diff --git a/src/TensorFlowNET.Core/Sessions/_ElementFetchMapper.cs b/src/TensorFlowNET.Core/Sessions/_ElementFetchMapper.cs
@@ -58,51 +58,18 @@ public override NDArray[] build_results(List<NDArray> values)
                     case NDArray value:
                         result = new[] { value };
                         break;
-#if _REGEN
-        %types=["sbyte", "bool", "byte", "short", "ushort", "int", "uint", "long", "ulong", "float", "double", "Complex"]
-        %foreach types%
-                    case #1 value:
-                        result = new[] { NDArray.Scalar(value) };
-                        break;
-         %
-#else
-                    case sbyte value:
-                        result = new[] { NDArray.Scalar(value) };
-                        break;
                     case bool value:
                         result = new[] { NDArray.Scalar(value) };
                         break;
                     case byte value:
                         result = new[] { NDArray.Scalar(value) };
                         break;
-                    case short value:
-                        result = new[] { NDArray.Scalar(value) };
-                        break;
-                    case ushort value:
-                        result = new[] { NDArray.Scalar(value) };
-                        break;
                     case int value:
                         result = new[] { NDArray.Scalar(value) };
                         break;
-                    case uint value:
-                        result = new[] { NDArray.Scalar(value) };
-                        break;
-                    case long value:
-                        result = new[] { NDArray.Scalar(value) };
-                        break;
-                    case ulong value:
-                        result = new[] { NDArray.Scalar(value) };
-                        break;
                     case float value:
                         result = new[] { NDArray.Scalar(value) };
                         break;
-                    case double value:
-                        result = new[] { NDArray.Scalar(value) };
-                        break;
-                    case Complex value:
-                        result = new[] { NDArray.Scalar(value) };
-                        break;
-#endif
                     default:
                         break;
                 }
diff --git a/src/TensorFlowNET.Core/TensorFlow.Binding.csproj b/src/TensorFlowNET.Core/TensorFlow.Binding.csproj
@@ -60,7 +60,7 @@ https://tensorflownet.readthedocs.io</Description>
 
   <ItemGroup>
     <PackageReference Include="Google.Protobuf" Version="3.11.2" />
-    <PackageReference Include="NumSharp" Version="0.20.5" />
+    <PackageReference Include="NumSharp" Version="0.30.0-alpha" />
   </ItemGroup>
 
   <ItemGroup>
diff --git a/src/TensorFlowNET.Hub/MnistModelLoader.cs b/src/TensorFlowNET.Hub/MnistModelLoader.cs
@@ -116,7 +116,7 @@ private NDArray ExtractImages(string file, int? limit = null)
                     throw new Exception($"Invalid magic number {magic} in MNIST image file: {file}");
                 
                 var num_images =  Read32(bytestream);
-                num_images = limit == null ? num_images : Math.Min(num_images, (uint)limit);
+                num_images = limit == null ? num_images : Math.Min(num_images, (int)limit);
 
                 var rows = Read32(bytestream);
                 var cols = Read32(bytestream);
@@ -125,8 +125,8 @@ private NDArray ExtractImages(string file, int? limit = null)
 
                 bytestream.Read(buf, 0, buf.Length);
 
-                var data = np.frombuffer(buf, np.uint8);
-                data = data.reshape((int)num_images, (int)rows, (int)cols, 1);
+                var data = np.frombuffer(buf, np.@byte);
+                data = data.reshape(num_images, rows, cols, 1);
 
                 return data;
             }
@@ -144,7 +144,7 @@ private NDArray ExtractLabels(string file, bool one_hot = false, int num_classes
                     throw new Exception($"Invalid magic number {magic} in MNIST label file: {file}");
                 
                 var num_items = Read32(bytestream);
-                num_items = limit == null ? num_items : Math.Min(num_items, (uint)limit);
+                num_items = limit == null ? num_items : Math.Min(num_items, (int)limit);
                 
                 var buf = new byte[num_items];
 
@@ -174,11 +174,11 @@ private NDArray DenseToOneHot(NDArray labels_dense, int num_classes)
             return labels_one_hot;
         }
 
