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Keras.NET is a high-level neural networks API, written in C# with 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:

Prerequisite

Nuget

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

Install-Package Keras.NET

dotnet add package Keras.NET

Example with XOR sample

//Load train dataNDarrayx=np.array(newfloat[,]{{0,0},{0,1},{1,0},{1,1}});NDarrayy=np.array(newfloat[]{0,1,1,0});//Build sequential modelvarmodel=newSequential();model.Add(newDense(32,activation:"relu",input_shape:newShape(2)));model.Add(newDense(64,activation:"relu"));model.Add(newDense(1,activation:"sigmoid"));//Compile and trainmodel.Compile(optimizer:"sgd",loss:"binary_crossentropy",metrics:newstring[]{"accuracy"});model.Fit(x,y,batch_size:2,epochs:1000,verbose:1);//Save model and weightsstringjson=model.ToJson();File.WriteAllText("model.json",json);model.SaveWeight("model.h5");//Load model and weightvarloaded_model=Sequential.ModelFromJson(File.ReadAllText("model.json"));loaded_model.LoadWeight("model.h5");

Output:

MNIST CNN Example

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

intbatch_size=128;intnum_classes=10;intepochs=12;// input image dimensionsintimg_rows=28,img_cols=28;Shapeinput_shape=null;// the data, split between train and test setsvar((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($"{x_test.shape[0]} test samples");// convert class vectors to binary class matricesy_train=Util.ToCategorical(y_train,num_classes);y_test=Util.ToCategorical(y_test,num_classes);// Build CNN modelvarmodel=newSequential();model.Add(newConv2D(32,kernel_size:(3,3).ToTuple(),activation:"relu",input_shape:input_shape));model.Add(newConv2D(64,(3,3).ToTuple(),activation:"relu"));model.Add(newMaxPooling2D(pool_size:(2,2).ToTuple()));model.Add(newDropout(0.25));model.Add(newFlatten());model.Add(newDense(128,activation:"relu"));model.Add(newDropout(0.5));model.Add(newDense(num_classes,activation:"softmax"));model.Compile(loss:"categorical_crossentropy",optimizer:newAdadelta(),metrics:newstring[]{"accuracy"});model.Fit(x_train,y_train,batch_size:batch_size,epochs:epochs,verbose:1,validation_data:newNDarray[]{x_test,y_test});varscore=model.Evaluate(x_test,y_test,verbose:0);Console.WriteLine($"Test loss: {score[0]}");Console.WriteLine($"Test accuracy: {score[1]}");

Output

Reached 98% accuracy within 3 epoches.

Documentation

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

SciSharp

About

Keras.NET is a high-level neural networks API, written in C# with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano.

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Skip to content
BranchesTags
Repository files navigation
Logo
Keras.NET is a high-level neural networks API, written in C# with 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:
Prerequisite
Nuget
Install from nuget: https://www.nuget.org/packages/Keras.NET
Install-Package Keras.NET

dotnet add package Keras.NET

Example with XOR sample
//Load train dataNDarrayx=np.array(newfloat[,]{{0,0},{0,1},{1,0},{1,1}});NDarrayy=np.array(newfloat[]{0,1,1,0});//Build sequential modelvarmodel=newSequential();model.Add(newDense(32,activation:"relu",input_shape:newShape(2)));model.Add(newDense(64,activation:"relu"));model.Add(newDense(1,activation:"sigmoid"));//Compile and trainmodel.Compile(optimizer:"sgd",loss:"binary_crossentropy",metrics:newstring[]{"accuracy"});model.Fit(x,y,batch_size:2,epochs:1000,verbose:1);//Save model and weightsstringjson=model.ToJson();File.WriteAllText("model.json",json);model.SaveWeight("model.h5");//Load model and weightvarloaded_model=Sequential.ModelFromJson(File.ReadAllText("model.json"));loaded_model.LoadWeight("model.h5");
Output:
MNIST CNN Example
Python example taken from: https://keras.io/examples/mnist_cnn/
intbatch_size=128;intnum_classes=10;intepochs=12;// input image dimensionsintimg_rows=28,img_cols=28;Shapeinput_shape=null;// the data, split between train and test setsvar((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($"{x_test.shape[0]} test samples");// convert class vectors to binary class matricesy_train=Util.ToCategorical(y_train,num_classes);y_test=Util.ToCategorical(y_test,num_classes);// Build CNN modelvarmodel=newSequential();model.Add(newConv2D(32,kernel_size:(3,3).ToTuple(),activation:"relu",input_shape:input_shape));model.Add(newConv2D(64,(3,3).ToTuple(),activation:"relu"));model.Add(newMaxPooling2D(pool_size:(2,2).ToTuple()));model.Add(newDropout(0.25));model.Add(newFlatten());model.Add(newDense(128,activation:"relu"));model.Add(newDropout(0.5));model.Add(newDense(num_classes,activation:"softmax"));model.Compile(loss:"categorical_crossentropy",optimizer:newAdadelta(),metrics:newstring[]{"accuracy"});model.Fit(x_train,y_train,batch_size:batch_size,epochs:epochs,verbose:1,validation_data:newNDarray[]{x_test,y_test});varscore=model.Evaluate(x_test,y_test,verbose:0);Console.WriteLine($"Test loss: {score[0]}");Console.WriteLine($"Test accuracy: {score[1]}");
Output
Reached 98% accuracy within 3 epoches.
Documentation
https://scisharp.github.io/Keras.NET/
SciSharp
About
Keras.NET is a high-level neural networks API, written in C# with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano. 
Resources
Readme
MIT license
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Skip to content
BranchesTags
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Logo
Keras.NET is a high-level neural networks API, written in C# with 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:
Prerequisite
Nuget
Install from nuget: https://www.nuget.org/packages/Keras.NET
Install-Package Keras.NET

