Minor edits to the DeepLearning Binary classification sample (#868)

* Minor edits to the DeepLearning Binary classification sample

* Add necessary library for when running on net core 2.1

samples/csharp/getting-started/DeepLearning_ImageClassification_Binary/DeepLearning_ImageClassification/DeepLearning_ImageClassification.csproj

@@ -10,6 +10,7 @@
     <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
     <PackageReference Include="Microsoft.ML.Vision" Version="$(MicrosoftMLVersion)" />
     <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.3.0" />
+    <PackageReference Condition="'$(TargetFramework)'=='netcoreapp2.1'" Include="System.Runtime.CompilerServices.Unsafe" Version="5.0.0" />
   </ItemGroup>
 
   <ItemGroup>

samples/csharp/getting-started/DeepLearning_ImageClassification_Binary/DeepLearning_ImageClassification/Program.cs

@@ -53,8 +53,7 @@ namespace DeepLearning_ImageClassification
                 MetricsCallback = (metrics) => Console.WriteLine(metrics),
                 TestOnTrainSet = false,
                 ReuseTrainSetBottleneckCachedValues = true,
-                ReuseValidationSetBottleneckCachedValues = true,
+                ReuseValidationSetBottleneckCachedValues = true
-                WorkspacePath=workspaceRelativePath
             };
 
             var trainingPipeline = mlContext.MulticlassClassification.Trainers.ImageClassification(classifierOptions)

samples/csharp/getting-started/DeepLearning_ImageClassification_Binary/README.md

@@ -19,6 +19,8 @@ products:
 
 For a detailed explanation of how to build this application, see the accompanying [tutorial](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/image-classification-api-transfer-learning) on the Microsoft Docs site.
 
+This sample may be downloaded and built directly. However, for a succesful run, you **must** first unzip *assets.zip* in the project directory, and copy its subdirectories into the *assets* directory.
+
 ## Understanding the problem
 
 Image classification is a computer vision problem. Image classification takes an image as input and categorizes it into a prescribed class. This sample shows a .NET Core console application that trains a custom deep learning model using transfer learning, a pretrained image classification TensorFlow model and the ML.NET Image Classification API to classify images of concrete surfaces into one of two categories, cracked or uncracked.