Use pip to install TensorFlow 2 as usual. TF Agents – TF-Agents is a library for Reinforcement Learning in TensorFlow The following tutorials should help you getting started with using and applying models from Hub to your needs. (See there for extra instructions about GPU support.) Issue #5: 2018/05/14 to 2018/05/20. TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models. The Model Maker API also lets us switch the underlying model. import tensorflow_datasets as tfds tfds.disable_progress_bar() EfficientNet Keras (and TensorFlow Keras) This repository contains a Keras (and TensorFlow Keras) reimplementation of EfficientNet, a lightweight convolutional neural network architecture achieving the state-of-the-art accuracy with an order of magnitude fewer parameters and FLOPS, on both ImageNet and five other commonly used transfer learning datasets. TensorFlow Hub is a comprehensive repository of pre-trained models ready for fine-tuning and deployable anywhere. Contribute on GitHub; Publish models on tfhub.dev; Portability & Deletion of Data; Developer info. A module is a self-contained piece of a TensorFlow graph, along with its weights and assets, that can be reused across different tasks in a process known as transfer learning. Collection of EfficientNet models for image classification and feature extraction trained on Imagenet (ILSVRC-2012-CLS). Sonnet – TensorFlow-based neural network library; OpenSpiel – Collection of environments and algorithms for research in general reinforcement learning and search/planning in games. Installing tensorflow_hub. In particular, we first use AutoML MNAS Mobile framework to develop a mobile-size baseline network, named as EfficientNet-B0; Then, we use the compound scaling method to scale up this baseline to obtain EfficientNet-B1 to EfficientNet-B7. The Challenge. The latest Tensorflow release (TF 2.1) was focused on TPUs and they’re now supported both through the Keras high-level API and at a lower level, in models using a custom training loop. We can’t wait to see how your solutions are accelerated by TPUs! Hier sollte eine Beschreibung angezeigt werden, diese Seite lässt dies jedoch nicht zu. Explore the efficientnet collection published by Google on TensorFlow Hub. 88 Stars. The new tool is designed to adapt machine learning models to datasets with transfer learning. The tensorflow_hub library can be installed alongside TensorFlow 1 and TensorFlow 2. Use with TensorFlow 2. 10M+ Downloads. GitHub Gist: star and fork Lauler's gists by creating an account on GitHub. Check out the models for Researchers, or learn How It Works. Official Docker images for the machine learning framework TensorFlow (http://www.tensorflow.org) Container. EfficientNet Keras (and TensorFlow Keras) This repository contains a Keras (and TensorFlow Keras) reimplementation of EfficientNet, a lightweight convolutional neural network architecture achieving the state-of-the-art accuracy with an order of magnitude fewer parameters and FLOPS, on both ImageNet and five other commonly used transfer learning datasets. This tfds package is the easiest way to load pre-defined data. TensorFlow World is the first event of its kind - gathering the TensorFlow ecosystem and machine learning developers to share best practices, use cases, and a firsthand look at the latest TensorFlow product developments. The Model Maker API also lets us switch the underlying model. Discover and publish models to a pre-trained model repository designed for research exploration.



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