dotnet add package Keras.NET

Example with XOR sample
//Load train dataNDarrayx=np.array(newfloat[,]{{0,0},{0,1},{1,0},{1,1}});NDarrayy=np.array(newfloat[]{0,1,1,0});//Build sequential modelvarmodel=newSequential();model.Add(newDense(32,activation:"relu",input_shape:newShape(2)));model.Add(newDense(64,activation:"relu"));model.Add(newDense(1,activation:"sigmoid"));//Compile and trainmodel.Compile(optimizer:"sgd",loss:"binary_crossentropy",metrics:newstring[]{"accuracy"});model.Fit(x,y,batch_size:2,epochs:1000,verbose:1);//Save model and weightsstringjson=model.ToJson();File.WriteAllText("model.json",json);model.SaveWeight("model.h5");//Load model and weightvarloaded_model=Sequential.ModelFromJson(File.ReadAllText("model.json"));loaded_model.LoadWeight("model.h5");
Output:
MNIST CNN Example
Python example taken from: https://keras.io/examples/mnist_cnn/
intbatch_size=128;intnum_classes=10;intepochs=12;// input image dimensionsintimg_rows=28,img_cols=28;Shapeinput_shape=null;// the data, split between train and test setsvar((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($"{x_test.shape[0]} test samples");// convert class vectors to binary class matricesy_train=Util.ToCategorical(y_train,num_classes);y_test=Util.ToCategorical(y_test,num_classes);// Build CNN modelvarmodel=newSequential();model.Add(newConv2D(32,kernel_size:(3,3).ToTuple(),activation:"relu",input_shape:input_shape));model.Add(newConv2D(64,(3,3).ToTuple(),activation:"relu"));model.Add(newMaxPooling2D(pool_size:(2,2).ToTuple()));model.Add(newDropout(0.25));model.Add(newFlatten());model.Add(newDense(128,activation:"relu"));model.Add(newDropout(0.5));model.Add(newDense(num_classes,activation:"softmax"));model.Compile(loss:"categorical_crossentropy",optimizer:newAdadelta(),metrics:newstring[]{"accuracy"});model.Fit(x_train,y_train,batch_size:batch_size,epochs:epochs,verbose:1,validation_data:newNDarray[]{x_test,y_test});varscore=model.Evaluate(x_test,y_test,verbose:0);Console.WriteLine($"Test loss: {score[0]}");Console.WriteLine($"Test accuracy: {score[1]}");
Output
Reached 98% accuracy within 3 epoches.
Documentation
https://scisharp.github.io/Keras.NET/
SciSharp
About
Keras.NET is a high-level neural networks API, written in C# with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano. 
Resources
Readme
MIT license
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Skip to content
BranchesTags
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Logo
Keras.NET is a high-level neural networks API, written in C# with 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:
Prerequisite
Nuget
Install from nuget: https://www.nuget.org/packages/Keras.NET
Install-Package Keras.NET

