tensorflow in production

3 Ways of reading data There are 3 ways to read data in a TensorFlow program:Supply data (feeding): At each step in the TensorFlow program, let the Python code supply the data.Reading data from a file: At the beginning of the TensorFlow graph, let an input pipeline read the data from the file.Preload data: Define constants or variables in the

The TensorFlow and the black Tech. Google hosted the first TensorFlow developer summit in Mountain View, California, February 16, 2017 (Beijing time) 2 o'clock in the morning. Google site announced the world's leading deep learning open source Framework TensorFlow officially released the V1.0 version, and to ensure that Google's current release API interface to m

Tensorboard Visualization Technology: It introduces how to use Tensorboard, and TensorFlow graph model, training data visualization and so on. TensorFlow High-level API: Describes the use of layers, estimators, and canned estimators API to define the training model. Integrating Keras TensorFlow: Describes how to use the Keras API for model definition and traini

Tf.variable_scope () and Tf.get_variables () interface. To ensure that each variable has a unique name and can easily modify the number of hidden nodes and the number of network layers, we recommend referencing the code in the project, especially when defining variables to bind Cpu,tensorflow using the GPU by default may cause parameter updates to be too slow. The code above is also common in production e

As a result of the recent busy, until the holidays are empty, so will learn from their own knowledge to share. If there is a wrong place, please point out, thank you! At present the deep study is getting more and more fire, the related worker who learns, uses TensorFlow more and more. Recently, a Python script was used to train the model under the TensorFlow line, and the Freeze_graph tool was used to outpu

TensorFlow Learning Notes 4: Distributed TensorFlow Brief Introduction The TensorFlow API provides cluster, server, and supervisor to support distributed training of models. The distributed training introduction about TensorFlow can refer to distributed TensorFlow. A simpl

Development environment: Mac OS 10.12.5Python 2.7.10GCC 4.2.1Mac default is no pip, install PIP.sudo easy_install pip1. Installing virtualenvsudo pip install virtualenv--upgradeCreate a working directory:sudo virtualenv--system-site-packages ~/tensorflowMake the directory, activate the sandboxCD ~/tensorflowSOURCE Bin/activateInstall TensorFlow in 2.virtualenvAfter entering the sandbox, execute the following command to install

1. Download and install Anaconda1.1 downloadDownload the Linux version from Anaconda official website (https://www.continuum.io/downloads)https://repo.continuum.io/archive/(Recommended python3.5)1.2 InstallationCD ~/downloadssudo bash anaconda2-5.0.1-linux-x86_64.sh (download the corresponding version of Python2.7 here)Ask if you want to add the Anaconda bin to the user's environment variable and select yes!Installation is complete.2. Install tensorflow2.1 set up

"Google" + "deep learning", two tags let the December 2015 Google open-source deep learning tool TensorFlow after its release quickly became the world's hottest open source project, April 2016, open source TensorFlow support distributed features, The application to the production environment is further.The TensorFlow A

Through a few routines, we gradually established a perceptual knowledge of tensorflow. This article will further from the internal principle of deep understanding, and then for reading source to lay a good foundation.1. Graph (graph)The TensorFlow calculation is abstracted as a forward graph that includes several nodes. As shown in the example:The corresponding TensorFl