-        private uint Read32(FileStream bytestream)
+        private int Read32(FileStream bytestream)
         {
             var buffer = new byte[sizeof(uint)];
             var count = bytestream.Read(buffer, 0, 4);
-            return np.frombuffer(buffer, ">u4").Data<uint>()[0];
+            return np.frombuffer(buffer, ">u4").Data<int>()[0];
         }
     }
 }
diff --git a/src/TensorFlowNET.Hub/Tensorflow.Hub.csproj b/src/TensorFlowNET.Hub/Tensorflow.Hub.csproj
@@ -2,7 +2,7 @@
   <PropertyGroup>
     <RootNamespace>Tensorflow.Hub</RootNamespace>
     <TargetFramework>netstandard2.0</TargetFramework>
-    <Version>0.0.5</Version>
+    <Version>0.0.6</Version>
     <Authors>Kerry Jiang, Haiping Chen</Authors>
     <Company>SciSharp STACK</Company>
     <Copyright>Apache 2.0</Copyright>
@@ -13,14 +13,15 @@
     <Description>TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models.</Description>
     <PackageId>SciSharp.TensorFlowHub</PackageId>
     <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
-    <PackageReleaseNotes>Fix GetNextBatch() bug.</PackageReleaseNotes>
+    <PackageReleaseNotes>Fix GetNextBatch() bug.
+Change to NumSharp compact version.</PackageReleaseNotes>
     <PackageIconUrl>https://avatars3.githubusercontent.com/u/44989469?s=200&amp;v=4</PackageIconUrl>
     <AssemblyName>TensorFlow.Hub</AssemblyName>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">
     <DefineConstants>DEBUG;TRACE</DefineConstants>
   </PropertyGroup>
   <ItemGroup>
-    <PackageReference Include="NumSharp" Version="0.20.5" />
+    <PackageReference Include="NumSharp" Version="0.30.0-alpha" />
   </ItemGroup>
 </Project>

Original file line number	Diff line number	Diff line change
`@@ -80,7 +80,7 @@ public static Tensor random_uniform(int[] shape,`
`80`	`80`	`}`
`81`	`81`
`82`	`82`	`public static Tensor random_uniform(Tensor shape,`
`83`		`- long minval = 0,`
	`83`	`+ int minval = 0,`
`84`	`84`	`Tensor maxval = null,`
`85`	`85`	`TF_DataType dtype = TF_DataType.TF_FLOAT,`
`86`	`86`	`int? seed = null,`
Original file line number	Diff line number	Diff line change
`@@ -116,7 +116,7 @@ private NDArray ExtractImages(string file, int? limit = null)`
`116`	`116`	`throw new Exception($"Invalid magic number {magic} in MNIST image file: {file}");`
`117`	`117`
`118`	`118`	`var num_images = Read32(bytestream);`
`119`		`- num_images = limit == null ? num_images : Math.Min(num_images, (uint)limit);`
	`119`	`+ num_images = limit == null ? num_images : Math.Min(num_images, (int)limit);`
`120`	`120`
`121`	`121`	`var rows = Read32(bytestream);`
`122`	`122`	`var cols = Read32(bytestream);`
`@@ -125,8 +125,8 @@ private NDArray ExtractImages(string file, int? limit = null)`
`125`	`125`
`126`	`126`	`bytestream.Read(buf, 0, buf.Length);`
`127`	`127`
`128`		`- var data = np.frombuffer(buf, np.uint8);`
`129`		`- data = data.reshape((int)num_images, (int)rows, (int)cols, 1);`
	`128`	`+ var data = np.frombuffer(buf, np.@byte);`
	`129`	`+ data = data.reshape(num_images, rows, cols, 1);`
`130`	`130`
`131`	`131`	`return data;`
`132`	`132`	`}`
`@@ -144,7 +144,7 @@ private NDArray ExtractLabels(string file, bool one_hot = false, int num_classes`
`144`	`144`	`throw new Exception($"Invalid magic number {magic} in MNIST label file: {file}");`
`145`	`145`
`146`	`146`	`var num_items = Read32(bytestream);`
`147`		`- num_items = limit == null ? num_items : Math.Min(num_items, (uint)limit);`
	`147`	`+ num_items = limit == null ? num_items : Math.Min(num_items, (int)limit);`
`148`	`148`
`149`	`149`	`var buf = new byte[num_items];`
`150`	`150`
`@@ -174,11 +174,11 @@ private NDArray DenseToOneHot(NDArray labels_dense, int num_classes)`
`174`	`174`	`return labels_one_hot;`
`175`	`175`	`}`
`176`	`176`
`177`		`- private uint Read32(FileStream bytestream)`
	`177`	`+ private int Read32(FileStream bytestream)`
`178`	`178`	`{`
`179`	`179`	`var buffer = new byte[sizeof(uint)];`
`180`	`180`	`var count = bytestream.Read(buffer, 0, 4);`
`181`		`- return np.frombuffer(buffer, ">u4").Data<uint>()[0];`
	`181`	`+ return np.frombuffer(buffer, ">u4").Data<int>()[0];`
`182`	`182`	`}`
`183`	`183`	`}`
`184`	`184`	`}`