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:
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Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
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Supports both convolutional networks and recurrent networks, as well as combinations of the two.
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Runs seamlessly on CPU and GPU.
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Python 3.7 or 3.8, Link: https://www.python.org/downloads/
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Install keras, numpy 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.
Install from nuget: https://www.nuget.org/packages/Keras.NET
dotnet add package Keras.NET
//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:
Python example taken from: https://keras.io/examples/mnist_cnn/
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://scisharp.github.io/Keras.NET/