Merge branch 'master' of https://github.com/SciSharp/TensorFlow.NET

Author: Oceania2018

README.md

@@ -33,6 +33,14 @@ In comparison to other projects, like for instance [TensorFlowSharp](https://www
 | tf.net 0.15               | x             | x              |               |
 | tf.net 0.14               | x             |                |               |
 
+Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflownet.readthedocs.io/en/latest/FrontCover.html).
+
+There are many examples reside at [TensorFlow.NET Examples](https://github.com/SciSharp/TensorFlow.NET-Examples).
+
+Troubleshooting of running example or installation, please  refer [here](tensorflowlib/README.md).
+
+#### C# Example
+
 Install TF.NET and TensorFlow binary through NuGet.
 ```sh
 ### install tensorflow C#/F# binding
@@ -63,6 +71,15 @@ int training_steps = 1000;
 float learning_rate = 0.01f;
 int display_step = 100;
 
+// Sample data
+NDArray train_X, train_Y;
+int n_samples;
+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);
+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);
+n_samples = train_X.shape[0];
+
 // We can set a fixed init value in order to demo
 var W = tf.Variable(-0.06f, name: "weight");
 var b = tf.Variable(-0.73f, name: "bias");
@@ -142,11 +159,65 @@ model.fit(x_train[new Slice(0, 1000)], y_train[new Slice(0, 1000)],
           validation_split: 0.2f);
 ```
 
-Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflownet.readthedocs.io/en/latest/FrontCover.html).
+#### F# Example
 
-There are many examples reside at [TensorFlow.NET Examples](https://github.com/SciSharp/TensorFlow.NET-Examples).
+Linear Regression in `Eager` mode:
+
+```fsharp
+#r "nuget: TensorFlow.Net"
+#r "nuget: TensorFlow.Keras"
+#r "nuget: SciSharp.TensorFlow.Redist"
+#r "nuget: NumSharp"
+
+open NumSharp
+open Tensorflow
+open type Tensorflow.Binding
+open type Tensorflow.KerasApi
+
+let tf = New<tensorflow>()
+tf.enable_eager_execution()
+
+// Parameters
+let training_steps = 1000
+let learning_rate = 0.01f
+let display_step = 100
+
+// Sample data
+let 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)
+let 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)
+let n_samples = train_X.shape.[0]
+
+// We can set a fixed init value in order to demo
+let W = tf.Variable(-0.06f,name = "weight")
+let b = tf.Variable(-0.73f, name = "bias")
+let optimizer = keras.optimizers.SGD(learning_rate)
+
+// Run training for the given number of steps.
+for step = 1 to  (training_steps + 1) do 
+    // Run the optimization to update W and b values.
+    // Wrap computation inside a GradientTape for automatic differentiation.
+    use g = tf.GradientTape()
+    // Linear regression (Wx + b).
+    let pred = W * train_X + b
+    // Mean square error.
+    let loss = tf.reduce_sum(tf.pow(pred - train_Y,2)) / (2 * n_samples)
+    // should stop recording
+    // compute gradients
+    let gradients = g.gradient(loss,struct (W,b))
+
+    // Update W and b following gradients.
+    optimizer.apply_gradients(zip(gradients, struct (W,b)))
+
+    if (step % display_step) = 0 then
+        let pred = W * train_X + b
+        let loss = tf.reduce_sum(tf.pow(pred-train_Y,2)) / (2 * n_samples)
+        printfn $"step: {step}, loss: {loss.numpy()}, W: {W.numpy()}, b: {b.numpy()}"
+```
 
-Troubleshooting of running example or installation, please  refer [here](tensorflowlib/README.md).
 
 ### Contribute:
 

test/TensorFlowNET.UnitTest/ManagedAPI/ControlFlowApiTest.cs

@@ -1,4 +1,5 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
+using NumSharp;
 using System;
 using Tensorflow;
 using static Tensorflow.Binding;
@@ -45,5 +46,23 @@ public void WhileLoopGraphMode()
             var r = tf.while_loop(c, b, i);
             Assert.AreEqual(10, (int)r);
         }
+
+
+        [TestMethod, Ignore]
+        public void ScanFunctionGraphMode()
+        {
+            tf.compat.v1.disable_eager_execution();
+
+            Func<Tensor, Tensor, Tensor> fn = (prev, current) => tf.add(prev, current);
+            var input = tf.placeholder(TF_DataType.TF_FLOAT, new TensorShape(6));
+            var scan = tf.scan(fn, input);
+
+            using (var sess = tf.Session())
+            {
+                sess.run(tf.global_variables_initializer());
+                var result = sess.run(scan, new FeedItem(input, np.array(1, 2, 3, 4, 5, 6)));
+                Assert.AreEqual(new float[] { 1, 3, 6, 10, 15, 21 }, result.ToArray<float>());
+            }
+        }
     }
 }