dotnet add package Keras.NET

Example with XOR sample
//Load train dataNDarrayx=np.array(newfloat[,]{{0,0},{0,1},{1,0},{1,1}});NDarrayy=np.array(newfloat[]{0,1,1,0});//Build sequential modelvarmodel=newSequential();model.Add(newDense(32,activation:"relu",input_shape:newShape(2)));model.Add(newDense(64,activation:"relu"));model.Add(newDense(1,activation:"sigmoid"));//Compile and trainmodel.Compile(optimizer:"sgd",loss:"binary_crossentropy",metrics:newstring[]{"accuracy"});model.Fit(x,y,batch_size:2,epochs:1000,verbose:1);//Save model and weightsstringjson=model.ToJson();File.WriteAllText("model.json",json);model.SaveWeight("model.h5");//Load model and weightvarloaded_model=Sequential.ModelFromJson(File.ReadAllText("model.json"));loaded_model.LoadWeight("model.h5");
Output:
MNIST CNN Example
Python example taken from: https://keras.io/examples/mnist_cnn/
intbatch_size=128;intnum_classes=10;intepochs=12;// input image dimensionsintimg_rows=28,img_cols=28;Shapeinput_shape=null;// the data, split between train and test setsvar((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($"{x_test.shape[0]} test samples");// convert class vectors to binary class matricesy_train=Util.ToCategorical(y_train,num_classes);y_test=Util.ToCategorical(y_test,num_classes);// Build CNN modelvarmodel=newSequential();model.Add(newConv2D(32,kernel_size:(3,3).ToTuple(),activation:"relu",input_shape:input_shape));model.Add(newConv2D(64,(3,3).ToTuple(),activation:"relu"));model.Add(newMaxPooling2D(pool_size:(2,2).ToTuple()));model.Add(newDropout(0.25));model.Add(newFlatten());model.Add(newDense(128,activation:"relu"));model.Add(newDropout(0.5));model.Add(newDense(num_classes,activation:"softmax"));model.Compile(loss:"categorical_crossentropy",optimizer:newAdadelta(),metrics:newstring[]{"accuracy"});model.Fit(x_train,y_train,batch_size:batch_size,epochs:epochs,verbose:1,validation_data:newNDarray[]{x_test,y_test});varscore=model.Evaluate(x_test,y_test,verbose:0);Console.WriteLine($"Test loss: {score[0]}");Console.WriteLine($"Test accuracy: {score[1]}");
Output
Reached 98% accuracy within 3 epoches.
Documentation
https://scisharp.github.io/Keras.NET/
SciSharp
About
Keras.NET is a high-level neural networks API, written in C# with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano. 
Resources
Readme
MIT license
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Skip to content
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Keras.NET is a high-level neural networks API, written in C# with 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:
Prerequisite
Nuget
Install from nuget: https://www.nuget.org/packages/Keras.NET
Install-Package Keras.NET

dotnet add package Keras.NET

Example with XOR sample
//Load train dataNDarrayx=np.array(newfloat[,]{{0,0},{0,1},{1,0},{1,1}});NDarrayy=np.array(newfloat[]{0,1,1,0});//Build sequential modelvarmodel=newSequential();model.Add(newDense(32,activation:"relu",input_shape:newShape(2)));model.Add(newDense(64,activation:"relu"));model.Add(newDense(1,activation:"sigmoid"));//Compile and trainmodel.Compile(optimizer:"sgd",loss:"binary_crossentropy",metrics:newstring[]{"accuracy"});model.Fit(x,y,batch_size:2,epochs:1000,verbose:1);//Save model and weightsstringjson=model.ToJson();File.WriteAllText("model.json",json);model.SaveWeight("model.h5");//Load model and weightvarloaded_model=Sequential.ModelFromJson(File.ReadAllText("model.json"));loaded_model.LoadWeight("model.h5");
Output:
MNIST CNN Example
Python example taken from: https://keras.io/examples/mnist_cnn/
intbatch_size=128;intnum_classes=10;intepochs=12;// input image dimensionsintimg_rows=28,img_cols=28;Shapeinput_shape=null;// the data, split between train and test setsvar((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($"{x_test.shape[0]} test samples");// convert class vectors to binary class matricesy_train=Util.ToCategorical(y_train,num_classes);y_test=Util.ToCategorical(y_test,num_classes);// Build CNN modelvarmodel=newSequential();model.Add(newConv2D(32,kernel_size:(3,3).ToTuple(),activation:"relu",input_shape:input_shape));model.Add(newConv2D(64,(3,3).ToTuple(),activation:"relu"));model.Add(newMaxPooling2D(pool_size:(2,2).ToTuple()));model.Add(newDropout(0.25));model.Add(newFlatten());model.Add(newDense(128,activation:"relu"));model.Add(newDropout(0.5));model.Add(newDense(num_classes,activation:"softmax"));model.Compile(loss:"categorical_crossentropy",optimizer:newAdadelta(),metrics:newstring[]{"accuracy"});model.Fit(x_train,y_train,batch_size:batch_size,epochs:epochs,verbose:1,validation_data:newNDarray[]{x_test,y_test});varscore=model.Evaluate(x_test,y_test,verbose:0);Console.WriteLine($"Test loss: {score[0]}");Console.WriteLine($"Test accuracy: {score[1]}");
Output
Reached 98% accuracy within 3 epoches.
Documentation
https://scisharp.github.io/Keras.NET/
SciSharp
About
Keras.NET is a high-level neural networks API, written in C# with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano. 
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Keras.NET is a high-level neural networks API, written in C# with 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:
Prerequisite
Nuget
Install from nuget: https://www.nuget.org/packages/Keras.NET
Install-Package Keras.NET

