TensorFlow-Notebook

TensorFlow™ is an open source software library for high-performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

Prerequisites

• Kubernetes cluster v1.8+
• TensorFlow execution model

Graphs

Machine learning can get complex quickly, and deep learning models can become large. For many model graphs, you need to be distributed training to be able to iterate within a reasonable time frame. And, you’ll typically want the models you develop to deploy to multiple platforms.

With the current version of TensorFlow, you write code to build a computation graph, then execute it. The graph is a data structure that fully describes the computation you want to perform. This has lots of advantages:

It’s portable, as the graph can be executed immediately or saved to use later, and it can run on multiple platforms: CPUs, GPUs, TPUs, mobile, embedded. Also, it can be deployed to production without having to depend on any of the code that built the graph, only the runtime necessary to execute it.
It’s transformable and optimizable, as the graph can be transformed to produce a more optimal version for a given platform. Also, memory or computer optimizations can be performed and trade-offs made between them. This is useful, for example, in supporting faster mobile inference after training on larger machines.
Support for distributed execution
TensorFlow’s high-level APIs, in conjunction with computation graphs, enable a rich and flexible development environment and powerful production capabilities in the same framework.

Eager execution

An upcoming addition to TensorFlow is eager execution, an imperative style for writing TensorFlow. When you enable eager execution, you will be executing TensorFlow kernels immediately, rather than constructing graphs that will be executed later.

Why is this important? Four major reasons:

• You can inspect and debug intermediate values in your graph easily.
• You can use Python control flow within TensorFlow APIs, including loops, conditionals, functions, closures, etc.
• Eager execution should make debugging more straightforward.
• Eager’s “define-by-run” semantics will make building and training dynamic graphs easy.

Once you are satisfied with your TensorFlow code running eagerly, you can convert it to a graph automatically. This will make it easier to save, port, and distribute your graphs.

TensorFlow’s open source models

The TensorFlow team has open sourced a large number of models. You can find them in the tensorflow/models repo. For many of these, the released code includes not only the model graph but also trained model weights. This means that you can try such models out of the box, and you can tune many of them further using a process called transfer learning.

Here are just a few of the recently released models (there are many more):

• The Object Detection API: It’s still a core machine learning challenge to create accurate machine learning models capable of localizing and identifying multiple objects in a single image. The recently open sourced TensorFlow Object Detection API has produced state-of-the-art results (and placed first in the COCO detection challenge).
• tf-seq2seq: Google previously announced Google Neural Machine Translation (GNMT), a sequence-to-sequence (seq2seq) model that is now used in Google Translate production systems. tf-seq2seq is an open source seq2seq framework in TensorFlow that makes it easy to experiment with seq2seq models and achieve state-of-the-art results.
  ParseySaurus is a set of pretrained models that reflect an upgrade to SyntaxNet. The new models use a character-based input representation and are much better at predicting the meaning of new words based both on their spelling and how they are used in context. They are much more accurate than their predecessors, particularly for languages where there can be dozens of forms for each word and many of these forms might never be observed during training, even in a very large corpus.
• Multi style Pastiche Generator from the Magenta Project: “Style transfer” is what’s happening under the hood with those fun apps that apply the style of a painting to one of your photos. This Magenta model extends image style transfer by creating a single network that can perform more than one stylization of an image, optionally at the same time.

Features

TensorFlow provides stable Python API and C APIs; and without API backward compatibility guarantee: C++, Go, Java, JavaScript, and Swift (early release). Third party packages are available for C#, Haskell,Julia, R, Scala, Rust, and OCaml.

Applications

Among the applications for which TensorFlow is the foundation, are automated image captioning software, such as DeepDream. RankBrain now handles a substantial number of search queries, replacing and supplementing traditional static algorithm-based search results

The post TensorFlow-Notebook appeared first on kubedex.com.