probability/README.md at main · tensorflow/probability
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation. T
tensorflow/README.md at master · tensorflow/tensorflow · GitHub
TensorFlow Lite for Microcontrollers This an experimental port of TensorFlow Lite aimed at micro controllers and other devices with only kilobytes of memory. It doesn't require any operating system support, any standard C or C++ libraries, or dynamic memory allocation, so it's designed to be portable even to 'bare metal' systems. The core runtime fits in 16KB on a Cortex M3, and with enough operat
tpu/models/experimental/keras at master · tensorflow/tpu · GitHub
Keras TPU Integration This directory contains examples of using the experimental Cloud TPU-Keras integration that was added in TF 1.9. To learn more about this new integration, check out the documentation (coming soon!). MNIST This is a simple sequential convolutional network to recognize handwritten digits. This is a simple example of how to use the new Keras integration. ResNet-50 ResNet-50 is a
models/samples/languages/java at master · tensorflow/models · GitHub
TensorFlow for Java: Examples These examples include using pre-trained models for image classification and object detection, and driving the training of a pre-defined model - all using the TensorFlow Java API. The TensorFlow Java API does not have feature parity with the Python API. The Java API is most suitable for inference using pre-trained models and for training pre-defined models from a sing
Mesh TensorFlow - Model Parallelism Made Easier
Introduction Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying a broad class of distributed tensor computations. The purpose of mesh-tensorflow is to formalize and implement distribution strategies for your computation graph over your hardware/processors For example: "Split the batch over rows of processors and split the units in the hidden layer across colum
swift/README.md at main · tensorflow/swift
Swift for TensorFlow was an experiment in the next-generation platform for machine learning, incorporating the latest research across machine learning, compilers, differentiable programming, systems design, and beyond. It was archived in February 2021. Some significant achievements from this project include: Added language-integrated differentiable programming into the Swift language. This work co
swift/docs/PythonInteroperability.md at main · tensorflow/swift
Date: April 2018 As described in the design overview document, Python API interoperability is an important requirement for this project. While Swift is designed to integrate with other programming languages (and their runtimes), the nature of dynamic languages does not require the deep integration needed to support static languages. Python in particular is designed to be embedded into other applic
swift/docs/WhySwiftForTensorFlow.md at main · tensorflow/swift
As discussed in the design overview document our project goal is to improve usability of TensorFlow. We quickly realized that our core static analysis-based Graph Program Extraction algorithm would not work well for Python given its highly dynamic nature. This led us down the path of having to pick another language to work with, and we wanted to approach this methodically. As such, we defined goal
Release TensorFlow 1.8.0 · tensorflow/tensorflow
Release 1.8.0 Major Features And Improvements Can now pass tf.contrib.distribute.MirroredStrategy() to tf.estimator.RunConfig() to run an Estimator model on multiple GPUs on one machine. Add tf.contrib.data.prefetch_to_device(), which supports prefetching to GPU memory. Added Gradient Boosted Trees as pre-made Estimators: BoostedTreesClassifier, BoostedTreesRegressor. Add 3rd generation pipeline c
swift/docs/AutomaticDifferentiation.md at main · tensorflow/swift
Automatic Differentiation (AD), also known as algorithmic differentiation, is a family of techniques used to obtain the derivative of a function. Functions can be represented as a composition of elementary operators whose derivatives are well-known. While partial derivatives can be computed through different techniques, the most common is a recursive application of the chain rule in the reverse di
Swift for TensorFlow
Swift for TensorFlow was an experiment in the next-generation platform for machine learning, incorporating the latest research across machine learning, compilers, differentiable programming, systems design, and beyond. It was archived in February 2021. Some significant achievements from this project include: Added language-integrated differentiable programming into the Swift language. This work co
swift/docs/GraphProgramExtraction.md at main · tensorflow/swift
Date: April 2018 Resources: Tech talk at 2018 LLVM Developers' Meeting Swift for TensorFlow provides a define-by-run programming model while also providing the full benefit of graphs. This is possible because of a core "graph program extraction" algorithm that we’ve built into the Swift compiler that takes imperative Swift code and automatically builds a graph as part of the normal compilation flo
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tf.keras multi input models don't work when using tf.data.Dataset · Issue #20698 · tensorflow/tensorflow · GitHub
Fix error that bugs Keras during multi-gpu with batch normalization by owsd · Pull Request #23197 · tensorflow/tensorflow · GitHub
Importing TF in Python yields 'cannot import name 'build_info' · Issue #13526 · tensorflow/tensorflow
models/research/slim/nets/mobilenet_v1.md at master · tensorflow/models
Oversampling functionality in dataset API · Issue #14451 · tensorflow/tensorflow
swift/Usage.md at main · tensorflow/swift
slim.separable_conv2d is too slow · Issue #12132 · tensorflow/tensorflow
tfjs-models/posenet at master · tensorflow/tfjs-models
