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TensorFlow Introductory Tutorials Collection __nlp/deeplearning

TensorFlow Introductory Tutorials 0:bigpicture The speed of introduction TensorFlow Introductory Tutorial 1: Basic Concepts and understanding TensorFlow Getting Started Tutorial 2: Installing and Using TensorFlow Introductory Tutorials The basic definition of 3:CNN convolution neural network understanding

TensorFlow Learning Notes 2:about Session, Graph, operation and Tensor

Brief introductionPrevious note: TensorFlow study notes 1:get Started We talked about TensorFlow is a computing system based on graph. The nodes of the graph are made up of operations (operation), and each node of the graph is connected by tensor (Tensor) as an edge. So TensorFlow's calculation process is a tensor flow graph. The TensorFlow diagram must be calcul

Problem solving notes-Ubuntu installation tensorflow and problem-solving notes

Cited articles 1. Python 2.7, Ubuntu14.04 as the base environment # Ubuntu/linux 64-bit, CPU only, Python 2.7: $ sudo pip install--upgrade https://storage.googleapis.com/tensorflow/l INUX/CPU/TENSORFLOW-0.8.0-CP27-NONE-LINUX_X86_64.WHL # ubuntu/linux 64-bit, GPU enabled, Python 2.7. Requires CUDA Toolkit 7.5 and CuDNN v4. With GPU acceleration, you need to install Cuda and CUDNN # for other versions, see "

Easy tutorial for installing TensorFlow under windows with Pycharm

79760616Recently began to learn the relevant knowledge of deep learning, ready to combat, read some about TensorFlow installation blog, around a few bends, so to fill the pit (redundant installed or non-Windows), mainly around the use of pycharm need to tensorflow installation process.Environment: WINDOWS10 Professional Edition. Just want to run a little bit tensorflow

WINDOWS10 Installing the TensorFlow GPU version (PIP3 installation method)

and the version information indicates that the installation was successful.(2), download CUDNNTensorFlow version different, the need for the CUDNN version is not the same, see TensorFlow release notes, such as: tensorflow1.3 Release Notes Configure CUDNN Download to the corresponding version of CUDNN (tensorflow1.3 need cuDNN6, can be downloaded to https://www.zhihu.com/question/37082272), unzip: The extracted bin directory is

Installing TensorFlow (CentOS) under Linux

One, Python installationCentOS comes with python2.7.5, this step can be omitted.Second, Python-pipPip--python index package, lifetimes Linux yum, installs the Management Python software pack.Yum Install Python-pip python-develThird, installation TensorFlowInstalling Linux and python2.7-based TensorFlow 0.9Pip Install https://storage.googleapis.com/tensorflow/linux/cpu/

TensorFlow Running program error FAILEDPRECONDITIONERROR

1 failedpreconditionerror Error phenomenaWhen running TensorFlow error, the error statement is as follows:Failedpreconditionerror (see above for Traceback): Attempting to use uninitialized value Variable[[Node:variable/read = _mklidentity[t=dt_float, _kernel= "Mklop", _device= "/job:localhost/replica:0/task:0/device: Cpu:0 "] (Variable, DMT/_0)]A straightforward translation of the cause of the error (letter, elegance, and accuracy of the serious missi

Queues in the TensorFlow

In the previous article, although the results were correct, the result was an error at the end of the run: _1_input_producer:skipping cancelled enqueue attempt with \ not closed This is mainly because the main thread has been closed, but the read Data queue thread is still executing the team. This article from the "Understanding of TensorFlow Queue", the article on the TF queue is very detailed, benefit, it is necessary to reprint over. There are some

The study and application of into gold deep learning tensorflow framework in smelting number video tutorial

Time of instruction:This course will begin on April 1. The duration of the course is approximately 14 weeks. Subject:People who are interested in deep learning AI, who want to learn about deep learning practices. learners need a little bit of the basics of Python development and deep learning, neural network fundamentalsCourse Environment:Windows10 + AnacondaHarvest Expectations:Master the basic use of TensorFlow and Tensorboard, can skillfully use

