# Keras.NET

**Repository Path**: deep-learing_admin/Keras.NET

## Basic Information

- **Project Name**: Keras.NET
- **Description**: Keras.NET is a high-level neural networks API for C# and F#, with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano. 
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-01-09
- **Last Updated**: 2021-08-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

![Logo](Images/keras.net_long.svg)<a href="http://scisharpstack.org"><img src="https://github.com/SciSharp/SciSharp/blob/master/art/scisharp_badge.png" width="200" height="200" align="right" /></a>

**Keras.NET** is a high-level neural networks API for C# and F# via a Python binding and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that:

* Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).

* Supports both convolutional networks and recurrent networks, as well as combinations of the two.

* Runs seamlessly on CPU and GPU.

## Keras.NET is using:

* [Numpy.NET](https://github.com/SciSharp/Numpy.NET)

* [pythonnet_netstandard](https://github.com/henon/pythonnet_netstandard)

## Prerequisite

* Python 2.7 - 3.7, Link: https://www.python.org/downloads/

* Install keras, [numpy](https://numpy.org/install/) and one of the backends (Tensorflow/CNTK/Theano). Keras is now bundled with Tensorflow 2.0, so the easiest way to install Keras and Tensorflow at the same time is to simply install [Tensorflow 2.0](https://www.tensorflow.org/install).

## Nuget

Install from nuget: https://www.nuget.org/packages/Keras.NET

```
dotnet add package Keras.NET
```


## Example with XOR sample (C#)

```csharp
//Load train data
NDarray x = np.array(new float[,] { { 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 } });
NDarray y = np.array(new float[] { 0, 1, 1, 0 });

//Build sequential model
var model = new Sequential();
model.Add(new Dense(32, activation: "relu", input_shape: new Shape(2)));
model.Add(new Dense(64, activation: "relu"));
model.Add(new Dense(1, activation: "sigmoid"));

//Compile and train
model.Compile(optimizer:"sgd", loss:"binary_crossentropy", metrics: new string[] { "accuracy" });
model.Fit(x, y, batch_size: 2, epochs: 1000, verbose: 1);

//Save model and weights
string json = model.ToJson();
File.WriteAllText("model.json", json);
model.SaveWeight("model.h5");

//Load model and weight
var loaded_model = Sequential.ModelFromJson(File.ReadAllText("model.json"));
loaded_model.LoadWeight("model.h5");
```

**Output:**

![](https://raw.githubusercontent.com/SciSharp/Keras.NET/master/Images/XOR_Output.PNG)

## MNIST CNN Example (C#)

Python example taken from: https://keras.io/examples/mnist_cnn/

```csharp
int batch_size = 128;
int num_classes = 10;
int epochs = 12;

// input image dimensions
int img_rows = 28, img_cols = 28;

Shape input_shape = null;

// the data, split between train and test sets
var ((x_train, y_train), (x_test, y_test)) = MNIST.LoadData();

if(Backend.ImageDataFormat() == "channels_first")
{
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols);
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols);
    input_shape = (1, img_rows, img_cols);
}
else
{
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1);
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1);
    input_shape = (img_rows, img_cols, 1);
}

x_train = x_train.astype(np.float32);
x_test = x_test.astype(np.float32);
x_train /= 255;
x_test /= 255;
Console.WriteLine($"x_train shape: {x_train.shape}");
Console.WriteLine($"{x_train.shape[0]} train samples");
Console.WriteLine($"{x_test.shape[0]} test samples");

// convert class vectors to binary class matrices
y_train = Util.ToCategorical(y_train, num_classes);
y_test = Util.ToCategorical(y_test, num_classes);

// Build CNN model
var model = new Sequential();
model.Add(new Conv2D(32, kernel_size: (3, 3).ToTuple(),
                        activation: "relu",
                        input_shape: input_shape));
model.Add(new Conv2D(64, (3, 3).ToTuple(), activation: "relu"));
model.Add(new MaxPooling2D(pool_size: (2, 2).ToTuple()));
model.Add(new Dropout(0.25));
model.Add(new Flatten());
model.Add(new Dense(128, activation: "relu"));
model.Add(new Dropout(0.5));
model.Add(new Dense(num_classes, activation: "softmax"));

model.Compile(loss: "categorical_crossentropy",
    optimizer: new Adadelta(), metrics: new string[] { "accuracy" });

model.Fit(x_train, y_train,
            batch_size: batch_size,
            epochs: epochs,
            verbose: 1,
            validation_data: new NDarray[] { x_test, y_test });
var score = model.Evaluate(x_test, y_test, verbose: 0);
Console.WriteLine($"Test loss: {score[0]}");
Console.WriteLine($"Test accuracy: {score[1]}");
```

**Output**

Reached 98% accuracy within 3 epoches.

![](https://raw.githubusercontent.com/SciSharp/Keras.NET/master/Images/MNIST_Output.PNG)

# Documentation

https://scisharp.github.io/Keras.NET/

![SciSharp](https://avatars3.githubusercontent.com/u/44989469)