Tensorflow API | Tensorflow Asp.Net

Docker makes it easy to develop and deploy custom and consistent environments that include specific applications and dependencies. Docker calls these compilations Images. Docker images can be hosted and retrieved from private locations or from the official repository,  Docker Hub . Create a Docker Image Permalink Create a new local image based on the latest Ubuntu Docker image. Although the repository already has a number of LAMP stack images available, we create one in this guide as an example of the process. Pull the latest Ubuntu image: docker pull ubuntu Create the new container, such that we can add our LAMP stack to Ubuntu. This example names the container  lamp-server-template  and adds the  bash  option to the docker command to enter the container in order to continue making changes: docker run --name lamp-server-template -it ubuntu:latest bash Install the  lamp-server  metapackage inside the container: apt-get install ...

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. Basic Serving Tutorial See the  basic tutorial  on the TensorFlow Serving site to learn how to export a trained TensorFlow model and build a server to serve the exported model. Advanced Serving Tutorial See the  advanced tutorial  on the TensorFlow Serving site to learn how to build a server that dynamically discovers and serves new versions of a trained TensorFlow model. Serving Inception Model Tutorial See the  serving inception tutorial  on the TensorFlow Serving site to learn how to serve the inception model with TensorFlow Serving and Kubernetes.

Here I extend the API to train on a new object that is not part of the COCO dataset. In this case I chose a toy that was lying around. See gif below. So far, I have been impressed by the performance of the API. The steps highlighted here can be extended to any single or multiple object detector that you want to build. Tensorflow Toy Detector~ You can find the code on my  Github  repo Collecting data The first step is collecting images for your project. You could download them from google ensuring you have a wide variation in angles, brightness, scale etc. In my case I created a video of the little aeroplane toy and used  Opencv  to extract images from the video. This saved me a lot of time. I ensured that images were taken from multiple angles. You can also randomly change brightness for some of the images so that the detector can work under different conditions of lightning. Overall 100–150 pics will suffice. See some sample images below: ...

In this post I want to explore some of the key similarities and differences between two popular deep learning frameworks: PyTorch and TensorFlow. Why those two and not the others? There are many deep learning frameworks and many of them are viable tools, I chose those two just because I was interested in comparing them specifically. Origins TensorFlow is developed by Google Brain and actively used at Google both for research and production needs. Its closed-source predecessor is called DistBelief. PyTorch is a cousin of lua-based Torch framework which is actively used at Facebook. However, PyTorch is not a simple set of wrappers to support popular language, it was rewritten and tailored to be fast and feel native. The best way to compare two frameworks is to code something up in both of them. I’ve written a companion jupyter notebook for this post and you can  get it here . All code will be provided in the post too. First, let’s code a simple approximator for t...