v1.1.0

deepakkumar1984 · Jun 5, 2019 · a4ba26c · a4ba26c
1 parent 1f8a22e
commit a4ba26c
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Showing 2 changed files with 11 additions and 9 deletions.
diff --git a/examples/MNIST/Program.cs b/examples/MNIST/Program.cs
@@ -26,9 +26,9 @@ static void Main(string[] args)
 
             var (train, val) = DataSetParser.MNIST(trainImagePath, trainLabelPath, valImagePath, valLabelPath, batchSize, 1);
             var model = new Module();
-            BuildNNModel(model);
+            //BuildNNModel(model);
             //BuildSymbolModel(model);
-            //BuildConvNNModel(model);
+            BuildConvNNModel(model);
 
             model.Fit(train, 10, batchSize, val);
 

diff --git a/src/MxNetLib/MxNetLib.csproj b/src/MxNetLib/MxNetLib.csproj
@@ -1,24 +1,25 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
-    <TargetFramework>netstandard2.0</TargetFramework>
+    <TargetFrameworks>net46;netstandard2.0</TargetFrameworks>
     <LangVersion>7.2</LangVersion>
     <Platforms>AnyCPU;x64</Platforms>
-    <Version>1.0.6</Version>
+    <Version>1.1.0</Version>
     <Authors>Deepak Battini</Authors>
     <Product />
     <Description>C# Binding for the Apache MxNet library with some high level API defined in MxNet.NN.
 
 MxNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.
 
 MXNet is more than a deep learning project. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.</Description>
-    <PackageProjectUrl>https://github.com/deepakkumar1984/mxnetlib/</PackageProjectUrl>
-    <RepositoryUrl>https://github.com/deepakkumar1984/mxnetlib/</RepositoryUrl>
+    <PackageProjectUrl>https://github.com/tech-quantum/mxnetlib/</PackageProjectUrl>
+    <RepositoryUrl>https://github.com/tech-quantum/mxnetlib/</RepositoryUrl>
     <RepositoryType>git</RepositoryType>
     <PackageTags>deep learning, mxnet, csharp, .net, neural network</PackageTags>
     <GeneratePackageOnBuild>false</GeneratePackageOnBuild>
-    <PackageLicenseFile>LICENSE</PackageLicenseFile>
-    <PackageIconUrl>https://raw.githubusercontent.com/deepakkumar1984/mxnetlib/master/icon_32.PNG</PackageIconUrl>
+    <PackageIconUrl>https://raw.githubusercontent.com/tech-quantum/mxnetlib/master/icon_32.PNG</PackageIconUrl>
+    <PackageLicenseUrl>https://raw.githubusercontent.com/tech-quantum/mxnetlib/master/LICENSE</PackageLicenseUrl>
+    <PackageId>mxnetlib</PackageId>
   </PropertyGroup>
 
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">
@@ -31,6 +32,7 @@ MXNet is more than a deep learning project. It is a collection of blue prints an
 
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|AnyCPU'">
     <AllowUnsafeBlocks>true</AllowUnsafeBlocks>
+    <Optimize>false</Optimize>
   </PropertyGroup>
 
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">