Gradient for .NET

Gradient allows you to create, train, and use machine learning models with the full power of TensorFlow API on .NET

var input = tf.placeholder(tf.float32, new TensorShape(null, 1), name: "x");
var output = tf.placeholder(tf.float32, new TensorShape(null, 1), name: "y");
 
var hiddenLayer = tf.layers.dense(input, hiddenSize,
    activation: tf.sigmoid_fn,
    kernel_initializer: new ones_initializer(),
    bias_initializer: new random_uniform_initializer(minval: -x1, maxval: -x0),
    name: "hidden");
 
var model = tf.layers.dense(hiddenLayer, units: 1, name: "output");
 
var cost = tf.losses.mean_squared_error(output, model);
 
var training = new GradientDescentOptimizer(learning_rate: learningRate).minimize(cost);

Code sample.

Features

• Access the full set of TensorFlow APIs

  • Build computation graphs, and run them in sessions
  • Use Keras-style high-level APIs
  • Build fast data pipelines, keep logs and model checkpoints
  • Use estimators and the full power of tf.contrib
  • Use eager mode to transform data interactively
  • Many more

• Train and run models on any hardware platform: CPUs, GPUs, TPUs

• Use distributed training features

• Track your training progress with Tensorboard

  

• Easily port numerous existing TensorFlow examples

  from simple numerical computation samples to state-of-art models like AlphaZero - the new world's Go champion by DeepMind

• Get started quickly with a collection of samples

• Seek help with the growing community

• Use C# for machine learning

  • Static typing when possible, fallback to dynamic in corner cases
  • IDE support: code completion, documentation hints for classes, functions, and parameters
  • Experimental support for upcoming C# 8.0 features, such as ranges
  • Can be used from C# interactive, and C# kernel for Jupyter

Comparison with TensorFlowSharp

	TensorFlowSharp	Gradient
Load TensorFlow models	✓	✓
Train existing models	✓	✓
Create new models with low-level API	✓	✓
Create new models with high-level API	✗	✓
Dependencies	TF	TF + Python
TensorBoard integration	✗	✓
Estimators	✗	✓
Dataset manipulation via tf.data	✗	✓
tf.contrib	✗	✓
Commercial support	✗	✓

---

Get started with an early tech preview, or sign up for Gradient News and Releases.

---