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.NET Standard bindings for Google's TensorFlow, enabling C# and F# developers to build, train, and deploy machine learning models.

TensorFlow.NET (TF.NET) is a .NET Standard binding for Google's TensorFlow, designed to bring data science capabilities to the .NET ecosystem. It implements the TensorFlow API in C#, enabling .NET developers to create, train, and deploy machine learning models using the cross-platform .NET Standard framework. TensorFlow.NET includes a built-in Keras high-level interface, available as the TensorFlow.Keras package. It allows developers to migrate Python-based TensorFlow code to .NET. ML.NET integrates TensorFlow.NET as a backend for model training and inference, improving .NET integration. The library provides APIs similar to TensorFlow/Python, aiming for a zero learning curve for developers familiar with TensorFlow. It supports both CPU and GPU versions with platform-specific packages. Documentation and examples are available to guide users in installation and usage.
TensorFlow.
Explore all tools that specialize in train machine learning models. This domain focus ensures TensorFlow.NET delivers optimized results for this specific requirement.
Explore all tools that specialize in deploy machine learning models. This domain focus ensures TensorFlow.NET delivers optimized results for this specific requirement.
Explore all tools that specialize in deep learning. This domain focus ensures TensorFlow.NET delivers optimized results for this specific requirement.
Allows for immediate execution of TensorFlow operations, providing a more intuitive debugging experience.
Provides a high-level API for building and training neural networks, simplifying model development.
Allows developers to create custom TensorFlow operators in C# for specialized tasks.
Supports saving and loading trained models, enabling deployment in .NET applications.
Leverages GPU hardware for faster training and inference, significantly improving performance.
Directly access and manipulate TensorFlow graphs, sessions, and operations for low-level control.
Install the TensorFlow.NET NuGet package: `Install-Package TensorFlow.NET`
Install the TensorFlow.Keras NuGet package: `Install-Package TensorFlow.Keras`
Install the appropriate computing support package (CPU or GPU version) based on your operating system.
For CPU on Windows/Linux: `Install-Package SciSharp.TensorFlow.Redist`
For CPU on macOS: `Install-Package SciSharp.TensorFlow.Redist-OSX`
For GPU on Windows: `Install-Package SciSharp.TensorFlow.Redist-Windows-GPU` (CUDA and cuDNN required)
For GPU on Linux: `Install-Package SciSharp.TensorFlow.Redist-Linux-GPU` (CUDA and cuDNN required)
Import necessary namespaces: `using static Tensorflow.Binding;` and `using static Tensorflow.KerasApi;`
Load and preprocess your data into TensorFlow tensors.
Define your model architecture using TensorFlow.NET or TensorFlow.Keras APIs.
Train your model using the defined data and optimization algorithms.
Evaluate the model's performance on a validation dataset.
Deploy the trained model for inference using .NET applications.
All Set
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