dotnet add package Keras.NET

Example with XOR sample
//Load train dataNDarrayx=np.array(newfloat[,]{{0,0},{0,1},{1,0},{1,1}});NDarrayy=np.array(newfloat[]{0,1,1,0});//Build sequential modelvarmodel=newSequential();model.Add(newDense(32,activation:"relu",input_shape:newShape(2)));model.Add(newDense(64,activation:"relu"));model.Add(newDense(1,activation:"sigmoid"));//Compile and trainmodel.Compile(optimizer:"sgd",loss:"binary_crossentropy",metrics:newstring[]{"accuracy"});model.Fit(x,y,batch_size:2,epochs:1000,verbose:1);//Save model and weightsstringjson=model.ToJson();File.WriteAllText("model.json",json);model.SaveWeight("model.h5");//Load model and weightvarloaded_model=Sequential.ModelFromJson(File.ReadAllText("model.json"));loaded_model.LoadWeight("model.h5");
Output:
MNIST CNN Example
Python example taken from: https://keras.io/examples/mnist_cnn/
intbatch_size=128;intnum_classes=10;intepochs=12;// input image dimensionsintimg_rows=28,img_cols=28;Shapeinput_shape=null;// the data, split between train and test setsvar((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($"{x_test.shape[0]} test samples");// convert class vectors to binary class matricesy_train=Util.ToCategorical(y_train,num_classes);y_test=Util.ToCategorical(y_test,num_classes);// Build CNN modelvarmodel=newSequential();model.Add(newConv2D(32,kernel_size:(3,3).ToTuple(),activation:"relu",input_shape:input_shape));model.Add(newConv2D(64,(3,3).ToTuple(),activation:"relu"));model.Add(newMaxPooling2D(pool_size:(2,2).ToTuple()));model.Add(newDropout(0.25));model.Add(newFlatten());model.Add(newDense(128,activation:"relu"));model.Add(newDropout(0.5));model.Add(newDense(num_classes,activation:"softmax"));model.Compile(loss:"categorical_crossentropy",optimizer:newAdadelta(),metrics:newstring[]{"accuracy"});model.Fit(x_train,y_train,batch_size:batch_size,epochs:epochs,verbose:1,validation_data:newNDarray[]{x_test,y_test});varscore=model.Evaluate(x_test,y_test,verbose:0);Console.WriteLine($"Test loss: {score[0]}");Console.WriteLine($"Test accuracy: {score[1]}");
Output
Reached 98% accuracy within 3 epoches.
Documentation
https://scisharp.github.io/Keras.NET/
SciSharp
About
Keras.NET is a high-level neural networks API, written in C# with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano. 
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MIT license
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Skip to content
BranchesTags
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Logo
Keras.NET is a high-level neural networks API, written in C# with 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:
Prerequisite
Nuget
Install from nuget: https://www.nuget.org/packages/Keras.NET
Install-Package Keras.NET

