tensorflow classification

C # writing TensorFlow AI applicationsTensorflowsharp get started using C # to write TensorFlow AI application learning.TensorFlow Brief Introduction TensorFlow is Google's second-generation machine learning system, according to Google, in some benchmarks, tensorflow performance is twice times faster than the first

Installation Environment: Windows 64bit Gpu:geforce GT 720 python:3.5.3 Cuda:8 First download the Anaconda3 version of Win10 64bit and install the Python3.5 release. Because currently TensorFlow only supports Python3.5 for Windows. You can download the Anaconda installation package directly, there is no problem. (Tsinghua Mirror https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/) There are two versions of TensorFlo

TensorFlow version 1.4 is now publicly available-this is a big update. We are very pleased to announce some exciting new features here and hope you enjoy it. Keras In version 1.4, Keras has migrated from Tf.contrib.keras to the core package Tf.keras. Keras is a very popular machine learning framework that contains a number of advanced APIs that can minimize the time between your creativity and your achievable implementation. Keras can be integrated

TensorFlow: A graph is used to indicate that a calculation task is performed in the context of a conversation called a session using tensor to represent data through variables (Variable) to maintain state using FE Ed and fetch can assign or fetch data from any operation (arbitrary operation) TensorFlow is a programming system that uses diagrams to represent computational tasks. The node in the diagram is ca

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

My device: Ubuntu14.04+gpu TensorFlow1.0.1 Related papers "Show and Tell:lessons learned from the Mscoco Image captioning Challenge" https://arxiv.org/abs/1609.06647 Last September, just open source Github:https://github.com/tensorflow/models/tree/master/im2txt#generating-captions According to GitHub's Readme Install related items First Bazel according to the official website $echo "Deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk

this uses TensorFlow to implement a simple convolution neural network using mnist datasets. The network structure is: Data input layer – convolution layer----------------------------------------------------------- Import TensorFlow as TF import numpy as NP import input_data mnist = input_data.read_data_sets (' data/', one_hot=true) pri NT ("Mnist ready") Sess = tf. InteractiveSession () # defines the init

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

TensorFlow Installation and Example-(Ubuntu16.04.1 Anaconda3) Python-pip and Python-dev PIP is the default package manager for Python, install TensorFlow directly with PIP, install both packagesCommand: Apt-get install PYTHON-PIP Python-dev python-virtualenvYou can virtualenv create an isolated container to install TensorFlow. This is optional, whi

vector of 1*784. The final result is to turn 55000 images into a tensor tensor [55000,784]. Each image corresponds to a one-hotx vector 1*10, so label becomes tensor[55000,10] Next is Softmax regressions Introduction: In fact, the solution is 10 classification of the problem, mainly divided into two steps: The first is to derive the input belongs to a class of evidence, and then, the evidence into probability. Here the evidence is actually similar to

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

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 "

connected layer to accommodate the new classification task. Import glob Import os.path import random import NumPy as NP import TensorFlow as TF from tensorflow.python.platform Import Gfile #模型和样本路径的设置 #inception-v3 bottleneck node Number bottleneck_tensor_size = 2048 #瓶颈层tenbsor name bottleneck_tensor_name = ' Pool_3/_ reshape:0 ' #图像输入tensor name jpeg_data_tensor_name = ' decodejpeg/contents:0 ' # v3 path