Release TensorFlow 2.0.0-alpha0 · tensorflow/tensorflow
Release 2.0.0-alpha0 Major Features and Improvements TensorFlow 2.0 focuses on simplicity and ease of use, featuring updates like: Easy model building with Keras and eager execution. Robust model deployment in production on any platform. Powerful experimentation for research. API simplification by reducing duplication removing deprecated endpoints. For information on upgrading your existing Tensor
docs/recurrent_quickdraw.md at master · tensorflow/docs
Recurrent Neural Networks for Drawing Classification Quick, Draw! is a game where a player is challenged to draw a number of objects and see if a computer can recognize the drawing. The recognition in Quick, Draw! is performed by a classifier that takes the user input, given as a sequence of strokes of points in x and y, and recognizes the object category that the user tried to draw. In this tutor
models/object_detection at master · tensorflow/models · GitHub
README.md Tensorflow Object Detection API Creating accurate machine learning models capable of localizing and identifying multiple objects in a single image remains a core challenge in computer vision. The TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. At Google we’ve certainly found
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
tensorflow/README.md at master · tensorflow/tensorflow · GitHub
TensorBoard TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs. TensorBoard currently supports five visualizations: scalars, images, audio, histograms, and the graph. This README gives an overview of key concepts in TensorBoard, as well as how to interpret the visualizations TensorBoard provides. For an in-depth example of using TensorBoard,
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
TensorBoard: Embedding Visualization
TensorBoard operates by reading TensorFlow events files, which contain summary data that you can generate when running TensorFlow. Here's the general lifecycle for summary data within TensorBoard. First, create the TensorFlow graph that you'd like to collect summary data from, and decide which nodes you would like to annotate with the tf.summary operations. For example, suppose you are training a
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tpu/models/official/mnasnet/mixnet at master · tensorflow/tpu
build/golang_install_guide at master · tensorflow/build
GitHub - tensorflow/text: Making text a first-class citizen in TensorFlow.
models/research/deeplab at master · tensorflow/models
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/guide/roadmap.md
https://github.com/tensorflow/tfjs-models/tree/master/speech-commands
GitHub - tensorflow/swift: Swift for TensorFlow
tensorflow/tensorflow/python/training/optimizer.py at master · tensorflow/tensorflow
cifar10_input.py calls tf.strided_slice() with 3 arguments, 4 needed. · Issue #817 · tensorflow/models
Couldn't open CUDA library libcudnn.so. LD_LIBRARY_PATH: :/usr/local/cuda-7.5/lib64 · Issue #4827 · tensorflow/tensorflow
tensorflow/mnist_with_summaries.py at master · tensorflow/tensorflow · GitHub