dotnet add package Keras.NET

Example with XOR sample
//Load train dataNDarrayx=np.array(newfloat[,]{{0,0},{0,1},{1,0},{1,1}});NDarrayy=np.array(newfloat[]{0,1,1,0});//Build sequential modelvarmodel=newSequential();model.Add(newDense(32,activation:"relu",input_shape:newShape(2)));model.Add(newDense(64,activation:"relu"));model.Add(newDense(1,activation:"sigmoid"));//Compile and trainmodel.Compile(optimizer:"sgd",loss:"binary_crossentropy",metrics:newstring[]{"accuracy"});model.Fit(x,y,batch_size:2,epochs:1000,verbose:1);//Save model and weightsstringjson=model.ToJson();File.WriteAllText("model.json",json);model.SaveWeight("model.h5");//Load model and weightvarloaded_model=Sequential.ModelFromJson(File.ReadAllText("model.json"));loaded_model.LoadWeight("model.h5");
Output:
MNIST CNN Example
Python example taken from: https://keras.io/examples/mnist_cnn/
intbatch_size=128;intnum_classes=10;intepochs=12;// input image dimensionsintimg_rows=28,img_cols=28;Shapeinput_shape=null;// the data, split between train and test setsvar((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($"{x_test.shape[0]} test samples");// convert class vectors to binary class matricesy_train=Util.ToCategorical(y_train,num_classes);y_test=Util.ToCategorical(y_test,num_classes);// Build CNN modelvarmodel=newSequential();model.Add(newConv2D(32,kernel_size:(3,3).ToTuple(),activation:"relu",input_shape:input_shape));model.Add(newConv2D(64,(3,3).ToTuple(),activation:"relu"));model.Add(newMaxPooling2D(pool_size:(2,2).ToTuple()));model.Add(newDropout(0.25));model.Add(newFlatten());model.Add(newDense(128,activation:"relu"));model.Add(newDropout(0.5));model.Add(newDense(num_classes,activation:"softmax"));model.Compile(loss:"categorical_crossentropy",optimizer:newAdadelta(),metrics:newstring[]{"accuracy"});model.Fit(x_train,y_train,batch_size:batch_size,epochs:epochs,verbose:1,validation_data:newNDarray[]{x_test,y_test});varscore=model.Evaluate(x_test,y_test,verbose:0);Console.WriteLine($"Test loss: {score[0]}");Console.WriteLine($"Test accuracy: {score[1]}");
Output
Reached 98% accuracy within 3 epoches.
Documentation
https://scisharp.github.io/Keras.NET/
SciSharp
About
Keras.NET is a high-level neural networks API, written in C# with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano. 
Resources
Readme
MIT license
Activity
Stars
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Keras.NET is a high-level neural networks API, written in C# with 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:
Prerequisite
Nuget
Install from nuget: https://www.nuget.org/packages/Keras.NET
Install-Package Keras.NET

dotnet add package Keras.NET

Example with XOR sample
//Load train dataNDarrayx=np.array(newfloat[,]{{0,0},{0,1},{1,0},{1,1}});NDarrayy=np.array(newfloat[]{0,1,1,0});//Build sequential modelvarmodel=newSequential();model.Add(newDense(32,activation:"relu",input_shape:newShape(2)));model.Add(newDense(64,activation:"relu"));model.Add(newDense(1,activation:"sigmoid"));//Compile and trainmodel.Compile(optimizer:"sgd",loss:"binary_crossentropy",metrics:newstring[]{"accuracy"});model.Fit(x,y,batch_size:2,epochs:1000,verbose:1);//Save model and weightsstringjson=model.ToJson();File.WriteAllText("model.json",json);model.SaveWeight("model.h5");//Load model and weightvarloaded_model=Sequential.ModelFromJson(File.ReadAllText("model.json"));loaded_model.LoadWeight("model.h5");
Output:
MNIST CNN Example
Python example taken from: https://keras.io/examples/mnist_cnn/
intbatch_size=128;intnum_classes=10;intepochs=12;// input image dimensionsintimg_rows=28,img_cols=28;Shapeinput_shape=null;// the data, split between train and test setsvar((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($"{x_test.shape[0]} test samples");// convert class vectors to binary class matricesy_train=Util.ToCategorical(y_train,num_classes);y_test=Util.ToCategorical(y_test,num_classes);// Build CNN modelvarmodel=newSequential();model.Add(newConv2D(32,kernel_size:(3,3).ToTuple(),activation:"relu",input_shape:input_shape));model.Add(newConv2D(64,(3,3).ToTuple(),activation:"relu"));model.Add(newMaxPooling2D(pool_size:(2,2).ToTuple()));model.Add(newDropout(0.25));model.Add(newFlatten());model.Add(newDense(128,activation:"relu"));model.Add(newDropout(0.5));model.Add(newDense(num_classes,activation:"softmax"));model.Compile(loss:"categorical_crossentropy",optimizer:newAdadelta(),metrics:newstring[]{"accuracy"});model.Fit(x_train,y_train,batch_size:batch_size,epochs:epochs,verbose:1,validation_data:newNDarray[]{x_test,y_test});varscore=model.Evaluate(x_test,y_test,verbose:0);Console.WriteLine($"Test loss: {score[0]}");Console.WriteLine($"Test accuracy: {score[1]}");
Output
Reached 98% accuracy within 3 epoches.
Documentation
https://scisharp.github.io/Keras.NET/
SciSharp
About
Keras.NET is a high-level neural networks API, written in C# with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano. 
Resources
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MIT license
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