GitHub - tensorflow/graphics: TensorFlow Graphics: Differentiable Graphics Layers for TensorFlowYou signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
tensorflow/README.md at master · tensorflow/tensorflow · GitHub
Quantization-aware training Quantization-aware model training ensures that the forward pass matches precision for both training and inference. There are two aspects to this: Operator fusion at inference time are accurately modeled at training time. Quantization effects at inference are modeled at training time. For efficient inference, TensorFlow combines batch normalization with the preceding con
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
GitHub - tensorflow/fold: Deep learning with dynamic computation graphs in TensorFlow
TensorFlow Fold is a library for creating TensorFlow models that consume structured data, where the structure of the computation graph depends on the structure of the input data. For example, this model implements TreeLSTMs for sentiment analysis on parse trees of arbitrary shape/size/depth. Fold implements dynamic batching. Batches of arbitrarily shaped computation graphs are transformed to produ
models/inception at master · tensorflow/models · GitHub
README.md Inception in TensorFlow ImageNet is a common academic data set in machine learning for training an image recognition system. Code in this directory demonstrates how to use TensorFlow to train and evaluate a type of convolutional neural network (CNN) on this academic data set. In particular, we demonstrate how to train the Inception v3 architecture as specified in: Rethinking the Inceptio
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GitHub - tensorflow/tensorrt: TensorFlow/TensorRT integration
GitHub - tensorflow/hub: A library for transfer learning by reusing parts of TensorFlow models.
GitHub - tensorflow/lucid: A collection of infrastructure and tools for research in neural network interpretability.
GitHub - tensorflow/adanet: Fast and flexible AutoML with learning guarantees.
tpu/models/official/resnet at master · tensorflow/tpu
GitHub - tensorflow/tfjs: A WebGL accelerated JavaScript library for training and deploying ML models.
models/research/deeplab at master · tensorflow/models
GitHub - tensorflow/tensor2tensor: Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.