tensorflow

Steps for building the Tensorflow Environment What? We need to build the TensorFlow environment through the vmwarevirtual Machine Platform + Ubuntu Virtual Machine + pip installation. For more information about other operating systems, see the link provided above. Tip: it is best not to use windows. There will be many compatibility problems later. There are also several installation methods, such as pip, do

Learning notes TF062: TensorFlow linear algebra compiling framework XLA, tf062tensorflow XLA (Accelerated Linear Algebra), a specialized Linear Algebra compiler (demain-specific compiler), optimizes TensorFlow computing. Real-time (just-in-time, JIT) compilation or advance (ahead-of-time, AOT) compilation to implement XLA, which facilitates hardware acceleration. XLA is still in the trial phase. Https://www

Ref: 77836459First, installation environmentThe TensorFlow can support the CPU, or it can support CPU+GPU. The former has a simple environmental requirement and the latter requires additional support. TensorFlow is developed based on vc++2015, so you need to download the installation visualc++ redistributable for Visual Studio 2015来 get MSVCP140.DLL support. If you are installing a GPU version (with n cards

This weekend, I decided it is time:i is going to update my Python environment and get Keras and TensorFlow installed So I could the start doing tutorials (particularly for deep learning) using R. Although I used to is a systems administrator (about years ago), I don ' t do much installing or configuring so I guess T Hat ' s why I ' ve put the this task off for so long. And it wasn ' t unwarranted:it took me the whole weekend to get the install working

Deep Learning Library packages Theano, Lasagne, and TensorFlow support GPU installation in Ubuntu With the popularity of deep learning, more and more people begin to use deep learning to train their own models. GPU training is much faster than the CPU, allowing models that require one week of training to be completed within one day. This post explains how to install Theano, Lasagne, TensorFlow trained with

Preface Recently learning TensorFlow, you need to install its environment. Originally intended to install an Ubuntu system for the computer, it was too troublesome to choose to install it in Windows. Because TensorFlow needs more dependent environment, it is time-consuming and laborious to install, and error prone. Search on the internet found the installation of Anaconda, after the practice found that the

On the internet to find a lot of cases, stepped on a lot of pits, feel more comprehensive is the following introductionHttp://www.cnblogs.com/xuliangxing/p/7575586.htmlLet's talk about my steps:First installed the Anacoda, because it is a scientific computing environment, so first installed the latest version, so the corresponding Python 3.6Install graphics driver: gtx950m installed the latest 390 drivers, worried about incompatibility, so all with the latestInstalling Cuda, this pit was finally

The TensorFlow training model is usually written using the Python API and simply records how the models are invoked in Java after they are saved. In Python, the model is saved using the following API: # Save binary model Output_graph_def = tf.graph_util.convert_variables_to_constants (Sess, Sess.graph_def, Output_node_ names=[' Y_conv_add '] with Tf.gfile.FastGFile ('/LOGS/MNIST.PB ', mode= ' WB ') as F: F.write (output_graph_def. Serializetostri

I. Installation of CUDASpecific installation process See my other blog, ubuntu16.04 installation configuration deep learning environmentSecond, installation TensorFlow1. Specific installation process In fact, the official website is written in more detail, summed up the words can be divided into two types: Install release version and source code compiled installation. Because the source code compiled installation is cumbersome, and need to install Google's own compiler Bazel, so I choose to inst

This is my first blog, in reference to other people's blog to install the process, for my platform system, encountered a lot of problems, here to write my practice and the problems encountered.For the reference to the blogger's article, here to express thanks.For this blog, if there is bad writing or wrong place, because my level is limited, as well as the limitations of the problems encountered, can not be taken into account, please give understanding, and hope to get good suggestions, for good

TensorFlowContinue typing in the Anaconda Prompt window:Conda create-n TensorFlow python=3.5Represents the creation of a tensorflow dependent environment, because TensorFlow currently does not support Python3.6, where we use Python3.5.Continue to watch console output:Fetching package metadata ...... ..... Solving Package Specifications:. Package Plan forInstall

Learning notes TF057: TensorFlow MNIST, convolutional neural network, recurrent neural network, unsupervised learning, tf057tensorflow MNIST convolutional neural network. Https://github.com/nlintz/TensorFlow-Tutorials/blob/master/05_convolutional_net.py.TensorFlow builds a CNN model to train the MNIST dataset. Build a model. Define input data and pre-process data. Read the data MNIST to obtain the training

Mnist Data Set IntroductionMnist is an entry-level computer vision dataset that contains a variety of handwritten digital pictures:The Mnist dataset contains callout information, which represents 5, 0, 4, and 1, respectively.The official website of the Mnist dataset is Yann LeCun ' s websiteAutomatic downloadFirst posted on GitHub address: https://github.com/tensorflow/tensorflow/tree/master/