fixing broken merges to master (migueldeicaza#460)

EnricoMi · migueldeicaza · commit cbe7c337dc4f · 2019-10-28T10:13:13.000-04:00
- adding AdamOptimizer (migueldeicaza#435) conflicted with fixing unit tests (migueldeicaza#448) - adding CreateTensorFromImageFileAlt function (migueldeicaza#421) was missing the System.Drawing reference
diff --git a/Examples/ExampleCommon/ExampleCommon.csproj b/Examples/ExampleCommon/ExampleCommon.csproj
@@ -36,6 +36,7 @@
   <ItemGroup>
     <Reference Include="System" />
     <Reference Include="System.Core" />
+    <Reference Include="System.Drawing" />
     <Reference Include="System.Xml.Linq" />
     <Reference Include="System.Data.DataSetExtensions" />
     <Reference Include="Microsoft.CSharp" />
diff --git a/Examples/ExampleCommon/ImageUtil.cs b/Examples/ExampleCommon/ImageUtil.cs
@@ -1,4 +1,6 @@
-&#65279;using System.IO;
+&#65279;using System.Drawing;
+using System.Drawing.Imaging;
+using System.IO;
 using TensorFlow;
 
 namespace ExampleCommon
diff --git a/TensorFlowSharp/Optimizer.cs b/TensorFlowSharp/Optimizer.cs
@@ -368,7 +368,7 @@ public override TFOperation[] ApplyGradient((TFOutput gradient, Variable variabl
             for (int i = 0; i < gradientsAndVariables.Length; i++)
             {
                 var gv = gradientsAndVariables[i];
-                var lr = _graph.Cast(LearningRate.Read, gv.gradient.OutputType);
+                var lr = _graph.Cast(LearningRate, gv.gradient.OutputType);
                 var one = _graph.Const(1f);
 
                 var t = _graph.Cast(Iterations.Read, _beta1.OutputType);
diff --git a/tests/TensorFlowSharp.Tests.CSharp/OptimizerTests.cs b/tests/TensorFlowSharp.Tests.CSharp/OptimizerTests.cs
@@ -854,6 +854,10 @@ public void LinearRegresionTrainingWithAdamOptimizerTest()
                 var sgd = new AdamOptimizer(graph, learning_rate);
                 var updateOps = sgd.Minimize(cost);
 
+                var iter = sgd.Iterations.ReadAfter (updateOps);
+                var readW = W.ReadAfter (updateOps);
+                var readb = b.ReadAfter (updateOps);
+
                 using (var sesssion = new TFSession(graph))
                 {
                     sesssion.GetRunner().AddTarget(graph.GetGlobalVariablesInitializer()).Run();
@@ -866,7 +870,7 @@ public void LinearRegresionTrainingWithAdamOptimizerTest()
                             var tensors = sesssion.GetRunner()
                                 .AddInput(X, new TFTensor(train_x[j]))
                                 .AddInput(Y, new TFTensor(train_y[j]))
-                                .AddTarget(updateOps).Fetch(cost, W.Read, b.Read, pred).Run();
+                                .AddTarget(updateOps).Fetch(cost, readW, readb, pred).Run();
                             var output = $"loss: {tensors[0].GetValue():F4}, W: {tensors[1].GetValue():F4}, b: {tensors[2].GetValue():F4}";
                             Assert.Equal(expectedLines[i * n_samples + j], output);
                         }
@@ -910,6 +914,10 @@ public void LinearRegresionTrainingWithAdamOptimizerDecayTest()
                 var sgd = new AdamOptimizer(graph, learning_rate, decay: 0.5f);
                 var updateOps = sgd.Minimize(cost);
 
+                var iter = sgd.Iterations.ReadAfter (updateOps);
+                var readW = W.ReadAfter (updateOps);
+                var readb = b.ReadAfter (updateOps);
+
                 using (var sesssion = new TFSession(graph))
                 {
                     sesssion.GetRunner().AddTarget(graph.GetGlobalVariablesInitializer()).Run();
@@ -922,7 +930,7 @@ public void LinearRegresionTrainingWithAdamOptimizerDecayTest()
                             var tensors = sesssion.GetRunner()
                                 .AddInput(X, new TFTensor(train_x[j]))
                                 .AddInput(Y, new TFTensor(train_y[j]))
-                                .AddTarget(updateOps).Fetch(cost, W.Read, b.Read, pred).Run();
+                                .AddTarget(updateOps).Fetch(cost, readW, readb, pred).Run();
                             var output = $"loss: {tensors[0].GetValue():F4}, W: {tensors[1].GetValue():F4}, b: {tensors[2].GetValue():F4}";
                             Assert.Equal(expectedLines[i * n_samples + j], output);
                         }

Original file line number	Diff line number	Diff line change
`@@ -368,7 +368,7 @@ public override TFOperation[] ApplyGradient((TFOutput gradient, Variable variabl`
`368`	`368`	`for (int i = 0; i < gradientsAndVariables.Length; i++)`
`369`	`369`	`{`
`370`	`370`	`var gv = gradientsAndVariables[i];`
`371`		`- var lr = _graph.Cast(LearningRate.Read, gv.gradient.OutputType);`
	`371`	`+ var lr = _graph.Cast(LearningRate, gv.gradient.OutputType);`
`372`	`372`	`var one = _graph.Const(1f);`
`373`	`373`
`374`	`374`	`var t = _graph.Cast(Iterations.Read, _beta1.OutputType);`