Displaying 1 to 17 from 17 results

Awesome-CoreML-Models - Largest list of models for Core ML (for iOS 11+)

  •    HTML

We've put up the largest collection of machine learning models in Core ML format, to help iOS, macOS, tvOS, and watchOS developers experiment with machine learning techniques. We've created a site with better visualization of the models CoreML.Store, and are working on more advance features. If you've converted a Core ML model, feel free to submit an issue.

simple_tensorflow_serving - Generic and easy-to-use serving service for machine learning models

  •    Javascript

Simple TensorFlow Serving is the generic and easy-to-use serving service for machine learning models. Read more in https://stfs.readthedocs.io. Install the server with pip.

tensorlayer-tricks - How to use TensorLayer


While research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

kemono_puyo - :cat: Take kemono pictures and lines up 3, then tanoshii

  •    Javascript

This is a toy application that made at the hackathon. It is inspired by following two concepts. At first I tried to implement the feature to control the falling objects but to see the animal faces is so fun and cute. Because of this, I dropped its implementation.

tensorflow-kubernetes-art-classification - Train a TensorFlow model on Kubernetes to recognize art culture based on the collection from the Metropolitan Museum of Art

  •    Python

Read this in other languages: 中国. In this Code Pattern, we will use Deep Learning to train an image classification model. The data comes from the art collection at the New York Metropolitan Museum of Art and the metadata from Google BigQuery. We will use the Inception model implemented in TensorFlow and we will run the training on a Kubernetes cluster. We will save the trained model and load it later to perform inference. To use the model, we provide as input a picture of a painting and the model will return the likely culture, for instance "Italian, Florence" art. The user can choose other attributes to classify the art collection, for instance author, time period, etc. Depending on the compute resources available, the user can choose the number of images to train, the number of classes to use, etc. In this Code Pattern, we will select a small set of images and a small number of classes to allow the training to complete within a reasonable amount of time. With a large dataset, the training may take days or weeks.

TFaaS - TensorFlow as a Service, a general purpose framework to serve TF models.

  •    Javascript

A general purpose framework (written in Go) to serve TensorFlow models. It provides reach and flexible set of APIs to efficiently access your favorite TF models via HTTP interface. The TFaaS supports JSON and ProtoBuffer data-formats. Fore more information please visit curl client page.

tf-bridge - Tensor Bridge: OpenAPI spec and REST wrapper around TensorFlow Serving

  •    Python

Tensor Bridge is an OpenAPI Specification as well as a simple Connexion wrapper for TensorFlow Serving. The specification was obtained by compiling an annotated tensor_bridge.proto using grpc-gateway. The result is located in swagger/tensor_bridge.json.


  •    Jupyter

This repo also contains a notebook that shows the result of the different steps in the convolutional architectures.

fast-weights - Implementation of the paper [Using Fast Weights to Attend to the Recent Past](https://arxiv

  •    Python

This script generates a file called associative-retrieval.pkl, which can be used for training. The following is the accuracy and loss graph for R=20. The experiments are barely tuned.

tensorflow_template - A simple template for tensorflow project. A high-level version:

  •    Python

A deep learning template for tensorflow, of which the idea is from another project MrGemy95/Tensorflow-Project-Template. All you need to do is finish the following four functions.