Welcome to TensorFlow.NET’s documentation!

The Definitive Guide to TensorFlow.NET

• The Definitive Guide to TensorFlow.NET
  • The CSharp binding for Google’s TensorFlow
    • An Open Source Machine Learning Framework for Everyone
• Foreword
• Preface
• Get started with TensorFlow.NET
  • Install the TensorFlow.NET SDK
  • Start coding Hello World
• Chapter 1. Tensor
  • Represents one of the outputs of an Operation
    • What is Tensor?
    • How to create a Tensor?
    • Data Structure of Tensor
• Chapter 2. Constant
  • How to create a Constant
  • Dive in Constant
    • NDArray
    • Tensor
    • Other functions to create a Constant
• Chapter. Variable
• Chapter. Placeholder
• Chapter. Graph
  • Defining the Graph
  • Save Model
  • Freezing the Graph
    • Why we need it?
  • Optimizing for Inference
  • Restoring the Model
• Chapter. Session
  • Running Computations in a Session
• Chapter. Operation
• Chapter. Queue
  • FIFOQueue
  • PaddingFIFOQueue
  • PriorityQueue
  • RandomShuffleQueue
• Chapter. Gradient
  • Register custom gradient function
• Chapter. Trainer
  • Saver
  • Saver Builder
    • Bulk Saver Builder
• Chapter. Eager Mode
• Chapter. Linear Regression
  • What is linear regression?
  • Cost Function
  • Gradient Descent
• Chapter. Logistic Regression
  • What is logistic regression?
• Chapter. Nearest Neighbor
• Chapter. Image Recognition
  • Let’s get started with real code.
    • 1. Prepare data
    • 2. Load image file and normalize
    • 3. Load pre-trained model and predict
    • 4. Print the result
• Chapter. Neural Network
• Chapter. Convolution Neural Network