doc updated.

v0.4.0 released.

docs/source/LinearRegression.md

@@ -0,0 +1,32 @@
+# Chapter. Linear Regression
+
+```csharp
+// Prepare training Data
+var train_X = np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f, 7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f);
+var train_Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f, 2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f);
+var n_samples = train_X.shape[0];
+```
+
+```csharp
+// tf Graph Input
+var X = tf.placeholder(tf.float32);
+var Y = tf.placeholder(tf.float32);
+
+// Set model weights 
+// We can set a fixed init value in order to debug
+// var rnd1 = rng.randn<float>();
+// var rnd2 = rng.randn<float>();
+var W = tf.Variable(-0.06f, name: "weight");
+var b = tf.Variable(-0.73f, name: "bias");
+
+// Construct a linear model
+var pred = tf.add(tf.multiply(X, W), b);
+
+// Mean squared error
+var cost = tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * n_samples);
+
+// Gradient descent
+// Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
+var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);
+```
+

docs/source/index.rst

@@ -28,4 +28,5 @@ Welcome to TensorFlow.NET's documentation!
    Gradient
    Train
    EagerMode
-   ImageRecognition
+   ImageRecognition
+   LinearRegression

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -4,7 +4,7 @@
     <TargetFramework>netstandard2.0</TargetFramework>
     <AssemblyName>TensorFlow.NET</AssemblyName>
     <RootNamespace>Tensorflow</RootNamespace>
-    <Version>0.3.0</Version>
+    <Version>0.4.0</Version>
     <Authors>Haiping Chen</Authors>
     <Company>SciSharp STACK</Company>
     <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
@@ -16,12 +16,11 @@
     <PackageTags>TensorFlow, NumSharp, SciSharp, MachineLearning, TensorFlow.NET</PackageTags>
     <Description>Google's TensorFlow binding in .NET Standard.
 Docs: https://tensorflownet.readthedocs.io</Description>
-    <AssemblyVersion>0.3.0.0</AssemblyVersion>
-    <PackageReleaseNotes>Added image prediction example.
-Upgraded to TensorFlow 1.13 RC2.
+    <AssemblyVersion>0.4.0.0</AssemblyVersion>
+    <PackageReleaseNotes>Added Linear Regression example.
 </PackageReleaseNotes>
     <LangVersion>7.2</LangVersion>
-    <FileVersion>0.3.0.0</FileVersion>
+    <FileVersion>0.4.0.0</FileVersion>
   </PropertyGroup>
 
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">

test/TensorFlowNET.Examples/LinearRegression.cs

@@ -28,8 +28,6 @@ namespace TensorFlowNET.Examples
                          2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f);
             var n_samples = train_X.shape[0];
 
-            var graph = tf.Graph().as_default();
-
             // tf Graph Input
             var X = tf.placeholder(tf.float32);
             var Y = tf.placeholder(tf.float32);
@@ -47,7 +45,7 @@ namespace TensorFlowNET.Examples
             // Mean squared error
             var cost = tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * n_samples);
 
-            // radient descent
+            // Gradient descent
             // Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
             var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);
 
@@ -55,7 +53,7 @@ namespace TensorFlowNET.Examples
             var init = tf.global_variables_initializer();
 
             // Start training
-            with<Session>(tf.Session(graph), sess => 
+            with<Session>(tf.Session(), sess => 
             {
                 // Run the initializer
                 sess.run(init);