The article is about a short introduction on how to train a first Model with Keras.NET.
Introduction
Since machine learning has become very popular, using open source SDKs like Keras and/or as backend Tensorflow, the language Python has become popular too again (as opposed to the forecast, that Python will be dead in the next five years). As a Research Engineer in the .NET environment, it creates a lot of problems for me, using Python there and my main focus is on C#. Of course, you can somehow embed Python code to your application (i.e., run a Python script within your .NET application), but this doesn't make it much more better.
Another solution, I thought, would be using IronPython, an SDK to run directly Python code, but
- it is still using Python 2.7 (with end of support in 2020), and support for Python 3 is pronounced like "DO NOT USE!", ok...
- it doesn't support a lot of additional packages like numpy which is essential to do machine learning things
- it's very hard to fix all this issues... just don't use it for purposes like this.
So I started to figure new solutions out and how to avoid using additional Python environments, that had to be called extra while the application is started, in a .NET Core application and get to know about Keras.NET (https://github.com/SciSharp/Keras.NET) from SciSharp STACK. To be clear, I don't care if a SDK is using Python embedded, but I don't want to code my machine learning prototypes in Python, so this is the game changer here.
Background
The documentation of Keras.NET is very short, and if you need further knowledge, they linked to the original Keras documentation. This is very useful for the understanding of how this Framework is working. But it is big pain to figure out how the SDK works in C#.
Here is the documentation link: https://scisharp.github.io/Keras.NET/
Please have a look at the prerequisites (where you get to know, that you have to install Python as well).
And here, you will find the API Documentation: https://scisharp.github.io/Keras.NET/api/index.html
To understand what is happening here, you need to understand the way in which Keras is working, what's the purposes of a Dense Layer, Epochs and so on.
Using the Code
The article from Arnaldo P. Castaño inspires me to show a second example using existing datasets and how to train them using Keras.NET. So this is something about using Keras.NET to see some difference than using Keras (in Python) and maybe someone can find this very useful.
First, you will need the Nuget Keras.NET. Using the package Manager in Visual Studio, it goes like:
PM> Install-Package Keras.NET -Version 3.8.4.4
Besides this, you will need to install Keras and Tensorflow for Python using the pip installer in the windows CLI or Powershell:
pip install keras
pip install tensorflow
It has to look like within the Windows-CLI:
C:\Users\YOURNAME>pip install keras
If you have trouble with the pip installer, please check if you have set up Python to the System environment. There is a checkbox, called 'Add Python to PATH' on the bottom of the install screen, which is very important.
If this doesn't help, you probably have to set the path by yourself (there are tones of How-To in the internet, how to fix pip, and once you fixed this issue, you are sure that Python is set up correctly).
And finally, you will need the training and test sets: https://github.com/zalandoresearch/fashion-mnist#get-the-data.
First, I stored the data locally and unzipped them, because I didn't want to make an other function for unzipping purposes (which is actually not hard to implement, but I want to stay on focus here). It is just one data. Using the code below (and call the openDatas
function, you will need to unzip this, otherwise the function can't read the data. Put the four datas (2x images, 2x lables...train and test).
Second, I figure in the API-Doc out, that there is already implement a function to load the datas directly, see: https://scisharp.github.io/Keras.NET/api/Keras.Datasets.FashionMNIST.html
So here we go: running the first training of the model for further purposes.
The entry point of my .NET-Core is very simple. I made a class (KerasClass
) for my training and just call them from the Main
function:
using System;
namespace Keras.net_and_fashion_mnist
{
class Program
{
static void Main(string[] args)
{
KerasClass keras = new KerasClass();
keras.TrainModel();
}
}
}
The KerasClass
is much more interesting:
using Keras.Datasets;
using Keras.Layers;
using Keras.Models;
using Keras.Utils;
using Numpy;
using System;
using System.IO;
using System.Linq;
namespace Keras.net_and_fashion_mnist
{
class KerasClass
{
public void TrainModel()
{
int batch_size = 1000;
int num_classes = 10;
int epochs = 30;
int img_rows = 28, img_cols = 28;
var ((x_train, y_train), (x_test, y_test)) =
FashionMNIST.LoadData();
x_train.reshape(-1, img_rows, img_cols).astype(np.float32);
y_train = Util.ToCategorical(y_train, num_classes);
y_test = Util.ToCategorical(y_test, num_classes);
var model = new Sequential();
model.Add(new Dense(100, 784, "sigmoid"));
model.Add(new Dense(10, null, "sigmoid"));
model.Compile(optimizer: "sgd", loss: "categorical_crossentropy",
metrics: new string[] { "accuracy" });
var X_train = x_train.reshape(60000, 784);
var X_test = x_test.reshape(10000, 784);
model.Fit(X_train, y_train, batch_size, epochs, 1);
Console.WriteLine("---------------------");
Console.WriteLine(X_train.shape);
Console.WriteLine(X_test.shape);
Console.WriteLine(y_train[0]);
Console.WriteLine(y_train[1]);
var y_train_pred = model.Predict(X_train);
Console.WriteLine(y_train_pred);
model.Evaluate(X_test.reshape(-1, 784), y_test);
}
private byte[] openDatas(string path, int skip)
{
var file = File.ReadAllBytes(path).Skip(skip).ToArray();
return file;
}
}
}
Points of Interest
Using this code will provide your first training of your own model. For me, this was my first step to Keras.NET and I want to share this, because there are not that many examples of Keras.NET. Maybe you can use this for your purposes.
History
- 19th November, 2020: Initial release
- 20th November, 2020: Removed two images
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