TensorFlow QuickStart 2--enabling handwritten digit recognition

TensorFlow Quick start for handwritten digit recognition Environment: Virtual machine ubuntun16.0.4 TensorFlow (CPU version only)TensorFlow installation See:http://blog.csdn.net/yhhyhhyhhyhh/article/details/54429034Or:Http://www.tensorfly.cn/tfdoc/get_started/os_setup.htmlIn this paper, we will use TensorFlow to te

Ubuntu14.04 under TensorFlow installation

My computer did not install a dual system, so decided to install a tensorflow in the virtual machine, the following is the installation process:1. Installing Anaconda2 for LinuxThe official website under the words very slowly, to the Tsinghua Mirror website, I last article has the websiteInstallation: Bash anaconda2.shNext, you can choose whether to create a virtual environment, create the words Conda create-n ten

Tensorflow-gpu, Cuda, CUDNN installation on Windows

, including more than 100 of the most popular python,r and Scala packages for data science.From Anaconda official download pageSee Anaconda Official tutorial for details, easy to understand!Anaconda Preliminary Study0. Download Anaconda installation package: Anaconda officialI downloaded the anaconda4.3.0for Windows 64bit (built-in python3.6)Download is ready to install, always next step.1. Check if Anaconda is installed successfully:conda --version(hehe, the first step succeeded, happy Point)2.

"Magenta project" to teach you to create music with TensorFlow neural network

original link: http://www.cnblogs.com/learn-to-rock/p/5677458.htmlaccidentally on the internet to see a I am very interested in the project Magenta, with TensorFlow let neural network automatically create music. The vernacular is: You can use some of the style of music to make models, and then use the training model of the new music processing to create new music. spent a half-time to finally have the results, very happy, but also this half-day experi

Convolutional Networks for Mnist in TensorFlow

It 's written in front . This paper introduces the task of identifying handwritten characters by using convolution neural network based on TensorFlow on Mnist dataset, including: {Two layers of volume base}+{a layer of Relu full link layer}+{the full link layer of Softmax layer}. Because the structure is simple, the code is clear, the whole article to the main code, reading save effort and convenience. 1. Load mnist Data # load Mnist data from tensor

TensorFlow Parallel Computing: multicore (multicore), multithreading (multi-thread), graph segmentation (graph Partition) _tensorflow

GitHub Download Complete code Https://github.com/rockingdingo/tensorflow-tutorial/tree/master/mnist Brief introduction It takes a long time to use the TensorFlow training depth neural network model, because the parallel computing provides an important way to improve the running speed. TensorFlow provides a variety of ways to run the program in parallel, and the

How to compile a demo running TensorFlow

1. Install the compilation tool Bazel, you can refer to the official tutorial. https://docs.bazel.build/versions/master/install-ubuntu.html 2. Configure the TensorFlow compilation environment Run the Configure file under the TensorFlow directory and configure it according to your environment. For example, the following: **root@fly-virtual-machine:/home/share/tensorf

WIN10 System Installation Anaconda+tensorflow+keras

was successful.Second, installation TensorFlowOpen Anaconda Prompt1. Upgrade Pip to the latest version:2. Create an environment named TensorFlow and install the Python3.5.2Conda Create--name TensorFlow python=3.5.2Enter Y, enter. After the installation is complete:3. Activate this environment: Activate TensorFlow4. Installing TensorFlowPip Install TensorFlowNote: To install

Windows installation TensorFlow error "DLL load failed: Specified module not found"

  After installing TensorFlow under Windows, after running Python under CMD, import TensorFlow appears with the following error:Traceback (most recent):File "D:\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line +, in Swig_ Import_helperreturn Importlib.import_module (mname)File "D:\Python\Python35\lib\importlib__init__.py",

Google Open source second generation machine learning system TensorFlow

Deep learning has a profound effect on computer science. It makes it possible for cutting-edge technology to research and develop products that are used by tens of millions of of people everyday.The study announced the launch of the second-generation machine learning System (TENSORFLOW), which has been strengthened for the previous distbelief, and more importantly,It's open source and can be used by anyone.Built in 2011, Google's internal deep learnin

Google machine learning system tensorflow v0.11.0 RC1 release _k open source framework

TensorFlow v0.11.0 RC1 Released, TensorFlow is Google's second-generation machine learning system, according to Google, in some benchmarks, tensorflow performance than the first generation of distbelief faster than twice times. Extended support for TensorFlow depth learning, any computation that can be expressed using

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