diff --git a/README.md b/README.md
index f067f9a4..7f27b42c 100644
--- a/README.md
+++ b/README.md
@@ -26,19 +26,17 @@ In comparison to other projects, like for instance [TensorFlowSharp](https://www
### How to use
-| TensorFlow | tf native1.14 | tf native 1.15 | tf native 2.3 |
-| ------------------------- | ------------- | -------------- | ------------- |
-| tf.net 0.30, tf.keras 0.1 | | | x |
-| tf.net 0.20 | | x | x |
-| 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).
+| TensorFlow | tf native1.14 | tf native 1.15 | tf native 2.3 |
+| -------------------------- | ------------- | -------------- | ------------- |
+| tf.net 0.3x, tf.keras 0.2x | | | x |
+| tf.net 0.2x | | x | x |
+| tf.net 0.1x | x | x | |
+| tf.net 0.1x | x | | |
Troubleshooting of running example or installation, please refer [here](tensorflowlib/README.md).
+There are many examples reside at [TensorFlow.NET Examples](https://github.com/SciSharp/TensorFlow.NET-Examples) written in C# and F#.
+
#### C# Example
Install TF.NET and TensorFlow binary through NuGet.
@@ -67,18 +65,16 @@ Linear Regression in `Eager` mode:
```c#
// Parameters
-int training_steps = 1000;
-float learning_rate = 0.01f;
-int display_step = 100;
+var training_steps = 1000;
+var learning_rate = 0.01f;
+var 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,
+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);
-train_Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f,
+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);
-n_samples = train_X.shape[0];
+var 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");
@@ -218,6 +214,7 @@ for step = 1 to (training_steps + 1) do
printfn $"step: {step}, loss: {loss.numpy()}, W: {W.numpy()}, b: {b.numpy()}"
```
+Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflownet.readthedocs.io/en/latest/FrontCover.html) if you want to know more about TensorFlow for .NET under the hood.
### Contribute:
@@ -249,9 +246,7 @@ git pull upstream master
### Contact
-Feel free to star or raise issue on [Github](https://github.com/SciSharp/TensorFlow.NET).
-
-Follow us on [Medium](https://medium.com/scisharp).
+Follow us on [Twitter](https://twitter.com/ScisharpStack), [Facebook](https://www.facebook.com/scisharp.stack.9), [Medium](https://medium.com/scisharp), [LinkedIn](https://www.linkedin.com/company/scisharp-stack/).
Join our chat on [Gitter](https://gitter.im/sci-sharp/community).
diff --git a/src/TensorFlowNET.Keras/Open.snk b/src/TensorFlowNET.Keras/Open.snk
new file mode 100644
index 00000000..22a3cbd2
Binary files /dev/null and b/src/TensorFlowNET.Keras/Open.snk differ
diff --git a/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj b/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj
index b99d9be4..616467ff 100644
--- a/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj
+++ b/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj
@@ -17,7 +17,8 @@
Keras for .NET is a C# version of Keras ported from the python version.
* Support CIFAR-10 dataset in keras.datasets.
-* Support Conv2D functional API.
+* Support Conv2D functional API.
+* Support BatchNormalization layer.
Keras for .NET
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages.
@@ -26,6 +27,8 @@ Keras is an API designed for human beings, not machines. Keras follows best prac
tensorflow, keras, deep learning, machine learning
false
Git
+ true
+ Open.snk