tensorflow/tensorflow/contrib/slim at master · tensorflow/tensorflow · GitHub
TensorFlow-Slim TF-Slim is a lightweight library for defining, training and evaluating complex models in TensorFlow. Components of tf-slim can be freely mixed with native tensorflow, as well as other frameworks, such as tf.contrib.learn. Usage Why TF-Slim? TF-Slim is a library that makes defining, training and evaluating neural networks simple: Allows the user to define models much more compactly
models/slim at master · tensorflow/models · GitHub
README.md TensorFlow-Slim image classification library TF-slim is a new lightweight high-level API of TensorFlow ( tensorflow.contrib.slim ) for defining, training and evaluating complex models. This directory contains code for training and evaluating several widely used Convolutional Neural Network (CNN) image classification models using TF-slim. It contains scripts that will allow you to train m
tensorflow/tensorflow/contrib/pi_examples at master · tensorflow/tensorflow · GitHub
TensorFlow Raspberry Pi Examples This folder contains examples of how to build applications for the Raspberry Pi using TensorFlow. Building the Examples Follow the Raspberry Pi section of the instructions at tensorflow/contrib/makefile to compile a static library containing the core TensorFlow code. Install libjpeg, so we can load image files: sudo apt-get install -y libjpeg-dev To download the ex
tensorflow/tensorflow/contrib/makefile at v0.10.0 · tensorflow/tensorflow
The recommended way to build TensorFlow from source is using the Bazel open-source build system. Sometimes this isn't possible. The build system may not have the RAM or processing power to support Bazel. Bazel or its dependencies may not be available. You may want to cross-compile for an unsupported target system. This experimental project supplies a Makefile automatically derived from the depende
models/im2txt at master · tensorflow/models · GitHub
README.md Show and Tell: A Neural Image Caption Generator A TensorFlow implementation of the image-to-text model described in the paper: "Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge." Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan. IEEE transactions on pattern analysis and machine intelligence (2016). Full text available at: http://arxiv.org/abs/1609
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GitHub - tensorflow/model-card-toolkit: A toolkit that streamlines and automates the generation of model cards
community/rfcs/20191017-tfx-standardized-inputs.md at master · tensorflow/community
docs/site/en/r1/tutorials/representation/kernel_methods.md at master · tensorflow/docs
OpenCL support · Issue #22 · tensorflow/tensorflow
tensorflow/tensorflow/g3doc/get_started/os_setup.md at v0.10.0 · tensorflow/tensorflow