coursera tensorflow

Original: How to Write Your Own TensorFlow in C + +Author: Ray ZhangNo, I fly Absrtact: TensorFlow is the second generation of AI learning system based on Distbelief, whose name originates from its own operating principle, it is completely open source, and the author expounds How to realize his tensorflow with C + + through a small project of his own. This articl

Recently in the study deeplearning, the theory looked over, ready to start using TensorFlow to do development. Of course, we need to use Python now. Accustomed to automatically fill the full function, or want to be in Python can be automatically filled, read a lot of posts, http://blog.csdn.net/robertsong2004/article/details/48165557, indeed can automatically fill up. But found that after each run of Python, exit () out of the Python environment, the

Current environment: WIN10, anaconda2,python2.7 Objective: To install TensorFlow without affecting the current software environment Currently TensorFlow only supports the Python 3.5 version under Windows, and I only have python2.7 on my system. Installing TensorFlow requires a Python dependency pack, so I chose to install the Anaconda 3 version, which eliminates

ImpressionsToday, I tested the model of my own training, and YOLOv2 done a comparison, the detection is correct, YOLOv2 version of the accuracy is not high, but there are a lot of SSD did not detect, recall rate is not high. Note that the SSD environment is Python3, and running on the python2 will be problematic. TENSORFLOW-GPU, OPENCV installation reference my blog: SSD environment installation 1 Making data setsThe most troublesome is the producti

Sometimes, we need to export the TensorFlow model to a single file (with both model schema definitions and weights) for easy use elsewhere (such as deploying a network in C + +). Using the Tf.train.write_graph () by default, only the definition of the network (without weights) is exported, and the file that is exported by Tf.train.Saver () is separated from the weight, and therefore other methods are required. We know that the Graph_def file does not

Update to TensorFlow 1.4 I. Read input data 1. If the database size can be fully read in memory, use the simplest numpy arrays format: 1). Convert the Npy file into a TF. Tensor2). Using Dataset.from_tensor_slices ()Example: # Load The training data into two numpy arrays, for example using ' np.load () '. With Np.load ("/var/data/training_data.npy") as data: features = data["Features"] labels = data["Labels"] # assume that each row of features corresp

using the specified GPU and GPU memory in TensorFlow This document is set up using the GPU 3 settings used by the GPU 2 Python code settings used in the 1 Terminal execution Program TensorFlow use of the memory size 3.1 quantitative settings memory 3.2 Set video memory on demand Reprint please specify the source: Http://www.cnblogs.com/darkknightzh/p/6591923.html Reference URL: Http://stackoverflow.com/que

Catalogue Graphics driver Installation Cuda Installation CUDNN Installation TENSORFLOW-GPU Installation this time using the host configuration:CPU:i7-8700k graphics :gtx-1080tiFirst, install the video driverOpen a Command Window (ctrl+alt+t)sudo apt-get purge nvidia*sudo add-apt-repository ppa:graphics-drivers/ppasudo apt-sudoinstall nvidia-384 nvidia-settingsif the error Add-apt-repository does not exist, run the following c

Reference website:[1] tensorflow official website Tutorials [2] Geek College 's translation of TensorFlow's official website tutorial[3] How to install TensorFlow under Csdn-ubuntuhttp://blog.csdn.net/zhaoyu106/article/details/52793183 [CSDN]Https://github.com/tensorflow/tensorflow/blob/master/

Always have a habit, see open source code updated, always want to update to the latest version, if ignored, I feel lazy or some irresponsible, this may be a form of obsessive-compulsive disorder it; A few nights ago git pull tensorflow, after finished also did not go after it, these two days to think up or do things manage it, also want to focus on the study into the TensorFlow, do play to feel ^_^. Acco

Installation Environment Win10 Python3.6.4 More than 3.5 version can be, currently tensorflow only support 64-bit python3.5 above version NumPy After installing Python, open the terminal cmd input PIP3 install NumPy Specific ProcessDownload installation Cuda8.0， must be 8.0 version. Download the address and follow the image below to download the local installation package. If the installation is wrong remember to uninstall the previous removal c

Tags: getdir class latest Run 0.11 directory with validation LinuTensorFlow is a deep learning framework with two installed versions to choose from: TensorFlow with the CPU support is only recommended to install this version because it is easy to install and very fast (installs in just 5-10 minutes). TensorFlow with GPU support if you have an NVIDIA GPU, you can install this version. This versi

TensorFlow installed under Windows for study purposes, if you want to do the technology, see the relevant blog: CentOS7 installation TensorFlow1 , installation Pytho3.5First go to the Anaconda website to download the Windows version of the software, here Select the v3.6 version.Https://www.continuum.io2 , after the installation is complete, open Anaconda PromptThen we enter a command to see the installable version of

Reprint Please specify link: http://www.cnblogs.com/SSSR/p/5630534.htmlExamples in Tflearn training VGG16 project: https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py has not been tested successfully.The next project is to use a model that has been trained by others to make predictions, and the test works very well.Github:https://github.com/ry/tensorflow-vgg16 This project has been tested successfully, the effect is very good

1 Learning Goals: Learn the basic TensorFlow concept Using classes in TensorFlow LinearRegressor and predicting the median house value of each city block based on a single input feature Estimating the accuracy of model predictions using RMS error (RMSE) Improve model accuracy by adjusting the model's hyper-parameters Note: Data is based on California State 1990 census data.2 settin

TensorFlow InstallationSystem CentOS 7.0 64-bit:Python version: (note TensorFlow currently only supports python2.7 version)Install PIPYum update-y yum install-y python python-devel epel-release.noarch python-pipInstalling TensorFlow with PIPPip Install HTTPS://STORAGE.GOOGLEAPIS.COM/TENSORFLOW/LINUX/CPU/

TensorFlow is used to train a simple binary classification neural network model. Use TensorFlow to implement the 4.7 pattern classification exercise in neural networks and machine learning The specific problem is to classify the dual-Crescent dataset as shown in. Tools used: Python3.5 tensorflow1.2.1 numpy matplotlib 1. Generate a two-month Dataset Def produceData (r, w, d, num): r1 = r-w/2 r2 = r + w/2 #

Mac Python3 installation TensorFlow error:futures requires Python ' >=2.6, Correspondence Resolution Link: https://github.com/tensorflow/tensorflow/issues/16478Same problem here on Ubuntu 16.04 with Python 3.5.Clearly, Python 3 doesn ' t need futures. And with Python 2.7, using:pip2 install tensorflow-gpu==1.5.0It goes

CX10 TensorFlow Application Practice of the most fire frame based on Python play to AIThe beginning of the new year, learning to be early, drip records, learning is progress!Do not look everywhere, seize the promotion of their own.For learning difficulties do not know how to improve themselves can be added: 1225462853 get information.CX103 TensorFlow Application Practice of the most fire frame based on Pyth

Just bought the new machine and installed the latest Ubuntu system. The GPU support is not selected according to the process installed on the official website. Download the source code. Git is required, and if you don't have Git installed you need to install it first. git clone --recurse-submodules https://github.com/tensorflow/tensorflow Installing BazelInstall dependencies First